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Determines why a block of code was written and what the author was trying to achieve. Use when the user asks what code is for, why it was written, the purpose of a code block, or the intent behind code in any programming language.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Guides the user through designing and creating the complete memory architecture for an AI agent OR for a workspace — MEMORY.md index, tiered topical store, update triggers, retention policies, connectivity test, setup report. Supports THREE Step-0 modes — (1) workspace memory; (2) per-agent memory (requires IDENTITY.md / SOUL.md / RULES.md); (3) workspace-confirm-and-update for boilerplate-derived projects (validates an existing MEMORY.md + memories/ tree structurally, additively merges anything missing without overwriting pre-existing content, surfaces every conflict for user decision, then runs Steps 8–10 unchanged). Use to build, design, or finalise a MEMORY.md / memory architecture / memory backend. Also fires as Phase 4 of build-agent and as the body of configure-workspace-memories during boilerplate bootstrap.
Reorganizes an agent's memory/daily/ folder into a progressively nested hierarchy of monthly (YYYY-MM/) and quarterly (YYYY-QN/) sub-folders, each anchored by a synthesized summary README.md. Keeps the memory/daily/README.md entry point small and navigable regardless of how many session logs have accumulated. Trigger when: flat daily files for a completed calendar month are still at memory/daily/ root; monthly folders for a completed quarter are not yet folded into a YYYY-QN/ folder; the user says "reorganize daily logs", "archive last month's logs", "compact this agent's daily memory", or "reorganize daily logs for [agent-name]". Scoped to exactly one agent per run — always ask which agent if ambiguous. Hard dependency on format-md-for-progressive-disclosure. Does not touch MEMORY.md, topical stores, or any other agent's files.
Weekly memory maintenance orchestrator for a single agent. Audits all eight Tier 2 memory stores, detects cross-store contradictions, delegates daily-log reorganization to compact-agent-daily-memories and topical-store compaction to compact-agent-topical-memories, rebuilds the MEMORY.md index (enforcing the 4 KB limit), and produces a unified maintenance report. Use this skill — not the sub-skills directly — when full weekly maintenance is needed: MEMORY.md index approaching 4 KB, both daily and topical stores need attention, or the user says "run memory maintenance for [agent-name]", "compact this agent's memory", or "run weekly maintenance". Scoped to one agent per invocation.
Compacts the seven Tier 2 topical memory stores (decisions/, projects/, activities/, lessons/, preferences/, people/, analysis/) for one agent. Merges related entries, archives lifecycle-complete records, and prunes expired content — preserving all decision reasoning and never resolving contradictions autonomously. Use when: a topical store README.md exceeds 5 KB; a store sub-folder has 10+ files eligible for merge or archive; the user says "compact decisions", "archive completed projects", "merge lessons", "clean up the projects", or "reorganize memory stores". Also triggered by diagnostic phrases ("are there contradictions?", "show stale entries", "which projects are not archived?") — diagnostic mode audits without writing anything. Invoked by skill-compact-memories for weekly memory maintenance. Requires format-md-for-progressive-disclosure sub-skill. Scoped to one agent per run — always asks which agent if ambiguous. Does not touch memory/daily/.
Adds or updates MAGI-compliant YAML front matter in markdown files so AI agents and indexing tools can discover, classify, and retrieve documents without reading the full body. Use this skill whenever the user wants to add front matter to a markdown file, make a markdown file discoverable by AI agents, tag or describe a file for agentic retrieval, prepare documents for progressive discovery, or update existing front matter metadata. Also trigger when the user mentions "front matter", "MAGI", "progressive discovery metadata", or asks to enrich a .md file with structured metadata — even if they don't use those exact terms.
Captures pending action items into uniform, ID-addressable, durable records — one markdown file per action plus a shared INDEX.md — so they survive past the originating session and remain queryable across agents and humans. Use when the user types /record-action or /action-needed; says "record this action", "add this to the action list", "track this as a todo", "capture this as an action item", "we need to", "someone should", "action item:", "TODO:", or "follow-up:"; or when an agent such as chat-session-scribe invokes it programmatically at session-end. A MEMORY.md path is a hard prerequisite — the skill stops and asks for it if not provided. Performs ASK-BACK for missing owner and due-date. Auto-generates a UUIDv7 ID (time-sortable). Dual-writes to shared workspace memories (source of truth) and, when the caller is an FBO-framework agent, a local copy in the agent's own memories. Companion skill: record-action-taken resolves actions by ID.
| name | determine-code-purpose |
| description | Determines why a block of code was written and what the author was trying to achieve. Use when the user asks what code is for, why it was written, the purpose of a code block, or the intent behind code in any programming language. |
You are an expert software developer. Your task is to determine why a block of code was written and what the coder was trying to achieve.
Input: The user provides a block of code (and optionally additional context). Infer the programming language from the code or the user’s message.
Output: Your full response must be in markdown. Give only the answer to “why was this block of code written?” — do not include your step-by-step analysis in the response.
Before sending the response, confirm:
