#!/usr/bin/env bun /** * personal-daily-ops * Builds a prioritized daily operating plan using OpenAI. */ import { parseArgs } from "util"; import { existsSync, mkdirSync, appendFileSync, readFileSync } from "fs"; import { join, dirname, resolve } from "path"; type OutputFormat = "markdown" | "json"; type EnergyProfile = "morning peak" | "evening peak" | "balanced"; type WorkdayTemplate = { tasks?: Array>; meetings?: Array>; constraints?: Array>; rituals?: Array>; notes?: string; }; interface SkillOptions { workload: string; role?: string; day?: string; timezone?: string; workHours?: string; energy?: EnergyProfile; format: OutputFormat; model: string; output?: string; template?: WorkdayTemplate; } interface OpenAIChatResponse { choices?: Array<{ message?: { content?: string | null; }; }>; error?: { message?: string; }; } const SKILL_SLUG = "personal-daily-ops"; function ensureDir(path: string) { if (!existsSync(path)) { mkdirSync(path, { recursive: true }); } } function getPaths() { const sessionStamp = new Date().toISOString().replace(/[:.]/g, "_").replace(/-/g, "_"); const exportsRoot = process.env.SKILLS_EXPORTS_DIR || join(process.cwd(), ".skills", "exports"); const logsRoot = process.env.SKILLS_LOGS_DIR || join(process.cwd(), ".skills", "logs"); const skillExportsDir = join(exportsRoot, SKILL_SLUG); const skillLogsDir = join(logsRoot, SKILL_SLUG); ensureDir(skillExportsDir); ensureDir(skillLogsDir); return { sessionStamp, skillExportsDir, skillLogsDir, }; } function createLogger(logDir: string, sessionStamp: string) { const logFile = join(logDir, `log_${sessionStamp}.txt`); function write(level: "info" | "success" | "error", message: string) { const timestamp = new Date().toISOString(); const entry = `[${timestamp}] [${level.toUpperCase()}] ${message}\n`; appendFileSync(logFile, entry); const prefix = level === "success" ? "✅" : level === "error" ? "❌" : "â„šī¸"; console.log(`${prefix} ${message}`); } return { info: (message: string) => write("info", message), success: (message: string) => write("success", message), error: (message: string) => write("error", message), logFile, }; } function slugify(value: string): string { return value .toLowerCase() .replace(/[^a-z0-9]+/g, "-") .replace(/^-+|-+$/g, "") .slice(0, 40) || "daily-ops"; } function parseJsonTemplate(content: string): WorkdayTemplate | undefined { try { const data = JSON.parse(content); if (typeof data === "object" && data !== null) { return data as WorkdayTemplate; } } catch (error) { // fall through to treat as plain text } return undefined; } function showHelp(): void { console.log(` personal-daily-ops - Build a prioritized daily operating plan using AI Usage: skills run personal-daily-ops -- [options] skills run personal-daily-ops -- --text "" [options] Options: -h, --help Show this help message --text Inline tasks and priorities --role Your role (default: knowledge worker) --day Planning day (default: today) --timezone Timezone (default: UTC) --work-hours Work hours (default: 09:00-17:00) --energy Energy profile: morning peak | evening peak | balanced (default: balanced) --format Output format: markdown | json (default: markdown) --model OpenAI model (default: gpt-4o-mini) --output Custom output file path Output includes: - Daily summary - Prioritized schedule - Energy-matched blocks - Risk flags - Follow-up actions Examples: skills run personal-daily-ops -- ./tasks.txt --energy "morning peak" skills run personal-daily-ops -- --text "Code review, client call, planning" --role "Tech lead" Requirements: OPENAI_API_KEY environment variable must be set. `); } function parseOptions(): SkillOptions { const { values, positionals } = parseArgs({ args: Bun.argv.slice(2), options: { help: { type: "boolean", short: "h" }, text: { type: "string" }, role: { type: "string" }, day: { type: "string" }, timezone: { type: "string" }, "work-hours": { type: "string" }, energy: { type: "string" }, format: { type: "string", default: "markdown" }, model: { type: "string", default: "gpt-4o-mini" }, output: { type: "string" }, }, allowPositionals: true, }); if (values.help) { showHelp(); process.exit(0); } let workload = values.text || ""; let template: WorkdayTemplate | undefined; if (!workload && positionals[0]) { const filePath = resolve(positionals[0]); const content = readFileSync(filePath, "utf-8"); template = parseJsonTemplate(content); workload = template ? "" : content; } if (!workload.trim() && !template) { throw new Error("Provide tasks via file path, JSON template, or --text."); } const format: OutputFormat = values.format === "json" ? "json" : values.format === "markdown" ? "markdown" : "markdown"; let energy: EnergyProfile = "balanced"; if (values.energy === "morning peak" || values.energy === "evening peak" || values.energy === "balanced") { energy = values.energy; } return { workload, role: values.role, day: values.day, timezone: values.timezone, workHours: values["work-hours"], energy, format, model: values.model, output: values.output, template, }; } function buildPrompt(options: SkillOptions) { const system = `You are an elite chief of staff and productivity strategist. Design a realistic daily operating plan with timeboxing, focus blocks, and recovery periods. Respect role context, meetings, and constraints. Flag conflicts and suggest delegation if needed.`; const instructions = options.format === "json" ? "Respond in JSON with keys: summary, schedule, priorities, rituals, risks, follow_up. Schedule should be array with start, end, label, objective, energy_match, location, notes." : "Respond in polished Markdown. Start with a blockquote summary, include a table for schedule blocks, highlight top priorities, rituals, risk flags, and follow-up list."; const payload = { role: options.role || "knowledge worker", planning_day: options.day || "today", timezone: options.timezone || "UTC", work_hours: options.workHours || "09:00-17:00", energy_profile: options.energy || "balanced", workload_text: options.workload.substring(0, 6000), workload_template: options.template, }; const user = `${instructions}\n\n${JSON.stringify(payload, null, 2)}`; return { system, user }; } async function callOpenAI(options: SkillOptions, system: string, user: string): Promise { const apiKey = process.env.OPENAI_API_KEY; if (!apiKey) { throw new Error("OPENAI_API_KEY environment variable is required."); } const body = { model: options.model, messages: [ { role: "system", content: system }, { role: "user", content: user }, ], temperature: 0.4, max_tokens: options.format === "json" ? 2200 : 2000, }; const response = await fetch("https://api.openai.com/v1/chat/completions", { method: "POST", headers: { "Content-Type": "application/json", Authorization: `Bearer ${apiKey}`, }, body: JSON.stringify(body), }); const data: OpenAIChatResponse = await response.json(); if (!response.ok) { throw new Error(data.error?.message || `OpenAI API error (${response.status})`); } const content = data.choices?.[0]?.message?.content; if (!content) { throw new Error("OpenAI response did not include content."); } return content.trim(); } async function writeExport(path: string, content: string) { ensureDir(dirname(path)); await Bun.write(path, content); } function buildExportPath(skillExportsDir: string, sessionStamp: string, options: SkillOptions) { if (options.output) { return resolve(options.output); } const base = slugify(options.role ? `${options.role}-${options.day || "agenda"}` : sessionStamp); const extension = options.format === "json" ? "json" : "md"; return join(skillExportsDir, `${base}_${sessionStamp}.${extension}`); } function preview(content: string) { const lines = content.split(/\r?\n/).slice(0, 8); lines.forEach(line => console.log(` ${line}`)); if (content.split(/\r?\n/).length > 8) { console.log(" ..."); } } async function main() { const { sessionStamp, skillExportsDir, skillLogsDir } = getPaths(); const logger = createLogger(skillLogsDir, sessionStamp); try { const options = parseOptions(); logger.info("Parsed input options."); const { system, user } = buildPrompt(options); logger.info("Constructed planning prompt."); const content = await callOpenAI(options, system, user); logger.success("Received agenda from OpenAI."); const exportPath = buildExportPath(skillExportsDir, sessionStamp, options); await writeExport(exportPath, content); logger.success(`Saved agenda to ${exportPath}`); console.log("\nPreview:"); preview(content); } catch (error) { const message = error instanceof Error ? error.message : String(error); logger.error(message); process.exitCode = 1; } } main();