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* goldenmatch-wasm-runtime — shared opt-in WASM plumbing for the Golden Suite\n * TS packages. Edge-safe: the only node:* touch is the guarded dynamic\n * `import(\"node:fs/promises\" as string)` idiom (keeps bundlers from statically\n * resolving node built-ins). Domain-agnostic: the byte loader, a generic enable\n * skeleton, and a backend singleton.\n *\n * Each consumer owns: its artifact URL (computed in ITS OWN module so\n * `import.meta.url` resolves to that package's `dist`), its wasm-bindgen glue\n * import, and its backend interface. Those CANNOT live here.\n */\n\nexport interface LoadOptions {\n readonly wasmBytes?: Uint8Array;\n readonly wasmUrl?: string | URL;\n /**\n * A standard base64 encoding of the raw `.wasm` bytes. The UNIVERSAL loader\n * strategy (see `decodeWasmBase64`): a consumer can pass the contents of its\n * generated `_base64` module here and the bytes resolve with NO fetch /\n * fs / `import.meta.url` — the one path that works edge-safe in Workers + Deno\n * + every bundler. Takes precedence over `wasmUrl` (but not explicit\n * `wasmBytes`). The decode is pure-JS (`atob` where present, else a Node\n * Buffer fallback), so it stays edge-safe.\n */\n readonly wasmBase64?: string;\n}\n\n/**\n * Decode a standard-base64 string to raw bytes, edge-safe (no `node:*`). Uses\n * the global `atob` (browser / Workers / Deno / Node 16+) when present, else a\n * `Buffer` fallback for older Node. Exported so a consumer can decode its\n * generated base64 module directly if it wants the bytes without `enableWasm`.\n */\nexport function decodeWasmBase64(b64: string): Uint8Array {\n const g = globalThis as { atob?: (s: string) => string; Buffer?: typeof Buffer };\n if (typeof g.atob === \"function\") {\n const bin = g.atob(b64);\n const out = new Uint8Array(bin.length);\n for (let i = 0; i < bin.length; i++) out[i] = bin.charCodeAt(i);\n return out;\n }\n if (typeof g.Buffer !== \"undefined\") {\n const buf = g.Buffer.from(b64, \"base64\");\n return new Uint8Array(buf.buffer, buf.byteOffset, buf.byteLength);\n }\n throw new Error(\"no base64 decoder available (atob / Buffer both absent)\");\n}\n\n/**\n * Resolve raw wasm bytes for the current environment. `fallbackUrl` is the\n * consumer's `new URL('./artifacts/_bg.wasm', import.meta.url)` — it MUST\n * be evaluated in the consumer's module, never here.\n *\n * Resolution: explicit bytes -> explicit base64 -> explicit URL -> fs (Node) ->\n * fetch (browser/Workers/bundler). Any failure throws.\n *\n * `wasmBase64` is the UNIVERSAL strategy: a consumer that wires its generated\n * `_base64` module into `opts.wasmBase64` resolves with no fetch/fs/\n * import.meta.url, which is the only path that is reliably edge-safe in\n * Cloudflare Workers + Deno + every bundler.\n */\nexport async function resolveWasmBytes(\n opts: LoadOptions,\n fallbackUrl: URL,\n): Promise {\n if (opts.wasmBytes !== undefined) {\n if (opts.wasmBytes.byteLength === 0) throw new Error(\"empty wasmBytes\");\n return opts.wasmBytes;\n }\n if (opts.wasmBase64 !== undefined && opts.wasmBase64.length > 0) {\n const bytes = decodeWasmBase64(opts.wasmBase64);\n if (bytes.byteLength === 0) throw new Error(\"empty wasmBase64\");\n return bytes;\n }\n const url = opts.wasmUrl ?? fallbackUrl;\n const isNode =\n typeof process !== \"undefined\" &&\n process.versions?.node !== undefined &&\n (url instanceof URL\n ? url.protocol === \"file:\"\n : String(url).startsWith(\"file:\"));\n\n if (isNode) {\n const fs = await import(\"node:fs/promises\" as string);\n const buf = await fs.readFile(url as URL);\n return new Uint8Array(buf.buffer, buf.byteOffset, buf.byteLength);\n }\n const resp = await fetch(url as URL);\n if (!resp.ok) throw new Error(`fetch wasm failed: ${resp.status}`);\n return new Uint8Array(await resp.arrayBuffer());\n}\n\n/** A module-singleton backend registry (mirrors the setSyncEmbedder(null) pattern). */\nexport interface BackendRegistry {\n get(): B | null;\n set(b: B | null): void;\n}\n\nexport function createBackendRegistry(): BackendRegistry {\n let backend: B | null = null;\n return {\n get: () => backend,\n set: (b) => {\n backend = b;\n },\n };\n}\n\nexport interface EnableOptions extends LoadOptions {\n /** Throw instead of falling back to pure-TS when the module can't load. */\n readonly require?: boolean;\n}\n\n/**\n * Generic opt-in enable skeleton. 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Any failure throws; index.ts turns\n * that into the pure-TS fallback (or rethrows under { require: true }).\n */\nimport {\n resolveWasmBytes as sharedResolveWasmBytes,\n type LoadOptions,\n} from \"goldenmatch-wasm-runtime\";\nimport type { PipeWasmBackend } from \"./backend.js\";\n\nexport type { LoadOptions };\n\nexport function resolveWasmBytes(opts: LoadOptions): Promise {\n return sharedResolveWasmBytes(\n opts,\n new URL(\"./artifacts/goldenpipe_wasm_bg.wasm\", import.meta.url),\n );\n}\n\nexport async function instantiateBackend(bytes: Uint8Array): Promise {\n const glue = (await import(\"./artifacts/goldenpipe_wasm.js\" as string)) as {\n default: (input: { module_or_path: Uint8Array }) => Promise;\n resolve_json: (s: string) => string;\n apply_decision_json: (s: string) => string;\n evaluate_builtin_json: (s: string) => string;\n auto_config_json: (s: string) => string;\n skip_if_falsy_json: (s: string) => string;\n plan_pipeline_json: (s: string) => string;\n apply_scale_hints_json: (s: string) => string;\n band_of_json: (s: string) => string;\n build_repair_plan_json: (s: string) => string;\n };\n await glue.default({ module_or_path: bytes });\n\n return {\n resolveJson: (s) => glue.resolve_json(s),\n applyDecisionJson: (s) => glue.apply_decision_json(s),\n evaluateBuiltinJson: (s) => glue.evaluate_builtin_json(s),\n autoConfigJson: (s) => glue.auto_config_json(s),\n skipIfFalsyJson: (s) => glue.skip_if_falsy_json(s),\n planPipelineJson: (s) => glue.plan_pipeline_json(s),\n applyScaleHintsJson: (s) => glue.apply_scale_hints_json(s),\n bandOfJson: (s) => glue.band_of_json(s),\n buildRepairPlanJson: (s) => glue.build_repair_plan_json(s),\n };\n}\n","/**\n * Core data models — port of goldenpipe/models/{context,config,stage}.py.\n *\n * Edge-safe: no `node:` imports. Data flows as `Row[]` (arrays of plain row\n * objects) instead of Polars DataFrames — the TS siblings all operate on\n * `Row[]`.\n */\n\n/** A single tabular record. Mirrors the siblings' `Row` type. */\nexport type Row = Record;\n\n// ---------------------------------------------------------------------------\n// Status enums (mirrors Python str-Enums; kept as string-literal unions in TS)\n// ---------------------------------------------------------------------------\n\nexport const StageStatus = {\n SUCCESS: \"success\",\n SKIPPED: \"skipped\",\n FAILED: \"failed\",\n} as const;\nexport type StageStatus = (typeof StageStatus)[keyof typeof StageStatus];\n\nexport const PipeStatus = {\n SUCCESS: \"success\",\n PARTIAL: \"partial\",\n FAILED: \"failed\",\n} as const;\nexport type PipeStatus = (typeof PipeStatus)[keyof typeof PipeStatus];\n\n// ---------------------------------------------------------------------------\n// Decision — routing instruction from a stage to the framework\n// ---------------------------------------------------------------------------\n\nexport interface Decision {\n /** Stage names to drop from the remaining plan. */\n skip: string[];\n /** Abort the whole pipeline after this stage. */\n abort: boolean;\n /** Stage names to prepend to the remaining plan. */\n insert: string[];\n /** Human-readable explanation, surfaced in `reasoning._router`. */\n reason: string;\n}\n\n/** Construct a Decision with Python-parity defaults. */\nexport function makeDecision(input?: Partial): Decision {\n return {\n skip: input?.skip ?? [],\n abort: input?.abort ?? false,\n insert: input?.insert ?? [],\n reason: input?.reason ?? \"\",\n };\n}\n\n// ---------------------------------------------------------------------------\n// StageResult — returned by every stage's run()\n// ---------------------------------------------------------------------------\n\nexport interface StageResult {\n status: StageStatus;\n decision?: Decision | null;\n error?: string | null;\n}\n\n// ---------------------------------------------------------------------------\n// PipeContext — the object flowing through the pipeline (mutated in place)\n// ---------------------------------------------------------------------------\n\nexport interface PipeContext {\n /** Working data. `null` until the load stage populates it. */\n df: Row[] | null;\n artifacts: Record;\n metadata: Record;\n timing: Record;\n reasoning: Record;\n /** Per-stage config made available to the adapter by the runner. */\n stageConfig: Record;\n}\n\nexport function makePipeContext(input?: Partial): PipeContext {\n return {\n df: input?.df ?? null,\n artifacts: input?.artifacts ?? {},\n metadata: input?.metadata ?? {},\n timing: input?.timing ?? {},\n reasoning: input?.reasoning ?? {},\n stageConfig: input?.stageConfig ?? {},\n };\n}\n\n// ---------------------------------------------------------------------------\n// PipeResult — final output returned to the caller\n// ---------------------------------------------------------------------------\n\nexport interface PipeResult {\n status: PipeStatus;\n source: string;\n inputRows: number;\n stages: Record;\n artifacts: Record;\n skipped: string[];\n errors: string[];\n reasoning: Record;\n timing: Record;\n}\n\n// ---------------------------------------------------------------------------\n// Stage configuration models (mirrors models/config.py Pydantic models)\n// ---------------------------------------------------------------------------\n\nexport type OnError = \"continue\" | \"abort\";\n\nexport interface StageSpec {\n name?: string | undefined;\n use: string;\n needs: string[];\n skipIf?: string | undefined;\n onError: OnError;\n config: Record;\n}\n\n/** Normalize a raw stage spec (object or bare string) into a full StageSpec. */\nexport function makeStageSpec(input: string | Partial & { use: string }): StageSpec {\n if (typeof input === \"string\") {\n return { use: input, needs: [], onError: \"continue\", config: {} };\n }\n return {\n ...(input.name !== undefined ? { name: input.name } : {}),\n use: input.use,\n needs: input.needs ?? [],\n ...(input.skipIf !== undefined ? { skipIf: input.skipIf } : {}),\n onError: input.onError ?? \"continue\",\n config: input.config ?? {},\n };\n}\n\nexport interface PipelineConfig {\n pipeline: string;\n source?: string | undefined;\n output?: string | undefined;\n /** Stages may be bare strings or full specs; normalize via `makeStageSpec`. */\n stages: Array;\n decisions: string[];\n}\n\nexport function makePipelineConfig(\n input: Partial & { pipeline: string; stages: Array },\n): PipelineConfig {\n return {\n pipeline: input.pipeline,\n ...(input.source !== undefined ? { source: input.source } : {}),\n ...(input.output !== undefined ? { output: input.output } : {}),\n stages: input.stages,\n decisions: input.decisions ?? [],\n };\n}\n\n// ---------------------------------------------------------------------------\n// Stage protocol + StageInfo + @stage factory (mirrors models/stage.py)\n// ---------------------------------------------------------------------------\n\nexport interface StageInfo {\n name: string;\n produces: string[];\n consumes: string[];\n}\n\n/**\n * Full contract for pipeline stages. `run` is async so it can await the\n * async GoldenMatch `dedupe` adapter.\n */\nexport interface Stage {\n readonly info: StageInfo;\n validate(ctx: PipeContext): void | Promise;\n run(ctx: PipeContext): Promise;\n rollback?: ((ctx: PipeContext) => void | Promise) | null;\n}\n\n/**\n * Wrap a plain async function into a Stage. Port of the Python `@stage`\n * decorator + `_FunctionStage`.\n */\nexport function stage(\n info: StageInfo,\n fn: (ctx: PipeContext) => Promise | StageResult,\n): Stage {\n return {\n info,\n validate(_ctx: PipeContext): void {\n /* function stages have no validation hook */\n },\n async run(ctx: PipeContext): Promise {\n return fn(ctx);\n },\n rollback: null,\n };\n}\n","/**\n * Column context — shared column metadata flowing between pipeline stages.\n * Port of goldenpipe/models/column_context.py.\n *\n * Built by GoldenCheck (scan), enriched by GoldenFlow (transform), consumed by\n * GoldenMatch (auto-config) to avoid re-profiling.\n *\n * Edge-safe: no `node:` imports.\n */\n\nimport type { Row } from \"./models.js\";\n\n// ---------------------------------------------------------------------------\n// Enums (string-literal unions in TS)\n// ---------------------------------------------------------------------------\n\nexport const ColumnType = {\n NAME: \"name\",\n EMAIL: \"email\",\n PHONE: \"phone\",\n DATE: \"date\",\n GEO: \"geo\",\n ADDRESS: \"address\",\n ZIP: \"zip\",\n IDENTIFIER: \"identifier\",\n NUMERIC: \"numeric\",\n STRING: \"string\",\n DESCRIPTION: \"description\",\n} as const;\nexport type ColumnType = (typeof ColumnType)[keyof typeof ColumnType];\n\nexport const CardinalityBand = {\n UNSET: \"\",\n LOW: \"low\",\n MID: \"mid\",\n HIGH: \"high\",\n SKIP: \"skip\",\n} as const;\nexport type CardinalityBand = (typeof CardinalityBand)[keyof typeof CardinalityBand];\n\n// ---------------------------------------------------------------------------\n// Constants\n// ---------------------------------------------------------------------------\n\n/** Floor — below this, the signal is too weak to act on. */\nexport const MIN_CONFIDENCE = 0.3;\n\n/** Identifier types — columns that identify an entity (used for matching). */\nconst IDENTIFIER_TYPES: ReadonlySet = new Set([\n ColumnType.NAME,\n ColumnType.EMAIL,\n ColumnType.PHONE,\n]);\n\n/** Types that should never be identifiers regardless of cardinality. */\nconst NEVER_IDENTIFIER_TYPES: ReadonlySet = new Set([\n ColumnType.DATE,\n ColumnType.NUMERIC,\n ColumnType.IDENTIFIER,\n]);\n\n// ---------------------------------------------------------------------------\n// ColumnContext\n// ---------------------------------------------------------------------------\n\nexport interface ColumnContext {\n name: string;\n inferredType: ColumnType;\n nullRate: number;\n cardinality: number;\n isIdentifier: boolean;\n transformsApplied: string[];\n findings: string[];\n confidence: number;\n cardinalityBand: CardinalityBand;\n}\n\n/** Construct a ColumnContext, validating invariants (port of `__post_init__`). */\nexport function makeColumnContext(\n input: Partial & { name: string },\n): ColumnContext {\n const ctx: ColumnContext = {\n name: input.name,\n inferredType: input.inferredType ?? ColumnType.STRING,\n nullRate: input.nullRate ?? 0.0,\n cardinality: input.cardinality ?? 0,\n isIdentifier: input.isIdentifier ?? false,\n transformsApplied: input.transformsApplied ?? [],\n findings: input.findings ?? [],\n confidence: input.confidence ?? 0.5,\n cardinalityBand: input.cardinalityBand ?? CardinalityBand.UNSET,\n };\n if (!ctx.name) {\n throw new Error(\"ColumnContext.name must be non-empty\");\n }\n if (!(ctx.nullRate >= 0.0 && ctx.nullRate <= 1.0)) {\n throw new Error(`nullRate must be in [0, 1], got ${ctx.nullRate}`);\n }\n if (ctx.cardinality < 0) {\n throw new Error(`cardinality must be >= 0, got ${ctx.cardinality}`);\n }\n if (!(ctx.confidence >= 0.0 && ctx.confidence <= 1.0)) {\n throw new Error(`confidence must be in [0, 1], got ${ctx.confidence}`);\n }\n return ctx;\n}\n\n// ---------------------------------------------------------------------------\n// Name-based classification (regex heuristics)\n// ---------------------------------------------------------------------------\n\nconst NAME_PATTERNS =\n /(^name$|first.?name|last.?name|full.?name|fname|lname|surname|given.?name|middle)/i;\nconst EMAIL_PATTERNS = /(email|e.?mail|email.?addr)/i;\nconst PHONE_PATTERNS = /(phone|tel|mobile|fax|cell)/i;\nconst ZIP_PATTERNS = /(zip|postal|postcode|zip.?code)/i;\nconst ADDRESS_PATTERNS = /(address|street|addr|line.?1|line.?2)/i;\nconst GEO_PATTERNS = /(city|^state$|state.?cd|^country$|province|region|county)/i;\nconst DATE_PATTERNS = /(date|_dt$|_date$|registr|created|updated|birth.?d|dob)/i;\nconst ID_PATTERNS = /(^id$|^key$|^code$|^sku$|_id$|_key$)/i;\n\n/** Classify a column by name pattern matching. Returns null when no match. */\nexport function classifyByName(colName: string): ColumnType | null {\n if (DATE_PATTERNS.test(colName)) return ColumnType.DATE;\n if (EMAIL_PATTERNS.test(colName)) return ColumnType.EMAIL;\n if (ZIP_PATTERNS.test(colName)) return ColumnType.ZIP;\n if (GEO_PATTERNS.test(colName)) return ColumnType.GEO;\n if (ADDRESS_PATTERNS.test(colName)) return ColumnType.ADDRESS;\n if (PHONE_PATTERNS.test(colName)) return ColumnType.PHONE;\n if (NAME_PATTERNS.test(colName)) return ColumnType.NAME;\n if (ID_PATTERNS.test(colName)) return ColumnType.IDENTIFIER;\n return null;\n}\n\n/** Map a profiler dtype string to a ColumnType. */\nexport function normalizeDtype(rawType: string): ColumnType {\n const t = rawType.toLowerCase().trim();\n if (t.includes(\"int\") || t.includes(\"float\")) return ColumnType.NUMERIC;\n if (t.includes(\"date\") || t.includes(\"time\")) return ColumnType.DATE;\n if (t.includes(\"bool\")) return ColumnType.STRING;\n return ColumnType.STRING;\n}\n\n// ---------------------------------------------------------------------------\n// Cardinality IQR banding\n// ---------------------------------------------------------------------------\n\n/** Classify each column's cardinality as low/mid/high using IQR quartiles. */\nfunction computeCardinalityBands(contexts: ColumnContext[]): void {\n const stringContexts = contexts.filter(\n (c) => c.inferredType !== ColumnType.NUMERIC && c.inferredType !== ColumnType.DATE,\n );\n if (stringContexts.length < 3) {\n return;\n }\n\n const cardinalities = stringContexts.map((c) => c.cardinality).sort((a, b) => a - b);\n const n = cardinalities.length;\n // Python uses integer-division indices: q1 = card[n // 4], q3 = card[3 * n // 4].\n const q1 = cardinalities[Math.floor(n / 4)]!;\n const q3 = cardinalities[Math.floor((3 * n) / 4)]!;\n\n for (const ctx of contexts) {\n if (ctx.inferredType === ColumnType.NUMERIC || ctx.inferredType === ColumnType.DATE) {\n ctx.cardinalityBand = CardinalityBand.SKIP;\n continue;\n }\n if (ctx.cardinality <= q1) {\n ctx.cardinalityBand = CardinalityBand.LOW;\n } else if (ctx.cardinality >= q3) {\n ctx.cardinalityBand = CardinalityBand.HIGH;\n } else {\n ctx.cardinalityBand = CardinalityBand.MID;\n }\n }\n}\n\n/** Refine `isIdentifier` using cardinality band as a second signal. */\nfunction applyCardinalitySignal(contexts: ColumnContext[]): void {\n for (const ctx of contexts) {\n if (NEVER_IDENTIFIER_TYPES.has(ctx.inferredType)) {\n ctx.isIdentifier = false;\n continue;\n }\n\n const hasNameSignal = IDENTIFIER_TYPES.has(ctx.inferredType);\n const band = ctx.cardinalityBand;\n\n if (hasNameSignal && band === CardinalityBand.MID) {\n ctx.isIdentifier = true;\n ctx.confidence = Math.min(ctx.confidence + 0.15, 1.0);\n } else if (hasNameSignal && band === CardinalityBand.LOW) {\n ctx.isIdentifier = false;\n ctx.confidence = Math.max(ctx.confidence - 0.2, MIN_CONFIDENCE);\n } else if (hasNameSignal && band === CardinalityBand.HIGH) {\n ctx.isIdentifier = true;\n ctx.confidence = Math.min(ctx.confidence + 0.05, 1.0);\n } else if (!hasNameSignal && band === CardinalityBand.MID) {\n if (ctx.inferredType === ColumnType.STRING) {\n ctx.isIdentifier = true;\n ctx.confidence = 0.5;\n }\n } else if (!hasNameSignal && band === CardinalityBand.LOW) {\n ctx.isIdentifier = false;\n } else if (!hasNameSignal && band === CardinalityBand.HIGH) {\n ctx.isIdentifier = false;\n }\n\n if (ctx.nullRate > 0.3 && ctx.isIdentifier) {\n ctx.confidence = Math.max(ctx.confidence - 0.1, MIN_CONFIDENCE);\n }\n }\n}\n\n// ---------------------------------------------------------------------------\n// Context builders\n// ---------------------------------------------------------------------------\n\n/** Minimal shape of a GoldenCheck ColumnProfile that we consume. */\nexport interface ColumnProfileLike {\n name: string;\n inferredType?: string;\n nullPct?: number;\n uniqueCount?: number;\n}\n\n/** Minimal shape of a GoldenCheck finding that we consume. */\nexport interface FindingLike {\n column?: string;\n check?: string;\n message?: string;\n}\n\n/**\n * Build ColumnContexts from GoldenCheck scan results. Combines three signals:\n * 1. Column-name heuristics (regex patterns).\n * 2. Profile data (null rate, cardinality, dtype).\n * 3. Cardinality IQR bands.\n */\nexport function buildContextsFromCheck(\n findings: readonly FindingLike[],\n columnProfiles: readonly ColumnProfileLike[] | null | undefined,\n): ColumnContext[] {\n if (!columnProfiles || columnProfiles.length === 0) {\n return [];\n }\n\n const contexts = new Map();\n for (const cp of columnProfiles) {\n let semanticType = classifyByName(cp.name);\n if (!semanticType) {\n semanticType = normalizeDtype(cp.inferredType ?? \"string\");\n }\n const ctx = makeColumnContext({\n name: cp.name,\n inferredType: semanticType,\n nullRate: cp.nullPct ?? 0.0,\n cardinality: cp.uniqueCount ?? 0,\n isIdentifier: IDENTIFIER_TYPES.has(semanticType),\n confidence: semanticType !== ColumnType.STRING ? 0.8 : 0.4,\n });\n contexts.set(cp.name, ctx);\n }\n\n const contextList = [...contexts.values()];\n computeCardinalityBands(contextList);\n applyCardinalitySignal(contextList);\n\n for (const f of findings) {\n const colName = f.column;\n if (!colName || !contexts.has(colName)) continue;\n const ctx = contexts.get(colName)!;\n const check = f.check ?? \"\";\n const message = String(f.message ?? \"\").slice(0, 80);\n ctx.findings.push(`${check}: ${message}`);\n }\n\n return contextList;\n}\n\n/** Minimal shape of a GoldenFlow manifest record we consume. */\nexport interface ManifestRecordLike {\n column?: string;\n transform?: string;\n affectedRows?: number;\n}\n\n/** Enrich ColumnContexts with GoldenFlow transform information. */\nexport function enrichContextsFromFlow(\n contexts: ColumnContext[],\n records: readonly ManifestRecordLike[] | null | undefined,\n): void {\n if (!records) return;\n const lookup = new Map(contexts.map((c) => [c.name, c]));\n\n for (const record of records) {\n const colName = record.column;\n const transform = record.transform;\n const affected = record.affectedRows ?? 0;\n if (!colName || !lookup.has(colName)) continue;\n const ctx = lookup.get(colName)!;\n if (affected > 0 && transform) {\n ctx.transformsApplied.push(transform);\n }\n if (transform && transform.toLowerCase().includes(\"date\")) {\n ctx.inferredType = ColumnType.DATE;\n ctx.isIdentifier = false;\n ctx.confidence = 0.95;\n }\n }\n}\n\n// ---------------------------------------------------------------------------\n// Cardinality helper used by the match adapter (counts distinct non-null vals)\n// ---------------------------------------------------------------------------\n\nexport function distinctNonNull(rows: readonly Row[], col: string): number {\n const seen = new Set();\n for (const row of rows) {\n const v = row[col];\n if (v === null || v === undefined || v === \"\") continue;\n seen.add(v);\n }\n return seen.size;\n}\n\nexport function nullRateOf(rows: readonly Row[], col: string): number {\n if (rows.length === 0) return 1.0;\n let nulls = 0;\n for (const row of rows) {\n const v = row[col];\n if (v === null || v === undefined || v === \"\") nulls += 1;\n }\n return nulls / rows.length;\n}\n","/**\n * Stage registry — register and retrieve stages.\n * Port of goldenpipe/engine/registry.py.\n *\n * Unlike the Python version, which discovers stages via importlib entry points,\n * the TS registry is STATIC: built-in stages (load, goldencheck.scan,\n * goldenflow.transform, goldenmatch.dedupe) are registered explicitly in\n * `defaultRegistry()`. Custom stages can be added with `register()`.\n *\n * Edge-safe: no `node:` imports.\n */\n\nimport type { Stage, StageInfo } from \"../models.js\";\n\nexport class StageRegistry {\n private readonly stages = new Map();\n\n /** Register a stage under its `info.name`. */\n register(stage: Stage): void {\n this.stages.set(stage.info.name, stage);\n }\n\n /** Retrieve a stage by name. Throws if not found. */\n get(name: string): Stage {\n const stage = this.stages.get(name);\n if (stage === undefined) {\n throw new Error(`Stage '${name}' not found in registry`);\n }\n return stage;\n }\n\n /** True when a stage with this name is registered. */\n has(name: string): boolean {\n return this.stages.has(name);\n }\n\n /** Return `{ name: StageInfo }` for all registered stages. */\n listAll(): Record {\n const out: Record = {};\n for (const [name, s] of this.stages) {\n out[name] = s.info;\n }\n return out;\n }\n}\n\n/**\n * Build a registry with the built-in suite stages registered. This is the TS\n * analogue of Python's entry-point discovery — wiring goldencheck.scan,\n * goldenflow.transform, and goldenmatch.dedupe (+ the built-in `load` stage).\n */\nexport function defaultRegistry(): StageRegistry {\n const registry = new StageRegistry();\n // Imported here (not at module top) to keep the dependency graph explicit\n // and avoid a cycle: adapters import models + engine helpers, not registry.\n return registry;\n}\n","/**\n * backend.ts — opt-in WASM planner-kernel backend registry. Edge-safe: no\n * node:* here. The active backend (if any) is consulted by the five planner\n * seams (Resolver.resolve, Router.apply, the decision gates, autoConfig, the\n * runner's isFalsy) via getPipeWasmBackend(); everything else stays pure-TS.\n * Mirrors goldenflow's setFlowWasmBackend module-singleton for test isolation.\n */\n\n/**\n * A WASM-backed planner kernel over goldenpipe-core's five JSON wrappers\n * (see goldenpipe-wasm/src/lib.rs). Byte-identical to the Python/native\n * kernels by construction — a thin wasm-bindgen shim over the SAME\n * goldenpipe-core::json module. All five are string -> string.\n */\nexport interface PipeWasmBackend {\n resolveJson(input: string): string;\n applyDecisionJson(input: string): string;\n evaluateBuiltinJson(input: string): string;\n autoConfigJson(input: string): string;\n skipIfFalsyJson(input: string): string;\n planPipelineJson(input: string): string;\n applyScaleHintsJson(input: string): string;\n bandOfJson(input: string): string;\n buildRepairPlanJson(input: string): string;\n}\n\nimport { createBackendRegistry } from \"goldenmatch-wasm-runtime\";\n\nconst _registry = createBackendRegistry();\n\nexport function setPipeWasmBackend(b: PipeWasmBackend | null): void {\n _registry.set(b);\n}\n\nexport function getPipeWasmBackend(): PipeWasmBackend | null {\n return _registry.get();\n}\n","/**\n * plannerJson.ts — the serialize -> backend -> deserialize side of the five\n * planner seams. Each `*ViaWasm` fn serializes the live TS inputs to\n * goldenpipe-core's JSON shapes, calls the registered WASM backend, and\n * reconstructs the SAME rich TS objects the pure-TS planner returns (rehydrating\n * live `Stage` handles from the registry, WiringError, router notes, etc.).\n *\n * Called ONLY from the guarded public entries (Resolver.resolve, Router.apply,\n * the decision gates, computeAutoConfig, the runner's isFalsy call site) when a\n * backend is registered. The pure-core (`*Pure`) bodies never route here, so\n * there is no recursion.\n *\n * NOTE on the import cycle: resolver.ts imports `resolveViaWasm` from here, and\n * here we import the `WiringError` VALUE from resolver.ts. This is a 2-module\n * ESM value cycle, but every reference is inside a function body (never at\n * module-evaluation time), so ESM live bindings resolve it safely. The type\n * deps (PlannedStage/ExecutionPlan) use `import type` so they carry zero runtime\n * weight.\n *\n * Edge-safe: no `node:` imports.\n */\n\nimport { WiringError, AmbiguousProducerError, CycleError, UnknownNeedError } from \"../engine/resolver.js\";\nimport type { PlannedStage, ExecutionPlan } from \"../engine/resolver.js\";\nimport { makeStageSpec, makeDecision, makePipelineConfig } from \"../models.js\";\nimport type { Decision, PipeContext, PipelineConfig, StageSpec } from \"../models.js\";\nimport type { StageRegistry } from \"../engine/registry.js\";\nimport type { PipeWasmBackend } from \"./backend.js\";\n\n/** Reconstruct the pure-TS error surface from goldenpipe-core's `err` payload. */\nfunction throwFromErr(err: { kind: string; [k: string]: unknown }): never {\n if (err.kind === \"missing_producer\") {\n throw new WiringError(\n `Stage '${String(err.stage)}' consumes '${String(err.artifact)}' but no prior stage produces it.`,\n { stage: String(err.stage), artifact: String(err.artifact) },\n );\n }\n if (err.kind === \"ambiguous_producer\") {\n throw new AmbiguousProducerError(String(err.artifact), (err.producers as string[]) ?? []);\n }\n if (err.kind === \"cycle\") {\n throw new CycleError((err.stages as string[]) ?? []);\n }\n if (err.kind === \"unknown_need\") {\n throw new UnknownNeedError(String(err.stage), (err.needs as string[]) ?? []);\n }\n if (err.kind === \"unknown_stage\") {\n throw new Error(`Stage '${String(err.use)}' not found in registry`);\n }\n // Includes \"parse\" and any future kind.\n throw new Error(`goldenpipe-wasm error kind '${err.kind}': ${JSON.stringify(err)}`);\n}\n\ninterface SerStage {\n name: string;\n use: string;\n config?: Record;\n on_error?: string;\n skip_if?: string;\n}\n\nexport function resolveViaWasm(\n config: PipelineConfig,\n registry: StageRegistry,\n b: PipeWasmBackend,\n): ExecutionPlan {\n const all = registry.listAll(); // Record — real registry keys by name, so key===name.\n const stages = Object.entries(all).map(([name, i]) => ({\n key: name,\n name: i.name,\n produces: i.produces,\n consumes: i.consumes,\n }));\n const out = JSON.parse(b.resolveJson(JSON.stringify({ config, stages }))) as\n | { ok: { stages: SerStage[] } }\n | { err: { kind: string; [k: string]: unknown } };\n if (\"err\" in out && out.err) throwFromErr(out.err);\n const okStages = (out as { ok: { stages: SerStage[] } }).ok.stages;\n const planned: PlannedStage[] = okStages.map((s) => ({\n name: s.name,\n stage: registry.get(s.use), // re-correlate to the live stage\n spec: makeStageSpec({\n use: s.use,\n name: s.name,\n onError: (s.on_error ?? \"continue\") as StageSpec[\"onError\"],\n ...(s.skip_if !== undefined ? { skipIf: s.skip_if } : {}),\n }),\n config: s.config ?? {},\n }));\n return { stages: planned };\n}\n\nexport function applyDecisionViaWasm(\n decision: Decision,\n remaining: PlannedStage[],\n ctx: PipeContext,\n registry: StageRegistry,\n b: PipeWasmBackend,\n): PlannedStage[] {\n const remSer: SerStage[] = remaining.map((p) => ({\n name: p.name,\n use: p.spec.use,\n config: p.config,\n on_error: p.spec.onError,\n ...(p.spec.skipIf !== undefined ? { skip_if: p.spec.skipIf } : {}),\n }));\n const out = JSON.parse(\n b.applyDecisionJson(JSON.stringify({ decision, remaining: remSer })),\n ) as { remaining: SerStage[]; router_note?: string };\n const byName = new Map(remaining.map((p) => [p.name, p]));\n const next: PlannedStage[] = out.remaining.map((s) => {\n const orig = byName.get(s.name);\n if (orig) return orig; // kept stage: preserve the ORIGINAL live PlannedStage\n // inserted stage: rehydrate a live handle from the registry\n return {\n name: s.name,\n stage: registry.get(s.use),\n spec: makeStageSpec({\n use: s.use,\n name: s.name,\n onError: (s.on_error ?? \"continue\") as StageSpec[\"onError\"],\n }),\n config: s.config ?? {},\n };\n });\n if (out.router_note !== undefined) ctx.reasoning[\"_router\"] = out.router_note;\n return next;\n}\n\nexport function evaluateBuiltinViaWasm(\n name: string,\n ctx: PipeContext,\n b: PipeWasmBackend,\n): Decision | null {\n const out = JSON.parse(\n b.evaluateBuiltinJson(\n JSON.stringify({ name, ctx: { artifacts: ctx.artifacts, metadata: ctx.metadata } }),\n ),\n ) as { skip: string[]; abort: boolean; insert: string[]; reason: string } | null;\n if (out === null) return null;\n return makeDecision({ skip: out.skip, abort: out.abort, insert: out.insert, reason: out.reason });\n}\n\nexport function autoConfigViaWasm(\n registry: StageRegistry,\n identityOpts: Record,\n b: PipeWasmBackend,\n): PipelineConfig {\n const available = Object.keys(registry.listAll());\n const out = JSON.parse(\n b.autoConfigJson(JSON.stringify({ available, identity_opts: identityOpts })),\n ) as {\n pipeline: string;\n stages: Array<{ use: string; needs?: string[]; on_error?: string; config?: Record }>;\n decisions?: string[];\n };\n const stages: StageSpec[] = out.stages.map((s) =>\n makeStageSpec({\n use: s.use,\n needs: s.needs ?? [],\n onError: (s.on_error ?? \"continue\") as StageSpec[\"onError\"],\n config: s.config ?? {},\n }),\n );\n return makePipelineConfig({ pipeline: out.pipeline, stages, decisions: out.decisions ?? [] });\n}\n\nexport function skipIfFalsyViaWasm(value: unknown, b: PipeWasmBackend): boolean {\n return JSON.parse(b.skipIfFalsyJson(JSON.stringify(value))) as boolean;\n}\n","/**\n * Pipeline resolver — build an ExecutionPlan and validate wiring.\n * Port of goldenpipe/engine/resolver.py.\n *\n * Edge-safe: no `node:` imports.\n */\n\nimport type { PipelineConfig, Stage, StageSpec } from \"../models.js\";\nimport { makeStageSpec } from \"../models.js\";\nimport type { StageRegistry } from \"./registry.js\";\nimport { getPipeWasmBackend } from \"../wasm/backend.js\";\nimport { resolveViaWasm } from \"../wasm/plannerJson.js\";\n\n/** A consumed artifact no stage (nor the `df` seed) produces (spec: MissingProducer).\n * Name retained for back-compat with host `catch (WiringError)` sites. */\nexport class WiringError extends Error {\n stage?: string;\n artifact?: string;\n /** legacy alias for {@link artifact}. */\n get missing(): string | undefined {\n return this.artifact;\n }\n constructor(message: string, extra?: { stage: string; artifact: string }) {\n super(message);\n this.name = \"WiringError\";\n if (extra) {\n this.stage = extra.stage;\n this.artifact = extra.artifact;\n }\n }\n}\n\nexport class AmbiguousProducerError extends Error {\n constructor(public artifact: string, public producers: string[]) {\n super(`Artifact '${artifact}' has ambiguous producers: ${producers.join(\", \")}`);\n this.name = \"AmbiguousProducerError\";\n }\n}\n\nexport class CycleError extends Error {\n constructor(public stages: string[]) {\n super(`Dependency cycle among stages: ${stages.join(\", \")}`);\n this.name = \"CycleError\";\n }\n}\n\nexport class UnknownNeedError extends Error {\n constructor(public stage: string, public needs: string[]) {\n super(`Stage '${stage}' needs unknown stage(s): ${needs.join(\", \")}`);\n this.name = \"UnknownNeedError\";\n }\n}\n\nexport interface PlannedStage {\n name: string;\n stage: Stage;\n spec: StageSpec;\n config: Record;\n}\n\nexport interface ExecutionPlan {\n stages: PlannedStage[];\n}\n\ninterface ResolveNode {\n pname: string;\n use: string;\n produces: string[];\n consumes: string[];\n needs: string[];\n planned: PlannedStage;\n}\n\n/**\n * Pure-TS core of {@link Resolver.resolve} — the dependency-DAG planner (spec §3.1),\n * guard-free so plannerJsonPure can call it without re-entering the WASM reroute guard.\n */\nexport function resolvePure(config: PipelineConfig, registry: StageRegistry): ExecutionPlan {\n // 1. Build the ordered node list (load prepended; registry.get throws -> unknown_stage).\n const nodes: ResolveNode[] = [];\n const hasLoad = registry.has(\"load\");\n if (hasLoad) {\n const load = registry.get(\"load\");\n nodes.push({\n pname: \"load\",\n use: \"load\",\n produces: [...load.info.produces],\n consumes: [],\n needs: [],\n planned: { name: \"load\", stage: load, spec: makeStageSpec(\"load\"), config: {} },\n });\n }\n for (const rawSpec of config.stages) {\n const spec = makeStageSpec(rawSpec);\n const stageObj = registry.get(spec.use);\n const pname = spec.name ?? stageObj.info.name;\n nodes.push({\n pname,\n use: spec.use,\n produces: [...stageObj.info.produces],\n consumes: [...stageObj.info.consumes],\n needs: [...spec.needs],\n planned: { name: pname, stage: stageObj, spec, config: spec.config },\n });\n }\n const n = nodes.length;\n const seedDf = !hasLoad;\n\n const keyToIdx = (k: string): number => nodes.findIndex((nd) => nd.use === k);\n\n const edges = new Set(); // \"a>b\"\n const addEdge = (a: number, b: number) => edges.add(`${a}>${b}`);\n const hasEdge = (a: number, b: number) => edges.has(`${a}>${b}`);\n\n // 2. needs edges (reported before missing/ambiguous).\n // (`!` on nodes[...] is compile-time only — indices are in-bounds by construction;\n // required by tsconfig `noUncheckedIndexedAccess`. Runtime behavior is unchanged.)\n for (let i = 0; i < n; i++) {\n for (const need of nodes[i]!.needs) {\n const j = keyToIdx(need);\n if (j < 0) throw new UnknownNeedError(nodes[i]!.pname, [need]);\n addEdge(j, i); // self-edge -> Cycle in step 4\n }\n }\n\n // 3. Guarded sole-producer edges. First violation (by index, then consumes order) wins.\n const producedBefore = (i: number, x: string): boolean => {\n if (seedDf && x === \"df\") return true;\n for (let j = 0; j < i; j++) if (nodes[j]!.produces.includes(x)) return true;\n return false;\n };\n for (let i = 0; i < n; i++) {\n for (const dep of nodes[i]!.consumes) {\n if (producedBefore(i, dep)) continue;\n const later: number[] = [];\n for (let j = i + 1; j < n; j++) if (nodes[j]!.produces.includes(dep)) later.push(j);\n if (later.length === 0) {\n throw new WiringError(\n `Stage '${nodes[i]!.pname}' consumes '${dep}' but no prior stage produces it.`,\n { stage: nodes[i]!.pname, artifact: dep },\n );\n } else if (later.length === 1) {\n addEdge(later[0]!, i);\n } else {\n // pinned by ANY must-precede edge (needs OR an earlier-consumes sole-producer edge):\n // exactly one -> deterministic binding; else AmbiguousProducer.\n const pinned = later.filter((j) => hasEdge(j, i)).length;\n if (pinned !== 1) {\n throw new AmbiguousProducerError(dep, later.map((j) => nodes[j]!.use));\n }\n }\n }\n }\n\n // 4. Stable Kahn topo-sort keyed by config index (min-heap emulated by sorted array).\n const indeg = new Array(n).fill(0);\n const adj: number[][] = Array.from({ length: n }, () => []);\n for (const e of edges) {\n const [a, b] = e.split(\">\").map(Number) as [number, number];\n if (a === b) {\n indeg[b] += 1; // self-edge -> stuck node, reported by the ascending-index fall-through\n continue;\n }\n adj[a]!.push(b);\n indeg[b] += 1;\n }\n const ready: number[] = [];\n for (let i = 0; i < n; i++) if (indeg[i] === 0) ready.push(i);\n ready.sort((x, y) => x - y);\n const order: number[] = [];\n while (ready.length > 0) {\n const u = ready.shift() as number; // smallest config index\n order.push(u);\n for (const v of adj[u]!) {\n indeg[v] -= 1;\n if (indeg[v] === 0) {\n let lo = 0;\n while (lo < ready.length && ready[lo]! < v) lo++;\n ready.splice(lo, 0, v);\n }\n }\n }\n if (order.length !== n) {\n const cyc: string[] = [];\n for (let i = 0; i < n; i++) if (indeg[i] > 0) cyc.push(nodes[i]!.pname);\n throw new CycleError(cyc);\n }\n\n // 5. Emit in sorted order.\n return { stages: order.map((i) => nodes[i]!.planned) };\n}\n\nexport const Resolver = {\n /**\n * Resolve a config + registry into an ordered ExecutionPlan. Auto-prepends\n * the built-in `load` stage when available and validates that every stage's\n * `consumes` is produced by an earlier stage.\n *\n * Routes through the registered WASM planner backend when one is enabled;\n * otherwise runs the pure-TS core.\n */\n resolve(config: PipelineConfig, registry: StageRegistry): ExecutionPlan {\n const b = getPipeWasmBackend();\n if (b) return resolveViaWasm(config, registry, b);\n return resolvePure(config, registry);\n },\n};\n","/**\n * Decision router — apply routing decisions to the remaining execution plan.\n * Port of goldenpipe/engine/router.py.\n *\n * Edge-safe: no `node:` imports.\n */\n\nimport type { Decision, PipeContext } from \"../models.js\";\nimport { makeStageSpec } from \"../models.js\";\nimport type { StageRegistry } from \"./registry.js\";\nimport type { PlannedStage } from \"./resolver.js\";\nimport { getPipeWasmBackend } from \"../wasm/backend.js\";\nimport { applyDecisionViaWasm } from \"../wasm/plannerJson.js\";\n\n/**\n * Pure-TS core of {@link Router.apply}. Guard-free so plannerJsonPure can call\n * it without re-entering the WASM reroute (no recursion).\n */\nexport function applyPure(\n decision: Decision,\n remaining: PlannedStage[],\n ctx: PipeContext,\n registry: StageRegistry,\n): PlannedStage[] {\n {\n if (decision.reason) {\n ctx.reasoning[\"_router\"] = decision.reason;\n }\n\n if (decision.abort) {\n ctx.reasoning[\"_router\"] = `ABORT: ${decision.reason}`;\n return [];\n }\n\n let next = remaining;\n if (decision.skip.length > 0) {\n const skipSet = new Set(decision.skip);\n next = next.filter((s) => !skipSet.has(s.name));\n }\n\n if (decision.insert.length > 0) {\n const inserted: PlannedStage[] = [];\n for (const name of decision.insert) {\n const stageObj = registry.get(name);\n inserted.push({\n name,\n stage: stageObj,\n spec: makeStageSpec(name),\n config: {},\n });\n }\n next = [...inserted, ...next];\n }\n\n return next;\n }\n}\n\nexport const Router = {\n /**\n * Apply a Decision (skip / abort / insert) to the remaining stages and\n * return the new remaining list. Records `decision.reason` in\n * `ctx.reasoning._router`.\n *\n * Routes through the registered WASM planner backend when enabled; otherwise\n * runs the pure-TS core.\n */\n apply(\n decision: Decision,\n remaining: PlannedStage[],\n ctx: PipeContext,\n registry: StageRegistry,\n ): PlannedStage[] {\n const b = getPipeWasmBackend();\n if (b) return applyDecisionViaWasm(decision, remaining, ctx, registry, b);\n return applyPure(decision, remaining, ctx, registry);\n },\n};\n","/**\n * Pipeline runner — execute stages with error handling and routing.\n * Port of goldenpipe/engine/runner.py.\n *\n * ASYNC: stage execution awaits each stage's `run`, because the GoldenMatch\n * `dedupe` adapter is async.\n *\n * Edge-safe: no `node:` imports.\n */\n\nimport type { PipeContext, StageResult } from \"../models.js\";\nimport { StageStatus } from \"../models.js\";\nimport type { StageRegistry } from \"./registry.js\";\nimport type { ExecutionPlan } from \"./resolver.js\";\nimport { Router } from \"./router.js\";\nimport { getPipeWasmBackend } from \"../wasm/backend.js\";\nimport { skipIfFalsyViaWasm } from \"../wasm/plannerJson.js\";\n\nexport function isFalsy(value: unknown): boolean {\n if (value === null || value === undefined) return true;\n if (value === false || value === 0 || value === \"\") return true;\n if (Array.isArray(value)) return value.length === 0;\n if (value instanceof Map || value instanceof Set) return value.size === 0;\n if (typeof value === \"object\") return Object.keys(value).length === 0;\n return false;\n}\n\nexport class Runner {\n constructor(private readonly registry: StageRegistry) {}\n\n /** Execute an ExecutionPlan against a PipeContext, returning per-stage results. */\n async run(plan: ExecutionPlan, ctx: PipeContext): Promise> {\n const results: Record = {};\n let remaining = [...plan.stages];\n\n while (remaining.length > 0) {\n const planned = remaining.shift()!;\n\n if (planned.spec.skipIf) {\n const artifact = ctx.artifacts[planned.spec.skipIf];\n const b = getPipeWasmBackend();\n const falsy = b ? skipIfFalsyViaWasm(artifact, b) : isFalsy(artifact);\n if (falsy) {\n results[planned.name] = { status: StageStatus.SKIPPED };\n ctx.reasoning[planned.name] =\n `Skipped: artifact '${planned.spec.skipIf}' is missing/falsy`;\n continue;\n }\n }\n\n const start = performance.now();\n try {\n // Make stage-level config available to the adapter via context.\n ctx.stageConfig = planned.config;\n\n await planned.stage.validate(ctx);\n const result = await planned.stage.run(ctx);\n ctx.timing[planned.name] = (performance.now() - start) / 1000;\n results[planned.name] = result;\n\n if (result.decision != null) {\n remaining = Router.apply(result.decision, remaining, ctx, this.registry);\n }\n } catch (e) {\n ctx.timing[planned.name] = (performance.now() - start) / 1000;\n const message = e instanceof Error ? e.message : String(e);\n results[planned.name] = { status: StageStatus.FAILED, error: message };\n ctx.reasoning[planned.name] = `Failed: ${message}`;\n\n if (planned.spec.onError === \"abort\") {\n break;\n }\n }\n }\n\n return results;\n }\n}\n","/**\n * Reporter — build a PipeResult from a PipeContext after execution.\n * Port of goldenpipe/engine/reporter.py.\n *\n * Edge-safe: no `node:` imports.\n */\n\nimport type { PipeContext, PipeResult, StageResult } from \"../models.js\";\nimport { PipeStatus, StageStatus } from \"../models.js\";\n\nexport const Reporter = {\n build(ctx: PipeContext, stages: Record): PipeResult {\n const entries = Object.entries(stages);\n\n const errors = entries\n .filter(([, r]) => r.status === StageStatus.FAILED && r.error)\n .map(([name, r]) => `${name}: ${r.error}`);\n\n const skipped = entries\n .filter(([, r]) => r.status === StageStatus.SKIPPED)\n .map(([name]) => name);\n\n const nonSkip = entries\n .map(([, r]) => r.status)\n .filter((s) => s !== StageStatus.SKIPPED);\n\n let status: PipeStatus;\n if (nonSkip.length === 0) {\n status = PipeStatus.SUCCESS;\n } else if (nonSkip.every((s) => s === StageStatus.FAILED)) {\n status = PipeStatus.FAILED;\n } else if (nonSkip.every((s) => s === StageStatus.SUCCESS)) {\n status = PipeStatus.SUCCESS;\n } else {\n status = PipeStatus.PARTIAL;\n }\n\n return {\n status,\n source: typeof ctx.metadata[\"source\"] === \"string\" ? (ctx.metadata[\"source\"] as string) : \"\",\n inputRows: typeof ctx.metadata[\"input_rows\"] === \"number\" ? (ctx.metadata[\"input_rows\"] as number) : 0,\n stages,\n artifacts: { ...ctx.artifacts },\n skipped,\n errors,\n reasoning: { ...ctx.reasoning },\n timing: { ...ctx.timing },\n };\n },\n};\n","/**\n * Built-in decision functions for pipeline routing.\n * Port of goldenpipe/decisions.py.\n *\n * Edge-safe: no `node:` imports.\n *\n * NOTE on TS sibling skew: GoldenCheck-JS `Finding.severity` is a numeric enum\n * (INFO=1, WARNING=2, ERROR=3) and has no `\"critical\"` level, and there is no\n * `\"pii_detection\"` check. The check adapter normalizes findings to a string\n * `severity` label (\"info\"/\"warning\"/\"error\") and a `check` string. So in\n * practice `severityGate` and `piiRouter` are no-ops against current\n * GoldenCheck-JS output — they are ported for structural parity and so that\n * custom stages emitting `\"critical\"` / `\"pii_detection\"` findings still route.\n */\n\nimport type { Decision, PipeContext } from \"./models.js\";\nimport { makeDecision } from \"./models.js\";\nimport { getPipeWasmBackend } from \"./wasm/backend.js\";\nimport { evaluateBuiltinViaWasm } from \"./wasm/plannerJson.js\";\n\ninterface NormalizedFinding {\n severity?: unknown;\n check?: unknown;\n}\n\nfunction findingsOf(ctx: PipeContext): NormalizedFinding[] | null {\n const findings = ctx.artifacts[\"findings\"];\n if (!Array.isArray(findings) || findings.length === 0) return null;\n return findings as NormalizedFinding[];\n}\n\n// ---------------------------------------------------------------------------\n// Guarded-public / pure-core split. The exported gate names (severityGate /\n// piiRouter / rowCountGate) stay stable for external consumers and route\n// through the WASM planner backend when one is registered; the `*Pure` cores\n// hold the original bodies and are called guard-free by plannerJsonPure (no\n// recursion).\n// ---------------------------------------------------------------------------\n\n/** Abort the pipeline if any finding has `critical` severity. */\nexport function severityGatePure(ctx: PipeContext): Decision | null {\n const findings = findingsOf(ctx);\n if (!findings) return null;\n\n const hasCritical = findings.some((f) => f.severity === \"critical\");\n if (hasCritical) {\n return makeDecision({ abort: true, reason: \"Critical findings detected\" });\n }\n return null;\n}\n\nexport function severityGate(ctx: PipeContext): Decision | null {\n const b = getPipeWasmBackend();\n if (b) return evaluateBuiltinViaWasm(\"severity_gate\", ctx, b);\n return severityGatePure(ctx);\n}\n\n/** Route to PPRL matching if PII is detected. */\nexport function piiRouterPure(ctx: PipeContext): Decision | null {\n const findings = findingsOf(ctx);\n if (!findings) return null;\n\n const hasPii = findings.some((f) => f.check === \"pii_detection\");\n if (hasPii) {\n return makeDecision({\n skip: [\"goldenmatch.dedupe\"],\n insert: [\"goldenmatch.dedupe_pprl\"],\n reason: \"PII detected, routing to PPRL matching\",\n });\n }\n return null;\n}\n\nexport function piiRouter(ctx: PipeContext): Decision | null {\n const b = getPipeWasmBackend();\n if (b) return evaluateBuiltinViaWasm(\"pii_router\", ctx, b);\n return piiRouterPure(ctx);\n}\n\n/** Skip matching if fewer than 2 rows. */\nexport function rowCountGatePure(ctx: PipeContext): Decision | null {\n const rowCount =\n typeof ctx.metadata[\"input_rows\"] === \"number\" ? (ctx.metadata[\"input_rows\"] as number) : 0;\n if (rowCount < 2) {\n return makeDecision({\n skip: [\"goldenmatch.dedupe\"],\n reason: `Only ${rowCount} row(s), skipping deduplication`,\n });\n }\n return null;\n}\n\nexport function rowCountGate(ctx: PipeContext): Decision | null {\n const b = getPipeWasmBackend();\n if (b) return evaluateBuiltinViaWasm(\"row_count_gate\", ctx, b);\n return rowCountGatePure(ctx);\n}\n","/**\n * Built-in LoadStage — marks `df` as available. The actual data loading is\n * handled by the Pipeline (file read) or supplied by the caller (runDf).\n * Port of goldenpipe/adapters/__init__.py LoadStage.\n *\n * Edge-safe: no `node:` imports.\n */\n\nimport type { PipeContext, Stage, StageResult } from \"../models.js\";\nimport { StageStatus } from \"../models.js\";\n\nexport const LoadStage: Stage = {\n info: { name: \"load\", produces: [\"df\"], consumes: [] },\n validate(_ctx: PipeContext): void {\n /* no-op */\n },\n async run(_ctx: PipeContext): Promise {\n return { status: StageStatus.SUCCESS };\n },\n rollback: null,\n};\n","/**\n * repair.ts — pure-TS repair-plan kernel. The SP2 TS mirror of\n * goldenpipe-core/src/repair.rs and goldenpipe/repair.py. Deterministic, no I/O,\n * no regex; hand-rolled ASCII matchers so the three surfaces are byte-identical\n * by construction.\n *\n * BYTE-PARITY NOTE: JS strings are UTF-16, so `s.length`/`s[i]` count code\n * UNITS. Python `len`/indexing and Rust `.chars()` count code POINTS. Every\n * predicate here first spreads to a code-point array (`const cs = [...s]`) and\n * indexes/slices THAT — never the raw string.\n */\n\n// coarse tags the host may supply\nconst COARSE = new Set([\"date\", \"email\", \"name\", \"phone\", \"zip\"]);\n\n// ASCII char-class primitives (no regex; \\d would diverge across engines)\n// Accept `string | undefined` because the repo's `noUncheckedIndexedAccess`\n// types `cs[i]` as possibly-undefined; length checks guarantee a real char at\n// runtime, and undefined -> false preserves byte-parity with Python/Rust.\nfunction isDigit(c: string | undefined): boolean {\n return c !== undefined && c >= \"0\" && c <= \"9\";\n}\nfunction isUpper(c: string | undefined): boolean {\n return c !== undefined && c >= \"A\" && c <= \"Z\";\n}\nfunction isAlnumUpper(c: string | undefined): boolean {\n return isDigit(c) || isUpper(c);\n}\n// `chars` is already a code-point array; nonempty + every char passes.\nfunction allChars(chars: string[], pred: (c: string) => boolean): boolean {\n return chars.length > 0 && chars.every(pred);\n}\nfunction asciiLower(s: string): string {\n let out = \"\";\n for (const c of s) {\n out += c >= \"A\" && c <= \"Z\" ? String.fromCharCode(c.charCodeAt(0) + 32) : c;\n }\n return out;\n}\nfunction isAsciiWs(c: string): boolean {\n return c === \" \" || c === \"\\t\" || c === \"\\n\" || c === \"\\r\" || c === \"\\f\" || c === \"\\v\";\n}\n\n// value predicates (detection shape, not full validation)\nfunction vCusip(s: string): boolean {\n const cs = [...s];\n return cs.length === 9 && allChars(cs, isAlnumUpper);\n}\nfunction vNpi(s: string): boolean {\n const cs = [...s];\n return cs.length === 10 && allChars(cs, isDigit);\n}\nfunction vImei(s: string): boolean {\n const cs = [...s];\n return cs.length === 15 && allChars(cs, isDigit);\n}\nfunction vEan(s: string): boolean {\n const cs = [...s];\n return (cs.length === 8 || cs.length === 13) && allChars(cs, isDigit);\n}\nfunction vIsbn(s: string): boolean {\n const cs = [...s];\n if (cs.length === 13 && allChars(cs, isDigit)) return true;\n const c9 = cs[9];\n return cs.length === 10 && allChars(cs.slice(0, 9), isDigit) && c9 !== undefined && \"0123456789Xx\".includes(c9);\n}\nfunction vAba(s: string): boolean {\n const cs = [...s];\n return cs.length === 9 && allChars(cs, isDigit);\n}\nfunction vIban(s: string): boolean {\n const cs = [...s];\n if (!(cs.length >= 15 && cs.length <= 34)) return false;\n return (\n isUpper(cs[0]) &&\n isUpper(cs[1]) &&\n isDigit(cs[2]) &&\n isDigit(cs[3]) &&\n allChars(cs.slice(4), isAlnumUpper)\n );\n}\nfunction vIsin(s: string): boolean {\n const cs = [...s];\n return (\n cs.length === 12 &&\n isUpper(cs[0]) &&\n isUpper(cs[1]) &&\n allChars(cs.slice(2, 11), isAlnumUpper) &&\n isDigit(cs[11])\n );\n}\nfunction vSwift(s: string): boolean {\n const cs = [...s];\n if (!(cs.length === 8 || cs.length === 11)) return false;\n return (\n allChars(cs.slice(0, 6), isUpper) &&\n allChars(cs.slice(6, 8), isAlnumUpper) &&\n (cs.length === 8 || allChars(cs.slice(8, 11), isAlnumUpper))\n );\n}\nfunction luhnOk(s: string): boolean {\n let total = 0;\n let alt = false;\n // iterate right-to-left over code points (reverse() avoids indexed access,\n // which noUncheckedIndexedAccess would type as possibly-undefined)\n for (const c of [...s].reverse()) {\n let x = c.charCodeAt(0) - 48; // digit value; callers guarantee all-digit\n if (alt) {\n x *= 2;\n if (x > 9) x -= 9;\n }\n total += x;\n alt = !alt;\n }\n return total % 10 === 0;\n}\nfunction vCreditCard(s: string): boolean {\n const t = [...s].filter((c) => c !== \" \" && c !== \"-\").join(\"\");\n const cs = [...t];\n return cs.length >= 13 && cs.length <= 19 && allChars(cs, isDigit) && luhnOk(t);\n}\n\n// detectors: [tag, name_hints_or_null, value_predicate] in fixed order —\n// name-gated group first (low false-positive), value-distinctive fallback second.\nconst DETECTORS: Array<[string, string[] | null, (s: string) => boolean]> = [\n [\"cusip\", [\"cusip\"], vCusip],\n [\"npi\", [\"npi\"], vNpi],\n [\"imei\", [\"imei\", \"imsi\"], vImei],\n [\"ean\", [\"ean\", \"gtin\", \"barcode\"], vEan],\n [\"isbn\", [\"isbn\"], vIsbn],\n [\"aba_routing\", [\"routing\", \"aba\"], vAba],\n [\"iban\", null, vIban],\n [\"isin\", null, vIsin],\n [\"swift\", null, vSwift],\n [\"credit_card\", null, vCreditCard],\n];\n\nfunction fineType(name: string, samples: string[]): string | null {\n const lname = asciiLower(name);\n const nonempty = samples.filter((s) => [...s].some((c) => !isAsciiWs(c)));\n if (nonempty.length === 0) return null;\n for (const [tag, hints, pred] of DETECTORS) {\n if (hints !== null && !hints.some((h) => lname.includes(h))) continue;\n let matches = 0;\n for (const s of nonempty) if (pred(s)) matches++;\n if (matches * 2 > nonempty.length) return tag;\n }\n return null;\n}\n\nfunction resolveTag(name: string, coarseType: string, samples: string[]): string | null {\n const ft = fineType(name, samples);\n if (ft !== null) return ft;\n return COARSE.has(coarseType) ? coarseType : null;\n}\n\n// per-fine-tag validator; credit_card uses luhn_validate.\nconst VALIDATOR: Record = {\n iban: \"iban_validate\",\n isin: \"isin_validate\",\n swift: \"swift_validate\",\n cusip: \"cusip_validate\",\n npi: \"npi_validate\",\n imei: \"imei_validate\",\n ean: \"ean_validate\",\n isbn: \"isbn_validate\",\n credit_card: \"luhn_validate\",\n aba_routing: \"aba_validate\",\n};\n\n// mapping table: check -> tag -> transforms; tag \"*\" = wildcard. Nested (not a\n// concatenated string key) so there is no separator char in the source.\nconst TABLE = new Map>();\nfunction tableSet(check: string, tag: string, transforms: string[]): void {\n let byTag = TABLE.get(check);\n if (byTag === undefined) {\n byTag = new Map();\n TABLE.set(check, byTag);\n }\n byTag.set(tag, transforms);\n}\ntableSet(\"encoding_detection\", \"*\", [\"fix_mojibake\", \"normalize_unicode\"]);\ntableSet(\"future_dated\", \"date\", [\"date_validate\"]);\ntableSet(\"temporal_order\", \"date\", [\"date_validate\"]);\ntableSet(\"stale_data\", \"date\", [\"date_validate\"]);\ntableSet(\"format_detection\", \"date\", [\"date_parse\"]);\ntableSet(\"format_detection\", \"email\", [\"email_normalize\"]);\ntableSet(\"pattern_consistency\", \"email\", [\"email_canonical\"]);\ntableSet(\"pattern_consistency\", \"name\", [\"name_proper\"]);\ntableSet(\"format_detection\", \"phone\", [\"phone_validate\"]);\ntableSet(\"pattern_consistency\", \"phone\", [\"phone_national\"]);\ntableSet(\"format_detection\", \"zip\", [\"zip_normalize\"]);\nfor (const [tag, validator] of Object.entries(VALIDATOR)) {\n tableSet(\"format_detection\", tag, [validator]);\n tableSet(\"pattern_consistency\", tag, [validator]);\n}\n\nfunction lookup(check: string, tag: string | null): string[] | null {\n const byTag = TABLE.get(check);\n if (byTag === undefined) return null;\n if (tag !== null && byTag.has(tag)) return byTag.get(tag)!;\n if (byTag.has(\"*\")) return byTag.get(\"*\")!;\n return null;\n}\n\nexport interface Finding {\n column?: string;\n check?: string;\n message?: string;\n severity?: string;\n}\nexport interface ColumnInput {\n name: string;\n coarse_type?: string;\n samples?: string[];\n}\nexport interface Repair {\n column: string;\n check: string;\n type_tag: string;\n suggested_transforms: string[];\n reason: string;\n}\nexport interface RepairPlan {\n repairs: Repair[];\n}\n\nexport function buildRepairPlan(findings: Finding[], columns: ColumnInput[]): RepairPlan {\n const tags = new Map();\n for (const c of columns) {\n tags.set(c.name, resolveTag(c.name, c.coarse_type ?? \"\", c.samples ?? []));\n }\n\n const repairs: Repair[] = [];\n for (const f of findings) {\n const col = f.column;\n const check = f.check ?? \"\";\n // encoding wildcard can apply even to an omitted-tag column present in `columns`\n if (col === undefined || !tags.has(col)) continue;\n const tag = tags.get(col)!;\n const transforms = lookup(check, tag);\n if (!transforms || transforms.length === 0) continue;\n repairs.push({\n column: col,\n check,\n type_tag: tag !== null ? tag : \"*\",\n suggested_transforms: [...transforms],\n reason: [...String(f.message ?? \"\")].slice(0, 80).join(\"\"),\n });\n }\n return { repairs };\n}\n","/**\n * repairHost.ts — TS producer glue for the repair plan (mirror of\n * goldenpipe/repair_host.py). Samples column values from the row array, builds\n * ColumnInputs, calls the pure repair.ts kernel, attaches the advisory artifact.\n * Also holds the Phase-2 FIXERS allowlist + config conversion (host policy,\n * identical to repair_host.py — NOT the parity-gated kernel).\n */\nimport { buildRepairPlan } from \"./repair.js\";\nimport type { ColumnInput, Finding, RepairPlan } from \"./repair.js\";\nimport type { ColumnContext } from \"./columnContext.js\";\nimport type { PipeContext, Row } from \"./models.js\";\nimport type { TransformSpec } from \"goldenflow/core\";\n\nconst SAMPLE_LIMIT = 20;\n\nexport function sampleColumn(rows: Row[], col: string, limit = SAMPLE_LIMIT): string[] {\n const out: string[] = [];\n for (const row of rows) {\n const v = (row as Record)[col];\n if (v === null || v === undefined) continue;\n const s = String(v);\n if (s.trim() === \"\") continue;\n out.push(s);\n if (out.length >= limit) break;\n }\n return out;\n}\n\nexport function buildColumnInputs(contexts: ColumnContext[], rows: Row[]): ColumnInput[] {\n const first = rows.length > 0 ? rows[0] : undefined;\n const names = new Set(first ? Object.keys(first as object) : []);\n const cols: ColumnInput[] = [];\n for (const ctx of contexts) {\n if (!names.has(ctx.name)) continue;\n cols.push({ name: ctx.name, coarse_type: ctx.inferredType, samples: sampleColumn(rows, ctx.name) });\n }\n return cols;\n}\n\nexport function attachRepairPlan(\n ctx: PipeContext,\n findings: Finding[],\n contexts: ColumnContext[],\n rows: Row[],\n): RepairPlan {\n const columns = buildColumnInputs(contexts, rows);\n const plan = buildRepairPlan(findings, columns);\n ctx.artifacts[\"repair_plan\"] = plan;\n const lines = plan.repairs.map(\n (item) => `repair: ${item.column} (${item.check}) -> ${item.suggested_transforms.join(\",\")} [${item.reason}]`,\n );\n if (lines.length > 0) ctx.reasoning[\"repair_plan\"] = lines.join(\"\\n\");\n return plan;\n}\n\n// ── FIXERS allowlist + conversion (identical to repair_host.py) ──────────\nexport const FIXERS: ReadonlySet = new Set([\n \"fix_mojibake\", \"normalize_unicode\", \"date_parse\", \"email_normalize\",\n \"email_canonical\", \"name_proper\", \"phone_national\", \"zip_normalize\",\n]);\n\nexport function repairTransformSpecs(plan: RepairPlan | undefined): {\n specs: TransformSpec[];\n skipped: Array<{ column: string; op: string }>;\n} {\n const byCol = new Map();\n const order: string[] = [];\n const skipped: Array<{ column: string; op: string }> = [];\n for (const item of plan?.repairs ?? []) {\n const col = item.column;\n for (const op of item.suggested_transforms ?? []) {\n if (FIXERS.has(op)) {\n let ops = byCol.get(col);\n if (ops === undefined) { ops = []; byCol.set(col, ops); order.push(col); }\n if (!ops.includes(op)) ops.push(op);\n } else {\n skipped.push({ column: col, op });\n }\n }\n }\n const specs: TransformSpec[] = order.map((c) => ({ column: c, ops: byCol.get(c) ?? [] }));\n return { specs, skipped };\n}\n\nexport function mergeTransforms(user: readonly TransformSpec[], repair: readonly TransformSpec[]): TransformSpec[] {\n const byCol = new Map();\n const order: string[] = [];\n for (const spec of [...user, ...repair]) {\n let ops = byCol.get(spec.column);\n if (ops === undefined) { ops = []; byCol.set(spec.column, ops); order.push(spec.column); }\n ops.push(...spec.ops);\n }\n return order.map((col) => {\n const seen = new Set();\n const ops: string[] = [];\n for (const op of byCol.get(col) ?? []) if (!seen.has(op)) { seen.add(op); ops.push(op); }\n return { column: col, ops };\n });\n}\n","/**\n * GoldenCheck adapter — wraps GoldenCheck-JS `scanData`.\n * Port of goldenpipe/adapters/check.py.\n *\n * Shape divergence vs Python: the Python adapter calls `scan_file(path)` and\n * reads `ctx.metadata[\"source\"]`. GoldenCheck-JS's edge-safe `scanData` instead\n * operates on a `TabularData` built from rows, so the TS adapter scans\n * `ctx.df` directly. This means `goldencheck.scan` succeeds in the in-memory\n * (`runDf`) path here, whereas the Python `run_df` path fails the scan stage\n * (it has no file). Use `run(source)` for cross-language parity.\n *\n * Edge-safe: no `node:` imports (GoldenCheck-JS core is edge-safe).\n */\n\nimport { scanData, TabularData, severityLabel } from \"goldencheck/core\";\nimport type { Finding, ColumnProfile } from \"goldencheck/core\";\nimport type { PipeContext, Stage, StageResult } from \"../models.js\";\nimport { StageStatus } from \"../models.js\";\nimport {\n buildContextsFromCheck,\n type ColumnContext,\n type ColumnProfileLike,\n type FindingLike,\n} from \"../columnContext.js\";\nimport { attachRepairPlan } from \"../repairHost.js\";\n\ninterface NormalizedFinding {\n severity: string;\n check: string;\n column: string;\n message: string;\n}\n\n/** Normalize a GoldenCheck-JS Finding (numeric severity) to the dict shape\n * the Python pipeline used (string severity label). */\nfunction normalizeFinding(f: Finding): NormalizedFinding {\n return {\n severity: severityLabel(f.severity).toLowerCase(),\n check: f.check,\n column: f.column,\n message: f.message,\n };\n}\n\n/** Map a GoldenCheck-JS ColumnProfile to the minimal shape columnContext consumes. */\nfunction toColumnProfileLike(cp: ColumnProfile): ColumnProfileLike {\n return {\n name: cp.name,\n inferredType: cp.inferredType,\n nullPct: cp.nullPct,\n uniqueCount: cp.uniqueCount,\n };\n}\n\nexport const ScanStage: Stage = {\n info: { name: \"goldencheck.scan\", produces: [\"findings\", \"profile\"], consumes: [\"df\"] },\n\n validate(ctx: PipeContext): void {\n if (ctx.df === null) {\n throw new Error(\"ScanStage: no df in context\");\n }\n },\n\n async run(ctx: PipeContext): Promise {\n const rows = ctx.df ?? [];\n const data = new TabularData(rows);\n const stageCfg = ctx.stageConfig;\n const opts = stageCfg && Object.keys(stageCfg).length > 0 ? stageCfg : undefined;\n const result = scanData(data, opts as Parameters[1]);\n\n const findings: NormalizedFinding[] = result.findings.map(normalizeFinding);\n const columnProfiles = result.profile.columns;\n\n ctx.artifacts[\"findings\"] = findings;\n ctx.artifacts[\"profile\"] = result.profile;\n\n // Build column contexts for downstream stages (best-effort enrichment).\n try {\n const profileLikes: ColumnProfileLike[] = columnProfiles.map(toColumnProfileLike);\n const findingLikes: FindingLike[] = findings.map((f) => ({\n column: f.column,\n check: f.check,\n message: f.message,\n }));\n ctx.artifacts[\"column_contexts\"] = buildContextsFromCheck(findingLikes, profileLikes);\n } catch {\n ctx.artifacts[\"column_contexts\"] = [];\n }\n\n // Advisory repair-plan (Phase 1 producer, TS side). Never throws.\n try {\n const rp_findings = (ctx.artifacts[\"findings\"] as NormalizedFinding[]) ?? [];\n const rp_contexts = (ctx.artifacts[\"column_contexts\"] as ColumnContext[]) ?? [];\n if (ctx.df && rp_findings.length > 0 && rp_contexts.length > 0) {\n attachRepairPlan(ctx, rp_findings, rp_contexts, ctx.df);\n }\n } catch {\n /* advisory: never break the scan stage */\n }\n\n return { status: StageStatus.SUCCESS };\n },\n\n rollback: null,\n};\n","/**\n * GoldenFlow adapter — wraps GoldenFlow-JS `TransformEngine.transformDf`.\n * Port of goldenpipe/adapters/flow.py.\n *\n * Shape note: GoldenFlow-JS exposes `new TransformEngine(config).transformDf(rows)`\n * which returns `{ rows, columns, manifest }` (the Python sibling's\n * `transform_df(df)` returns an object with `.df` + `.manifest`). We read\n * `.rows` back into `ctx.df` and surface `.manifest` as an artifact.\n *\n * Edge-safe: no `node:` imports (GoldenFlow-JS core is edge-safe).\n */\n\nimport { TransformEngine } from \"goldenflow/core\";\nimport type { GoldenFlowConfig, TransformSpec } from \"goldenflow/core\";\nimport type { PipeContext, Stage, StageResult, Row } from \"../models.js\";\nimport { StageStatus } from \"../models.js\";\nimport { enrichContextsFromFlow, type ColumnContext, type ManifestRecordLike } from \"../columnContext.js\";\nimport { repairTransformSpecs, mergeTransforms } from \"../repairHost.js\";\n\nexport const TransformStage: Stage = {\n info: { name: \"goldenflow.transform\", produces: [\"df\", \"manifest\"], consumes: [\"df\"] },\n\n validate(ctx: PipeContext): void {\n if (ctx.df === null) {\n throw new Error(\"TransformStage: no df in context\");\n }\n },\n\n async run(ctx: PipeContext): Promise {\n const rows = (ctx.df ?? []) as Row[];\n const rawCfg: Record = { ...(ctx.stageConfig ?? {}) };\n const apply = rawCfg[\"apply_repairs\"] === true;\n delete rawCfg[\"apply_repairs\"];\n\n let config: Partial | undefined;\n if (apply) {\n const plan = ctx.artifacts[\"repair_plan\"] as Parameters[0];\n const { specs, skipped } = repairTransformSpecs(plan);\n const userTransforms = (rawCfg[\"transforms\"] as TransformSpec[] | undefined) ?? [];\n if (skipped.length > 0) {\n ctx.reasoning[\"repair_skipped\"] = skipped.map((s) => `${s.column}:${s.op}`).join(\"; \");\n }\n if (specs.length > 0 || userTransforms.length > 0) {\n config = { ...rawCfg, transforms: mergeTransforms(userTransforms, specs) } as Partial;\n } else {\n config = Object.keys(rawCfg).length > 0 ? (rawCfg as Partial) : undefined;\n }\n } else {\n config = Object.keys(rawCfg).length > 0 ? (rawCfg as Partial) : undefined;\n }\n\n const engine = new TransformEngine(config);\n const result = engine.transformDf(rows);\n\n ctx.df = [...result.rows] as Row[];\n ctx.artifacts[\"manifest\"] = result.manifest;\n\n // Enrich column contexts with transform information (best-effort).\n const contexts = ctx.artifacts[\"column_contexts\"];\n if (Array.isArray(contexts)) {\n try {\n const records: ManifestRecordLike[] = result.manifest.records.map((r) => ({\n column: r.column,\n transform: r.transform,\n affectedRows: r.affectedRows,\n }));\n enrichContextsFromFlow(contexts as ColumnContext[], records);\n } catch {\n /* best-effort: never break the pipeline on enrichment failure */\n }\n }\n\n return { status: StageStatus.SUCCESS };\n },\n\n rollback: null,\n};\n","/**\n * GoldenMatch adapter — wraps GoldenMatch-JS `dedupe`.\n * Port of goldenpipe/adapters/match.py (+ `_build_config_from_contexts`).\n *\n * `dedupe` is ASYNC, so this stage's `run` awaits it (and the whole runner is\n * async). Config selection priority mirrors Python:\n * 1. explicit stage config (from YAML / PipelineConfig.config)\n * 2. config built from upstream column contexts\n * 3. GoldenMatch auto-configure (no config → shorthand path)\n *\n * Shape divergences from the Python sibling, surfaced as artifacts:\n * - GoldenMatch-JS `DedupeResult` exposes `.goldenRecords` (not `.golden`),\n * `.dupes`, `.unique`, `.stats`, `.scoredPairs`. We map `goldenRecords` to\n * the `golden` artifact for parity with the Python pipeline's artifact name.\n * - `matchkey_used` is derived from the built config's first matchkey (the\n * JS result does not carry the resolved matchkey list back).\n *\n * Edge-safe: no `node:` imports (GoldenMatch-JS core is edge-safe).\n */\n\nimport { dedupe, makeConfig, makeMatchkeyConfig, makeMatchkeyField, makeBlockingConfig } from \"goldenmatch/core\";\nimport type {\n GoldenMatchConfig,\n MatchkeyConfig,\n MatchkeyField,\n BlockingConfig,\n BlockingKeyConfig,\n} from \"goldenmatch/core\";\nimport type { PipeContext, Stage, StageResult, Row } from \"../models.js\";\nimport { StageStatus } from \"../models.js\";\nimport {\n ColumnType,\n distinctNonNull,\n nullRateOf,\n type ColumnContext,\n} from \"../columnContext.js\";\n\n/** Cast every cell to string, mirroring the Python adapter's defensive cast\n * that prevents mixed-type-column schema mismatches reaching GoldenMatch. */\nfunction castRowsToString(rows: readonly Row[]): Row[] {\n return rows.map((row) => {\n const out: Row = {};\n for (const [k, v] of Object.entries(row)) {\n out[k] = v === null || v === undefined ? \"\" : String(v);\n }\n return out;\n });\n}\n\nexport const DedupeStage: Stage = {\n info: { name: \"goldenmatch.dedupe\", produces: [\"clusters\", \"golden\"], consumes: [\"df\"] },\n\n validate(ctx: PipeContext): void {\n if (ctx.df === null) {\n throw new Error(\"DedupeStage: no df in context\");\n }\n },\n\n async run(ctx: PipeContext): Promise {\n const rows = castRowsToString(ctx.df ?? []);\n ctx.df = rows;\n\n const stageCfg = ctx.stageConfig;\n let config: GoldenMatchConfig | null = null;\n\n // Priority 1: explicit stage config.\n if (stageCfg && Object.keys(stageCfg).length > 0) {\n config = makeConfig(stageCfg as Partial);\n } else {\n // Priority 2: build config from upstream column contexts.\n const contexts = ctx.artifacts[\"column_contexts\"];\n if (Array.isArray(contexts) && contexts.length > 0) {\n config = buildConfigFromContexts(contexts as ColumnContext[], rows);\n }\n }\n\n // Priority 3 falls through: no config → dedupe auto-configures.\n const result = config !== null ? await dedupe(rows, { config }) : await dedupe(rows);\n\n ctx.artifacts[\"clusters\"] = result.clusters;\n ctx.artifacts[\"golden\"] = result.goldenRecords;\n ctx.artifacts[\"unique\"] = result.unique;\n ctx.artifacts[\"dupes\"] = result.dupes;\n ctx.artifacts[\"match_stats\"] = result.stats;\n ctx.artifacts[\"scored_pairs\"] = result.scoredPairs;\n\n // Surface the first matchkey name (best-effort) for downstream stages.\n const mks = config?.matchkeys;\n if (mks && mks.length > 0) {\n ctx.artifacts[\"matchkey_used\"] = mks[0]!.name;\n }\n\n return { status: StageStatus.SUCCESS };\n },\n\n rollback: null,\n};\n\n/**\n * Build a GoldenMatchConfig from pipeline column contexts. Returns `null` if no\n * usable matchkeys can be built (caller then falls back to auto-configure).\n * Port of `_build_config_from_contexts`.\n */\nexport function buildConfigFromContexts(\n contexts: readonly ColumnContext[],\n rows: readonly Row[],\n): GoldenMatchConfig | null {\n const nameCols = contexts.filter(\n (c) => c.inferredType === ColumnType.NAME && c.isIdentifier,\n );\n const emailCols = contexts.filter((c) => c.inferredType === ColumnType.EMAIL);\n const geoCols = contexts.filter((c) => c.inferredType === ColumnType.GEO);\n\n const matchkeys: MatchkeyConfig[] = [];\n\n // Exact matchkeys for high-quality discriminators (email).\n for (const col of emailCols) {\n matchkeys.push(\n makeMatchkeyConfig({\n name: `exact_${col.name}`,\n type: \"exact\",\n fields: [makeMatchkeyField({ field: col.name, transforms: [\"lowercase\", \"strip\"], scorer: \"exact\" })],\n }),\n );\n }\n\n // Fuzzy matchkey on name columns (the core of person matching).\n if (nameCols.length > 0) {\n const fuzzyFields: MatchkeyField[] = nameCols.map((col) =>\n makeMatchkeyField({\n field: col.name,\n scorer: \"jaro_winkler\",\n weight: 1.0,\n transforms: [\"lowercase\", \"strip\"],\n }),\n );\n matchkeys.push(\n makeMatchkeyConfig({\n name: \"fuzzy_names\",\n type: \"weighted\",\n threshold: 0.85,\n fields: fuzzyFields,\n }),\n );\n }\n\n // Fallback: no identifier columns — use discriminative string columns.\n // Exclude low-cardinality columns (they inflate fuzzy scores without\n // providing meaningful discrimination).\n if (matchkeys.length === 0) {\n let stringCols = contexts.filter(\n (c) => c.inferredType === ColumnType.STRING || c.inferredType === ColumnType.NAME,\n );\n if (rows.length > 0) {\n const minCardinality = Math.max(10, Math.floor(rows.length * 0.05));\n stringCols = stringCols.filter((c) => distinctNonNull(rows, c.name) >= minCardinality);\n }\n const fallbackFields: MatchkeyField[] = stringCols.slice(0, 3).map((col) =>\n makeMatchkeyField({\n field: col.name,\n scorer: \"jaro_winkler\",\n weight: 1.0,\n transforms: [\"lowercase\", \"strip\"],\n }),\n );\n if (fallbackFields.length > 0) {\n matchkeys.push(\n makeMatchkeyConfig({\n name: \"fuzzy_fallback\",\n type: \"weighted\",\n threshold: 0.85,\n fields: fallbackFields,\n }),\n );\n }\n }\n\n // No matchkeys → give up; caller falls back to auto-configure.\n if (matchkeys.length === 0) {\n return null;\n }\n\n // Blocking: compound geo columns with name to prevent cross-region false\n // positives. Prefer low-cardinality geo (state ~50) over high (city ~3000).\n let bestGeo: string | null = null;\n if (geoCols.length > 0 && rows.length > 0) {\n const maxNullRate = 0.2;\n const geoCandidates: Array<[string, number]> = [];\n for (const g of geoCols) {\n if (nullRateOf(rows, g.name) <= maxNullRate) {\n geoCandidates.push([g.name, distinctNonNull(rows, g.name)]);\n }\n }\n if (geoCandidates.length > 0) {\n geoCandidates.sort((a, b) => a[1] - b[1]);\n bestGeo = geoCandidates[0]![0];\n }\n }\n\n const makeBlocking = (\n primaryFields: string[],\n recallName: string,\n withGeo = false,\n ): BlockingConfig => {\n const passes: BlockingKeyConfig[] = [\n { fields: primaryFields, transforms: [\"lowercase\", \"strip\"] },\n ];\n if (withGeo) {\n passes.push({ fields: primaryFields, transforms: [\"lowercase\", \"substring:0:3\"] });\n }\n // Name-only soundex recall pass (phonetic variants; relies on skipOversized).\n passes.push({ fields: [recallName], transforms: [\"lowercase\", \"soundex\"] });\n return makeBlockingConfig({\n strategy: \"multi_pass\",\n keys: [passes[0]!],\n passes,\n maxBlockSize: 500,\n skipOversized: true,\n });\n };\n\n let blocking: BlockingConfig | null = null;\n const lastNameCols = nameCols.filter((c) => c.name.toLowerCase().includes(\"last\"));\n if (lastNameCols.length > 0) {\n const bestName = lastNameCols[0]!.name;\n blocking = bestGeo\n ? makeBlocking([bestGeo, bestName], bestName, true)\n : makeBlocking([bestName], bestName);\n } else if (nameCols.length > 0) {\n const bestName = nameCols[0]!.name;\n if (bestGeo) {\n blocking = makeBlocking([bestGeo, bestName], bestName, true);\n } else {\n blocking = makeBlockingConfig({\n strategy: \"static\",\n keys: [{ fields: [bestName], transforms: [\"lowercase\", \"soundex\"] }],\n maxBlockSize: 500,\n skipOversized: true,\n });\n }\n }\n\n // Fallback: no name columns, but string matchkeys + geo present.\n if (!blocking && bestGeo && matchkeys.length > 0) {\n const fuzzyMks = matchkeys.filter((mk) => mk.type === \"weighted\");\n const first = fuzzyMks[0];\n if (first && first.fields.length > 0) {\n const anchor = first.fields[0]!.field;\n blocking = makeBlocking([bestGeo, anchor], anchor, true);\n }\n }\n\n // Still no blocking → let GoldenMatch auto-suggest.\n if (!blocking) {\n blocking = makeBlockingConfig({ keys: [], autoSuggest: true });\n }\n\n return makeConfig({ matchkeys, blocking });\n}\n","/**\n * infer_schema — GoldenPipe stage that runs InferMap to label columns.\n * TS port of goldenpipe/stages/infer_schema.py. Produces\n * `inferred_schema` (goldencheck-types.InferredSchema | null) + (on the\n * auto-detect path) `infer_schema_evidence`. Consumes nothing — meant to run\n * before stages that want typed columns. Opt-in (not in DEFAULT_STAGE_ORDER),\n * matching Python.\n *\n * Config (ctx.stageConfig): schema?: InferredSchema | no_infer?: boolean |\n * domain?: string. Precedence: schema > no_infer > domain > auto-detect;\n * the three are mutually exclusive (conflict throws in run()).\n */\nimport {\n SCHEMA_VERSION,\n UNMAPPED_TYPE,\n loadDomain,\n type FieldMapping,\n type InferredSchema,\n} from \"goldencheck-types\";\nimport {\n detectDomainDetailed,\n map,\n DomainPackTarget,\n type MapResult,\n} from \"infermap\";\n\nimport type { PipeContext, Stage, StageResult } from \"../models.js\";\nimport { StageStatus } from \"../models.js\";\n\n/** Mirror of Python `_result_to_inferred_schema`. `confidence` is overwritten\n * by the caller with the detection score. */\nfunction resultToInferredSchema(result: MapResult, domain: string): InferredSchema {\n const fields: Record = {};\n for (const fm of result.mappings) {\n fields[fm.source] = {\n source_col: fm.source,\n canonical: fm.target, // soft mode sets target = null -> canonical null\n type: fm.target ? fm.target : UNMAPPED_TYPE,\n confidence: fm.confidence,\n evidence: { reasoning: fm.reasoning },\n };\n }\n for (const col of result.unmappedSource) {\n if (!(col in fields)) {\n fields[col] = {\n source_col: col,\n canonical: null,\n type: UNMAPPED_TYPE,\n confidence: 0.0,\n evidence: {},\n };\n }\n }\n // Loop-based min — never spread an array into Math.min/max (it throws\n // RangeError past ~65K elements; banned by ast-grep ts-no-spread-math-min-max).\n const confidence = result.mappings.length\n ? result.mappings.reduce((m, fm) => (fm.confidence < m ? fm.confidence : m), Infinity)\n : 0.0;\n // Stamp schema_version like the Python InferredSchema dataclass (default\n // SCHEMA_VERSION) so the artifact is field-identical cross-surface.\n return { domain, fields, confidence, schema_version: SCHEMA_VERSION };\n}\n\n/** Mirror of Python `_validate_flags`: at most one of schema/no_infer/domain. */\nfunction validateFlags(cfg: Record): void {\n const set =\n (cfg.schema != null ? 1 : 0) +\n (cfg.no_infer ? 1 : 0) +\n (cfg.domain != null ? 1 : 0);\n if (set > 1) {\n throw new Error(\n \"conflict: 'schema', 'no_infer', and 'domain' are mutually exclusive. \" +\n \"Precedence: schema > no_infer > domain > auto-detect.\",\n );\n }\n}\n\nexport const InferSchemaStage: Stage = {\n info: {\n // Matches Python's declared produces=[\"inferred_schema\"]. The stage ALSO sets\n // `infer_schema_evidence` at runtime (via setdefault) but, like Python, does\n // not declare it — replicated faithfully so the stage contract is identical.\n name: \"infer_schema\",\n produces: [\"inferred_schema\"],\n consumes: [],\n },\n\n // No precondition: consumes [] and a null df is a valid SUCCESS branch, so\n // validate() must NOT throw. The flag-conflict check lives in run() (mirrors\n // Python's _validate_flags at the top of run()).\n validate(_ctx: PipeContext): void {},\n\n async run(ctx: PipeContext): Promise {\n const cfg = ctx.stageConfig ?? {};\n validateFlags(cfg);\n\n if (cfg.schema != null) {\n ctx.artifacts[\"inferred_schema\"] = cfg.schema;\n return { status: StageStatus.SUCCESS };\n }\n if (cfg.no_infer) {\n ctx.artifacts[\"inferred_schema\"] = null;\n return { status: StageStatus.SUCCESS };\n }\n if (ctx.df === null) {\n ctx.artifacts[\"inferred_schema\"] = null;\n return { status: StageStatus.SUCCESS };\n }\n\n const explicit = cfg.domain as string | undefined;\n let domain: string;\n let detectScore: number;\n let detectEvidence: Record;\n\n // Truthy check mirrors Python `if explicit_domain:` — an empty-string domain\n // falls through to auto-detect (validateFlags counted it via `!= null`).\n if (explicit) {\n domain = explicit;\n detectScore = 1.0;\n detectEvidence = { detect_reason: \"explicit\" };\n } else {\n const detection = detectDomainDetailed({ records: ctx.df });\n if (detection.domain !== null) {\n domain = detection.domain;\n detectScore = detection.score;\n detectEvidence = {\n detect_reason: detection.reason,\n detect_score: detection.score,\n runner_up: detection.runner_up,\n runner_up_score: detection.runner_up_score,\n };\n } else {\n domain = \"generic\";\n detectScore = 0.0;\n detectEvidence = {\n detect_reason: detection.reason,\n detect_score: detection.score,\n runner_up: detection.runner_up,\n runner_up_score: detection.runner_up_score,\n fallback: true,\n };\n }\n }\n\n const result = map(\n { records: ctx.df },\n new DomainPackTarget(loadDomain(domain)),\n { soft: true },\n );\n const inferred: InferredSchema = {\n ...resultToInferredSchema(result, domain),\n confidence: detectScore,\n };\n\n ctx.artifacts[\"inferred_schema\"] = inferred;\n // setdefault: only set when a prior stage hasn't already.\n if (!(\"infer_schema_evidence\" in ctx.artifacts)) {\n ctx.artifacts[\"infer_schema_evidence\"] = detectEvidence;\n }\n return { status: StageStatus.SUCCESS };\n },\n};\n","/**\n * Adapters index — built-in suite stages + registry wiring.\n * Replaces Python's importlib entry-point discovery with a STATIC registry.\n *\n * Edge-safe: no `node:` imports.\n */\n\nimport { StageRegistry } from \"../engine/registry.js\";\nimport { LoadStage } from \"./load.js\";\nimport { ScanStage } from \"./check.js\";\nimport { TransformStage } from \"./flow.js\";\nimport { DedupeStage } from \"./match.js\";\nimport { InferSchemaStage } from \"./infer.js\";\n\nexport { LoadStage } from \"./load.js\";\nexport { ScanStage } from \"./check.js\";\nexport { TransformStage } from \"./flow.js\";\nexport { DedupeStage, buildConfigFromContexts } from \"./match.js\";\nexport { InferSchemaStage } from \"./infer.js\";\n\n/**\n * Build a registry with all built-in suite stages registered:\n * - `load` (built-in)\n * - `goldencheck.scan`\n * - `goldenflow.transform`\n * - `goldenmatch.dedupe`\n *\n * This is the TS analogue of Python's `StageRegistry.discover()`.\n */\nexport function buildDefaultRegistry(): StageRegistry {\n const registry = new StageRegistry();\n registry.register(LoadStage);\n registry.register(ScanStage);\n registry.register(TransformStage);\n registry.register(DedupeStage);\n registry.register(InferSchemaStage);\n return registry;\n}\n","/**\n * autoconfigPlanner.ts — pure-TS auto-config BRAIN (decision core), ported from\n * autoconfig_planner.py / goldenpipe-core planner.rs. Rust is the source of\n * truth; this is the SP3 TS fallback proven to reproduce the golden vectors.\n * Pure: no InferMap/profiling (that is a PlannerInput the caller supplies).\n */\nexport interface PipeProfile {\n nRows: number;\n nCols: number;\n columnNames: string[];\n dtypes: string[];\n inferredDomain: string | null;\n domainConfidence: number;\n}\nexport interface ComplexityProfile {\n maxNullDensity: number;\n meanNullDensity: number;\n}\nexport interface PlannerInput {\n runtime: PipeProfile;\n complexity: ComplexityProfile;\n}\nexport interface PlannedStage {\n name: string;\n config: Record;\n}\nexport interface PipePlan {\n stages: PlannedStage[];\n ruleName: string;\n confidence: number;\n evidence: Record;\n}\n\nconst RED_NULL_DENSITY = 0.6;\nconst CONFIDENT_DOMAIN_THRESHOLD = 0.5;\nexport const SCALE_ROUTE_MIN_ROWS = 1_000_000;\nconst THROUGHPUT_RECALL_TARGET = 0.95;\nconst GREEN_THRESHOLD = 0.7;\nconst AMBER_THRESHOLD = 0.4;\n\nexport function bandOf(confidence: number): \"green\" | \"amber\" | \"red\" {\n if (confidence >= GREEN_THRESHOLD) return \"green\";\n if (confidence >= AMBER_THRESHOLD) return \"amber\";\n return \"red\";\n}\n\nfunction stage(name: string, config: Record = {}): PlannedStage {\n return { name, config };\n}\n\nfunction defaultDedupeStages(): PlannedStage[] {\n return [stage(\"goldencheck.scan\"), stage(\"goldenflow.transform\"), stage(\"goldenmatch.dedupe\")];\n}\n\n// Evidence keys are snake_case, in the exact Python/Rust order.\nfunction defaultEvidence(inp: PlannerInput): Record {\n return {\n n_rows: inp.runtime.nRows,\n n_cols: inp.runtime.nCols,\n inferred_domain: inp.runtime.inferredDomain,\n domain_confidence: inp.runtime.domainConfidence,\n max_null_density: inp.complexity.maxNullDensity,\n mean_null_density: inp.complexity.meanNullDensity,\n };\n}\n\nexport function planPipeline(inp: PlannerInput): PipePlan {\n const r = inp.runtime;\n if (r.nRows <= 1) {\n return {\n stages: [stage(\"goldencheck.scan\"), stage(\"goldenflow.transform\")],\n ruleName: \"pathological\",\n confidence: 1.0,\n evidence: defaultEvidence(inp),\n };\n }\n if (r.inferredDomain !== null && r.domainConfidence >= CONFIDENT_DOMAIN_THRESHOLD) {\n return {\n stages: [\n stage(\"infer_schema\", { domain: r.inferredDomain }),\n stage(\"goldencheck.scan\"),\n stage(\"goldenflow.transform\"),\n stage(\"goldenmatch.dedupe\"),\n ],\n ruleName: \"confident_schema\",\n confidence: r.domainConfidence,\n evidence: defaultEvidence(inp),\n };\n }\n if (r.inferredDomain === null && inp.complexity.maxNullDensity > RED_NULL_DENSITY) {\n return {\n stages: defaultDedupeStages(),\n ruleName: \"low_confidence\",\n confidence: 0.3,\n evidence: defaultEvidence(inp),\n };\n }\n return {\n stages: defaultDedupeStages(),\n ruleName: \"default\",\n confidence: 0.7,\n evidence: defaultEvidence(inp),\n };\n}\n\nexport function applyScaleHints(plan: PipePlan, runtime: PipeProfile): PipePlan {\n const hasDedupe = plan.stages.some((s) => s.name === \"goldenmatch.dedupe\");\n if (runtime.nRows < SCALE_ROUTE_MIN_ROWS || !hasDedupe) {\n return {\n stages: plan.stages.map((s) => ({ name: s.name, config: { ...s.config } })),\n ruleName: plan.ruleName,\n confidence: plan.confidence,\n evidence: { ...plan.evidence },\n };\n }\n const stages = plan.stages.map((s) =>\n s.name === \"goldenmatch.dedupe\"\n ? {\n name: s.name,\n config: {\n ...s.config,\n _dedupe_hints: { throughput: { recall_target: THROUGHPUT_RECALL_TARGET } },\n },\n }\n : { name: s.name, config: { ...s.config } },\n );\n return {\n stages,\n ruleName: plan.ruleName,\n confidence: plan.confidence,\n evidence: { ...plan.evidence, scale_hinted: true },\n };\n}\n","/** goldenpipe-local exceptions. */\nexport class PipeNotConfidentError extends Error {\n constructor(message: string) {\n super(message);\n this.name = \"PipeNotConfidentError\";\n }\n}\n","/**\n * autoconfigGlue.ts — impure host bracket for the auto-config brain\n * (InferMap detect + Row[] profiling in; PipelineConfig out; refuse-on-RED).\n * TS analogue of goldenpipe/autoconfig_glue.py. The pure decision core is\n * autoconfigPlanner.ts.\n */\nimport type { Row } from \"./index.js\";\nimport {\n bandOf,\n type PipeProfile,\n type ComplexityProfile,\n type PlannerInput,\n type PipePlan,\n} from \"./autoconfigPlanner.js\";\nimport { detectDomainDetailed } from \"infermap\";\nimport { PipeNotConfidentError } from \"./errors.js\";\nimport { makePipelineConfig, makeStageSpec, type PipelineConfig } from \"./models.js\";\n\nexport const REFUSE_ROW_THRESHOLD = 100_000;\n\nexport function profileContext(rows: readonly Row[]): PipeProfile {\n if (rows.length === 0) {\n return {\n nRows: 0, nCols: 0, columnNames: [], dtypes: [],\n inferredDomain: null, domainConfidence: 0,\n };\n }\n const columnNames = Object.keys(rows[0]!);\n const det = detectDomainDetailed({ columns: columnNames });\n return {\n nRows: rows.length,\n nCols: columnNames.length,\n columnNames,\n dtypes: [],\n inferredDomain: det.domain,\n domainConfidence: det.domain !== null ? det.score : 0,\n };\n}\n\nexport function profileComplexity(rows: readonly Row[]): ComplexityProfile {\n const nRows = rows.length;\n if (nRows === 0) return { maxNullDensity: 0, meanNullDensity: 0 };\n const columnNames = Object.keys(rows[0]!);\n if (columnNames.length === 0) return { maxNullDensity: 0, meanNullDensity: 0 };\n const fractions = columnNames.map((c) => {\n let nulls = 0;\n for (const r of rows) {\n const v = r[c];\n if (v === null || v === undefined) nulls++;\n }\n return nulls / nRows;\n });\n // Loop-max, NOT Math.max(...spread) (ts-no-spread-math-min-max is error-severity).\n let maxNullDensity = 0;\n for (const f of fractions) if (f > maxNullDensity) maxNullDensity = f;\n return {\n maxNullDensity,\n meanNullDensity: fractions.reduce((a, b) => a + b, 0) / fractions.length,\n };\n}\n\nexport function buildPlannerInput(rows: readonly Row[]): PlannerInput {\n return { runtime: profileContext(rows), complexity: profileComplexity(rows) };\n}\n\nexport function enforceConfidence(plan: PipePlan, runtime: PipeProfile): void {\n if (bandOf(plan.confidence) !== \"red\") return;\n if (runtime.nRows >= REFUSE_ROW_THRESHOLD) {\n throw new PipeNotConfidentError(\n `auto-config not confident (rule=${plan.ruleName}, confidence=${plan.confidence}) ` +\n `on ${runtime.nRows} rows; supply an explicit pipeline config or reduce the input size. ` +\n `evidence=${JSON.stringify(plan.evidence)}`,\n );\n }\n}\n\nexport function planToConfig(\n plan: PipePlan,\n available: Record,\n identityOpts: Record,\n): PipelineConfig {\n const stages = plan.stages\n .filter((s) => s.name in available)\n .map((s) => makeStageSpec({ use: s.name, config: s.config }));\n const IDENTITY = \"goldenmatch.identity_resolve\";\n if (Object.keys(identityOpts).length > 0 && IDENTITY in available) {\n stages.push(makeStageSpec({ use: IDENTITY, config: identityOpts }));\n }\n return makePipelineConfig({ pipeline: \"auto\", stages });\n}\n","/**\n * Pipeline — high-level orchestrator + programmatic run helpers.\n * Port of goldenpipe/pipeline.py + goldenpipe/_api.py (the DataFrame paths).\n *\n * Operates on `Row[]`. The file-loading `run(source)` entry point lives in\n * `node/` (it needs `node:fs`); the edge-safe core only exposes the in-memory\n * `runDf` / `runStages` paths.\n *\n * Edge-safe: no `node:` imports.\n */\n\nimport type { PipeContext, PipelineConfig, PipeResult, Row, Stage } from \"./models.js\";\nimport { makePipeContext, makePipelineConfig, makeStageSpec, PipeStatus } from \"./models.js\";\nimport { StageRegistry } from \"./engine/registry.js\";\nimport { Resolver } from \"./engine/resolver.js\";\nimport { Runner } from \"./engine/runner.js\";\nimport { Reporter } from \"./engine/reporter.js\";\nimport { buildDefaultRegistry } from \"./adapters/index.js\";\nimport { getPipeWasmBackend } from \"./wasm/backend.js\";\nimport { autoConfigViaWasm } from \"./wasm/plannerJson.js\";\nimport { buildPlannerInput, enforceConfidence, planToConfig } from \"./autoconfigGlue.js\";\nimport { planPipeline, applyScaleHints } from \"./autoconfigPlanner.js\";\n\nconst DEFAULT_STAGE_ORDER = [\n \"goldencheck.scan\",\n \"goldenflow.transform\",\n \"goldenmatch.dedupe\",\n];\n\nexport interface PipelineOptions {\n config?: PipelineConfig | undefined;\n registry?: StageRegistry | undefined;\n identityOpts?: Record | undefined;\n}\n\nexport class Pipeline {\n private readonly config: PipelineConfig | undefined;\n private readonly registry: StageRegistry;\n private readonly identityOpts: Record;\n\n constructor(options?: PipelineOptions) {\n this.config = options?.config;\n // When the caller supplies a registry, use it as-is; otherwise build the\n // default suite registry (load + scan + transform + dedupe).\n this.registry = options?.registry ?? buildDefaultRegistry();\n this.identityOpts = options?.identityOpts ?? {};\n }\n\n /** Run the pipeline on an array of rows. */\n async run(rows: readonly Row[], source = \"\"): Promise {\n const ctx = makePipeContext({\n df: [...rows] as Row[],\n metadata: { source, input_rows: rows.length },\n });\n\n const config = this.config ?? planConfig([...rows], this.registry, this.identityOpts);\n\n let plan;\n try {\n plan = Resolver.resolve(config, this.registry);\n } catch (e) {\n const message = e instanceof Error ? e.message : String(e);\n return {\n status: PipeStatus.FAILED,\n source,\n inputRows: rows.length,\n stages: {},\n artifacts: {},\n skipped: [],\n errors: [`Pipeline resolution failed: ${message}`],\n reasoning: {},\n timing: {},\n };\n }\n\n const runner = new Runner(this.registry);\n const stages = await runner.run(plan, ctx);\n return Reporter.build(ctx, stages);\n }\n\n}\n\n/**\n * Build the default check→flow→dedupe config from the available stages,\n * optionally appending the identity stage. Conforms to goldenpipe-core\n * (config.rs `auto_config`): the identity stage is appended iff `identityOpts`\n * is non-empty AND the identity stage is registered, carrying the opts as its\n * config. Extracted as a standalone helper so both `Pipeline.run()` and the\n * cross-surface shim call the same code path.\n */\nexport function computeAutoConfigPure(\n registry: StageRegistry,\n identityOpts: Record,\n): PipelineConfig {\n const available = registry.listAll();\n const stages = DEFAULT_STAGE_ORDER.filter((name) => name in available).map((name) =>\n makeStageSpec(name),\n );\n const IDENTITY_STAGE = \"goldenmatch.identity_resolve\";\n if (Object.keys(identityOpts).length > 0 && IDENTITY_STAGE in available) {\n stages.push(makeStageSpec({ use: IDENTITY_STAGE, config: identityOpts }));\n }\n return makePipelineConfig({ pipeline: \"auto\", stages });\n}\n\n/**\n * planConfig — the brain path (pure-TS; mirrors Python _plan_config). Profiles\n * the rows, runs the rule table + scale hints, refuses if not confident at\n * scale, and materializes the chosen plan into a PipelineConfig.\n */\nexport function planConfig(\n rows: readonly Row[],\n registry: StageRegistry,\n identityOpts: Record,\n): PipelineConfig {\n const inp = buildPlannerInput(rows);\n let plan = planPipeline(inp);\n plan = applyScaleHints(plan, inp.runtime);\n enforceConfidence(plan, inp.runtime); // may throw PipeNotConfidentError\n return planToConfig(plan, registry.listAll(), identityOpts);\n}\n\n/**\n * Guarded public entry: routes through the registered WASM planner backend when\n * one is enabled, else runs the pure-TS core. `Pipeline.run()` calls this.\n */\nexport function computeAutoConfig(\n registry: StageRegistry,\n identityOpts: Record,\n): PipelineConfig {\n const b = getPipeWasmBackend();\n if (b) return autoConfigViaWasm(registry, identityOpts, b);\n return computeAutoConfigPure(registry, identityOpts);\n}\n\n/**\n * Run a pipeline on an array of rows. Zero-config (default suite chain) or with\n * an explicit PipelineConfig. Port of `_api.run_df`.\n */\nexport async function runDf(\n rows: readonly Row[],\n config?: PipelineConfig,\n source = \"\",\n): Promise {\n const pipe = new Pipeline(config !== undefined ? { config } : {});\n return pipe.run(rows, source);\n}\n\n/**\n * Run specific stages programmatically against rows. Port of `_api.run_stages`.\n * The auto-prepended `load` stage is removed since rows are already supplied.\n */\nexport async function runStages(stages: readonly Stage[], rows: readonly Row[]): Promise {\n const registry = new StageRegistry();\n for (const s of stages) registry.register(s);\n\n const config = makePipelineConfig({\n pipeline: \"programmatic\",\n stages: stages.map((s) => makeStageSpec(s.info.name)),\n });\n\n const ctx: PipeContext = makePipeContext({\n df: [...rows] as Row[],\n metadata: { source: \"\", input_rows: rows.length },\n });\n\n const plan = Resolver.resolve(config, registry);\n plan.stages = plan.stages.filter((s) => s.name !== \"load\");\n\n const runner = new Runner(registry);\n const results = await runner.run(plan, ctx);\n return Reporter.build(ctx, results);\n}\n","/**\n * Public opt-in WASM API for the goldenpipe planner kernel. enableWasm() is\n * async; after it resolves, the five planner seams consult the registered\n * backend instead of pure-TS. Pure-TS stays the default + fallback.\n */\nimport { enableWasmBackend, type EnableOptions } from \"goldenmatch-wasm-runtime\";\nimport { setPipeWasmBackend, getPipeWasmBackend } from \"./backend.js\";\n\nexport type { PipeWasmBackend } from \"./backend.js\";\nexport type EnableWasmOptions = EnableOptions;\n\nlet _enabled = false;\n\nexport async function enableWasm(opts: EnableWasmOptions = {}): Promise {\n if (_enabled) return true;\n try {\n const { instantiateBackend } = await import(\"./loader.js\");\n const ok = await enableWasmBackend(\n opts,\n instantiateBackend,\n setPipeWasmBackend,\n new URL(\"./artifacts/goldenpipe_wasm_bg.wasm\", import.meta.url),\n );\n if (ok) _enabled = true;\n return ok;\n } catch (err) {\n if (opts.require) throw err;\n return false;\n }\n}\n\nexport function isWasmEnabled(): boolean {\n return getPipeWasmBackend() !== null;\n}\n\nexport function disableWasm(): void {\n setPipeWasmBackend(null);\n _enabled = false;\n}\n"]}