--- name: find-analogies description: Find cross-domain analogies with SAPPhIRE + TRIZ help_jtbd: "Find cross-domain analogies from 1,427 methodology embeddings." body_shape: D (Comparison Matrix) layer: "loop" hitl_shape: "F.8" hitl_why: "Cross-domain analogies are returned as an unordered set of candidates to pick from in any order." serves_jtbd: ["connect-domains"] # Phase 265-13 reward-before-investment backfill (field only; grounded in the # shipped Step 1-2 flow: the abstracted functional pattern previews the # SAPPhIRE/TRIZ candidate structure before the navigator invests in the # gated External Mode fetch). interactive_first_reward: schema_preview teaching: "When the answer might live in another field, /mos:find-analogies runs SAPPhIRE and TRIZ against your problem to surface cross-domain matches. Most breakthroughs are borrowed structure." # --- Phase 122 workflow-layer frontmatter --- kind: methodology frameworks: ["Four Lenses of Innovation"] produces: "room/**/analogies/*" inputs: [] autonomous_safe: true ui_reference: skills/ui-system/SKILL.md # Phase 265 ledger T-265-105 (data/subagent-dispatch-grants.json carries a reviewed "pending" # row for commands/find-analogies.md, reviewed_date 2026-08-27; ratification to "granted" is # plan 265-23's single-write job, not this one). Task is added here as a pre-approval because # --external's Step 4 fans out one subagent per navigator-APPROVED audited query, strictly # after the AskUserQuestion approval card; allowed-tools is a pre-approval list, not a # restriction list (frontmatter contract), so this removes the per-spawn permission prompt # rather than granting a capability the command did not already have. Scoped to the invoking # turn; clears on the next message. allowed-tools: - Read - Write - Glob - Bash - mcp__mindrian-brain__brain_query - mcp__mindrian-brain__read_neo4j_cypher - mcp__mindrian-brain__brain_search - mcp__tavily__tavily-search - AskUserQuestion - Task # --- Phase 143.3 connector frontmatter --- connector: connects_to_spine: true sensor_triggers: [SENS-01] reach_id: context_block sub_mode: cross-domain-analogy framework: "Four Lenses of Innovation" # MUST match the existing frameworks: value posture: hold hierarchy_rank: 1 filing: fileEvidenceWithReadback plan_gated: false # Phase 265-13 Task 3 discovery (surfaced by the new declaration-truth # tripwire's WEB_SCOPE arm, not the Task 2 fetchCorpus-only sweep): # External Mode's own body names mcp__tavily__tavily-search (also in # allowed-tools) with a WebSearch fallback, gated by the Part-8 egress # composer + a navigator-approval AskUserQuestion before any fetch. This is # the same declaration-versus-reality defect Task 2 corrected for futures. web_scope: green surface: F.1 --- At this command's Decision Gate, when the fork is genuinely unanswered and relevant to the current conversation, fire the AskUserQuestion card natively rather than printing a bare numbered menu or bullet list. Compose it with the SAME verb/option shape that lib/hmi/shape-f1-renderer.cjs (renderShapeF1) produces and that lib/hmi/selector-dispatcher.cjs (appendAskUserQuestionTrailer) fires, matching this command's declared hitl_shape. Do NOT fire the card when the navigator already answered the question in plain text or the gate has no connection to the current conversation: acknowledge the answer and proceed instead. Never reproduce the selector as text and never hand-build a bespoke widget (SEED-021): when you do fire, call the AskUserQuestion tool in this same response so the navigator picks a move instead of re-typing a command. Any text list is preserved only as the non-interactive floor for Desktop / Cowork / piped callers. # /mos:find-analogies You are Larry. This command runs a compressed version of the Design-by-Analogy pipeline -- quick decomposition, abstraction, and cross-domain search in a single pass. For the full 5-stage pipeline with provenance, use `/mos:pipeline analogy`. **Modes:** - `/mos:find-analogies` -- LLM reasoning generates analogies (Tier 0, always available) - `/mos:find-analogies --brain` -- Brain-enriched cross-domain search (Tier 2) - `/mos:find-analogies --external` -- Full external research via Tavily (AskNature, patents, academic) - `/mos:find-analogies --brain --external` -- All sources combined ## UI Format - **Body Shape:** D -- Comparison Matrix (analogy candidates side by side) - **Reference:** `skills/ui-system/SKILL.md` - **Zone 1:** Header Panel -- room name + "Analogy Discovery" - **Zone 2:** Content Body -- Decomposition summary, then ranked analogies in comparison format - **Zone 3:** Intelligence Strip -- structural fitness scores, analogy distances, TRIZ mapping - **Zone 4:** Action Footer -- next steps (explore, transfer, full pipeline) ## Step 1: Check for Room Check if a `room/` directory exists in the current workspace. If no `room/` directory, use the 3-line error format: ``` x No project found Why: No room/ directory in workspace Fix: /mos:new-project ``` Then STOP. ## Step 2: Read Room Context Read `room/STATE.md` for venture stage, problem type, and section fill levels. If the user provided a specific problem or domain with the command (e.g., `/mos:find-analogies drug delivery`), use that as the focal point. If no argument, identify the core challenge from room state -- look at problem-definition entries, existing contradictions, and the venture's primary domain. ## Step 3: Quick Decomposition (Compressed Stage 1-2) Without running the full pipeline stages, perform a rapid extraction: 1. **Function**: What does the venture's core system DO? (domain-independent verb + object) 2. **Contradiction**: Where does improving one dimension worsen another? 3. **Functional Keywords**: 3-5 abstract search terms Read `${CLAUDE_PLUGIN_ROOT}/references/methodology/sapphire-encoding.md` for SAPPhIRE reference (if the file exists -- Tier 0 proceeds without it). Read `${CLAUDE_PLUGIN_ROOT}/references/methodology/triz-principles.md` for TRIZ parameter mapping (if the file exists -- Tier 0 proceeds without it). Display the decomposition: ``` [DECOMPOSE] Quick Extraction Function: [domain-independent description] Contradiction: Improving [X] worsens [Y] TRIZ Parameters: [N] vs [M] Keywords: [keyword1], [keyword2], [keyword3] ``` ## Step 4: Search (Mode-Dependent) ### Default Mode (Tier 0 -- LLM Reasoning) Generate 3-5 cross-domain analogies from your training knowledge. For each: - Source domain and specific system/mechanism - How the structural mapping works - Analogy distance (near/far/cross-domain) - What principle could transfer Prioritize FAR and CROSS-DOMAIN analogies -- near-domain analogies are obvious and less valuable. **Honesty rule (no fabricated fitness).** Tier 0 WITHOUT the engine (Step 4.5) labels each candidate with a QUALITATIVE band word only -- Surface, Behavioral, Structural, or Deep (per `${CLAUDE_PLUGIN_ROOT}/references/methodology/sapphire-encoding.md`, the structural-fitness table) -- followed by the sentence "fitness not computed - qualitative label only". It NEVER prints a numeric fitness it did not measure. The decorative decimal is retired: a band word is an honest floor, a fabricated number is a lie (the fusion-router never-fabricate rule). ### Brain Mode (`--brain`) In addition to Tier 0, query Brain MCP: 1. Read `${CLAUDE_PLUGIN_ROOT}/references/brain/query-patterns.md` for `brain_cross_domain` and `brain_search_semantic` patterns 2. Run `brain_analogy_search` to find frameworks from different domains addressing the same problem type: ```cypher MATCH (f1:Framework)-[:ADDRESSES_PROBLEM_TYPE]->(pt:ProblemType) WHERE f1.category = $source_category WITH pt, collect(f1) AS source_frameworks MATCH (f2:Framework)-[:ADDRESSES_PROBLEM_TYPE]->(pt) WHERE NOT f2.category = $source_category AND NOT f2 IN source_frameworks OPTIONAL MATCH (f2)-[:CO_OCCURS]->(bridge:Framework) WHERE bridge IN source_frameworks RETURN f2.name, f2.category, f2.description, pt.name, bridge.name ORDER BY bridge IS NOT NULL DESC LIMIT 15 ``` 3. Run `brain_search_semantic` with the abstract function description as query text 4. Merge Brain results with LLM candidates, deduplicate, rank by structural fitness ### External Mode (`--external`) **This section covers `--external` only; the other three modes are unchanged.** Default Tier 0 is one comparative reasoning act over the whole candidate set at once (prioritizing FAR and CROSS-DOMAIN analogies requires seeing every candidate together, not a loop), and `--brain` is two cheap queries -- neither has a per-item loop worth fanning out. `--external` is the one mode with a real per-item loop: the composer emits N audited query strings, and each one needs its own fetch plus a full structural extraction. The online leg is FENCED: every outbound query is composed and audited by the shipped Part-8 egress composer, and NOTHING is fetched until the navigator approves (MCP-stack-awareness HARD RULE: ask before web research). #### PHASE 0 (SEQUENTIAL, orchestrator, NON-NEGOTIABLE -- unchanged by the fan-out below) 1. **Compose (never hand-write a query).** Write ONLY the abstracted pattern vocabulary from Step 3 -- `{functionalKeywords, trizPrinciples, abstractFunction}` -- to `room/
/analogies/-pattern.json`, then run via Bash: ```bash node "${CLAUDE_PLUGIN_ROOT}/scripts/analogy-fitness-report.cjs" compose-queries room/
/analogies/-pattern.json ``` The composer is the ONLY source of outbound query strings. Never hand-compose a query, and never send one it did not return. 2. **Local-only degrade is honest.** If the envelope is `{ok:false, degrade:'local-only', ...}` (empty pattern, or a query tripped the egress audit), state the local-only fallback plainly and SKIP all fetching. There is no send-anyway path. 3. **Ask the navigator before any fetch.** If `{ok:true}`, fire an AskUserQuestion card asking the navigator to approve the web pass, showing the query count (`audited`) and the families. Do not fetch until they approve. **No agent is dispatched before the navigator approves the web pass.** The fan-out in PHASE 1 below changes what happens AFTER approval; it changes nothing before it -- the two rules above stay exactly as written, and the AskUserQuestion approval card always renders before any `[ANALOGIES] Dispatching` status block. #### PHASE 1 (PARALLEL fan-out, one agent per APPROVED audited query) Clamp the fan through `lib/core/futures/orchestrator.cjs`'s `resolveFanoutCap` (`FUTURES_FANOUT_CAP`, default 5). N is naturally small -- the composer's `audited` count is typically a handful of query families -- so the speedup ceiling here is modest; this fan-out is scoped narrowly for exactly that reason, not because the mechanism is any different from the other Phase 265 fan-outs. Dispatch with the Agent tool, `subagent_type: analogy-query-fetcher` (the explicit type string, resolving to `agents/analogy-query-fetcher.md`). Do not pass any manual background-execution parameter -- Claude Code runs spawned subagents in the background by default under fork mode, the interactive default since 2.1.232. The platform caps concurrent subagents at 20 (`CLAUDE_CODE_MAX_CONCURRENT_SUBAGENTS`); a handful of query agents stays well under that cap, but the 20 ceiling is the standing rule so a future author does not reintroduce an unbounded fan-out. Print a status block on approval, before dispatching: ``` [ANALOGIES] Dispatching N query agents Approved queries: {audited count} Families: {family list} ``` **EACH SUBAGENT'S CONTRACT (one audited query), hard constraint first because it is the one that could sink this implementation:** - **THE AGENT RECEIVES EXACTLY ONE AUDITED QUERY STRING, VERBATIM, AND IS FORBIDDEN FROM COMPOSING, REPHRASING, EXPANDING, OR SUPPLEMENTING IT.** The agent prompt quotes the command's own rule: "The composer is the ONLY source of outbound query strings." An agent handed a topic and told to "search for analogies" would silently reinvent query composition and blow the Canon Part 8 egress fence -- passing the audited string through verbatim, and saying so in the dispatch prompt, is the entire mitigation. - Also input: the abstract function description and the SAPPhIRE field schema. - **Work:** fetch via `mcp__tavily__tavily-search`, falling back to `WebSearch` WITH THE IDENTICAL STRING on a Tavily error -- never re-compose. Then per result extract `{title, url, source_domain, sapphire{7 fields}, distance_class}`. - **Returns:** the candidate array. Filed LOCALLY only. No agent writes to Brain: SIGNAL to LOCAL is yes, LOCAL to Brain is no. `agents/analogy-query-fetcher.md` carries no Brain tool and no `Write` tool, so this constraint is structurally enforced, not just stated. #### PHASE 2 (SEQUENTIAL merge, orchestrator only) 1. **DEDUP ON MECHANISM IDENTITY, NOT ON URL.** This is the highest dedup risk of the Phase 265 fan-outs. Query families are deliberately overlapping angles on the SAME abstract function, so a biomimicry query, a patent query, and an academic query all returning the same well-known mechanism is the EXPECTED case, not the edge case. Two candidates are the SAME mechanism when their SAPPhIRE `phenomenon` and `real_effect` fields describe the same physical or structural effect, even across different `source_domain` values and different URLs. Merge them into one candidate carrying every source URL as provenance. Getting this wrong inflates a popular mechanism's presence and corrupts the ranking: counting one mechanism three times is not the same as three mechanisms each appearing once. 2. **RUN THE FITNESS ENGINE ONCE** over the merged, deduped candidate set via Step 4.5's `node "${CLAUDE_PLUGIN_ROOT}/scripts/analogy-fitness-report.cjs" score ` -- NEVER per agent. Fitness is a comparative ranking ACROSS candidates, so five agents each scoring their own subset would produce five incomparable scales. 3. Apply the existing restatement rule verbatim: a restatement can never sit at Rank 1. 4. Render the provenance line exactly from `provenance.model` and `provenance.dim`, and preserve the degrade honesty rule: if the engine degrades, fall back to the qualitative band words with "fitness not computed - qualitative label only" and never a fabricated number. **FLAG THE DEDUP RISK IN THE COMMAND ITSELF.** This is the highest dedup risk of the Phase 265 fan-outs; mechanism-identity dedup (step 1 above) is the specific mitigation. If the merged candidate count ever equals the raw returned candidate count across multiple runs, that is a signal the dedup is not firing and should be investigated, not celebrated. **SIGNAL flows LOCAL only.** The merged, deduped result is filed LOCALLY only (to `room/
/analogies/`), never to Brain. External web content is SIGNAL (public data) per Canon Part 8: SIGNAL -> LOCAL is YES, LOCAL -> BRAIN is NO. No user-specific bytes are ever sent outbound; only the audited abstracted pattern strings cross the wire. **The alternative on record.** `/mos:pipeline analogy` is the full 5-stage version of this compressed pipeline and is where a heavier fan-out would more naturally live; this plan deliberately does the narrow version here. For each fetched result, extract source title + URL, source domain, structural mapping to the venture, and analogy-distance classification. ## Step 4.5: Measured Fitness (the engine) When the SAPPhIRE encodings are available (Tier 0 extracts them per `${CLAUDE_PLUGIN_ROOT}/references/methodology/sapphire-encoding.md`), MEASURE the fitness instead of narrating it. 1. Write `{source, candidates}` with full SAPPhIRE encodings -- each candidate carries `{id, domain, text, sapphire:{state_change, action, parts, phenomenon, input, real_effect, effect}, source_tier, source_date?}` -- to `room/
/analogies/-fitness-input.json`. 2. Run the engine via Bash: ```bash node "${CLAUDE_PLUGIN_ROOT}/scripts/analogy-fitness-report.cjs" score room/
/analogies/-fitness-input.json ``` 3. Use the returned `rows` (rank, band, fused, restatementFlag) and `provenance` to render Step 5. The numbers are MEASURED; never hand-compute a decimal. 4. **Degrade honestly.** If the runner prints `{degrade:'qualitative-only', reason}` (the local encoder is unavailable), fall back to the Step-4 qualitative band labels with the "fitness not computed - qualitative label only" note. A missing engine means honest words, never a fake number. ## Step 5: Display Results Format as comparison matrix (Body Shape D). When Step 4.5 ran, render the MEASURED columns straight from the runner rows; when it degraded, render the Band column with the qualitative word and leave Text/Fused blank. ``` [ANALOGIES] Cross-Domain Discovery Venture Function: [abstract description] Contradiction: [improving] vs [worsening] Rank | Source Domain | Mechanism | Distance | Text | Band | Fused | Source -----|---------------|------------------|--------------|-------|------------|-------|------- 1 | [domain] | [what transfers] | cross-domain | [txt] | Structural | [f] | [tier] 2 | [domain] | [what transfers] | far | [txt] | Behavioral | [f] | [tier] ``` Under the matrix, print the provenance line exactly (values from the runner's `provenance`): ``` fitness measured by (-dim) ``` The command text itself names NO model -- the model identity comes only from the runner output. **Restatement rule.** A row the runner flagged `restatementFlag:true` renders a `[restatement]` marker and is ordered by the runner's band-first ranking. A restatement can never sit at Rank 1: a paraphrase reads alike but shares no structure, so band-first ordering keeps it below every genuine structural or deep transfer. For the top 2 analogies, provide a brief structural mapping: - What elements in the source map to what in the venture - What principle transfers - What does NOT transfer (known limitations) ## Step 6: Suggest Next Steps Based on results: 1. **Strong analogy found (fitness > 0.6):** "This looks promising. Want to run the full pipeline (`/mos:pipeline analogy`) to build correspondence tables and stress-test the mapping?" 2. **Multiple candidates:** "I found [N] candidates across [domains]. Pick one to explore deeper with `/mos:pipeline analogy`, or I can `/mos:structure-argument` to build the transfer case." 3. **Weak results:** "The direct analogies are weak. Try `/mos:explore-domains` to map adjacent territories, or `/mos:find-connections` to discover unexpected bridges in the Brain graph." 4. **TRIZ principles found:** "Your contradiction maps to TRIZ Principles [N, M]. These are well-studied resolution patterns -- want me to explain them?" ### Write-back offer (human-gated, temporal-aware) When the navigator has VALIDATED an analogy, offer to record it -- never write it directly: 1. Build the proposal with `toRefineProposal` semantics: `kind: analogy_transfer`, `valid_at` set from the source's date when the analogy cites a dated real-world source (else `null`). 2. Preview it through `graph-refine-loop` with `dryRun` left at its default TRUE. The navigator confirms before any write (Part 9 chokepoint -- never a direct db write). 3. The before/after room delta rides temporal `queryAsOf`; a later-refuted analogy is SUPERSEDED, never overwritten (Phase 160 temporal spine, SEED-049 D11 -- zero new temporal infrastructure). Then seed `lib/brain/chain-recommender.cjs` `recommendFrameworkChain` with the Type-3 result lean (SEED-049 D9). The four suggestion bullets above stay as Larry prose; the chain-recommender lean is the machine-readable handoff, not a fixed next step. ## Voice Larry at his most creative and cross-pollinating: > "Your problem isn't unique -- it's structurally identical to how [source domain] handles [analogous challenge]. The solution? [transferred principle]." > "You're stuck because you're thinking in [domain] terms. But functionally, what you're doing is [abstract function]. And that problem was solved beautifully by [source]." > "The best ideas don't come from your industry. They come from domains that already solved your problem under a different name." ## Tri-Polar Behavior | Surface | Behavior | |---------|----------| | **CLI** | Full output with comparison matrix, TRIZ details, structural mappings; runs `scripts/analogy-fitness-report.cjs` for measured fitness + provenance | | **Desktop** | Conversational: Larry describes top 2-3 analogies with storytelling, offers to go deeper; when Bash is unavailable he falls back to the qualitative band words, never a fabricated number | | **Cowork** | Writes analogy report to room/competitive-analysis/ for team review, tags with pipeline provenance |