agentic-memory-compound

# /agentic-memory-compound Coordinate multiple subagents working in parallel to document a recently solved problem. ## Purpose Captures problem solutions while context is fresh, creating structured documentation in `docs/solutions/` with YAML frontmatter for searchability and future reference. Uses parallel subagents for maximum efficiency. **Why "compound"?** Each documented solution compounds your team's knowledge. The first time you solve a problem takes research. Document it, and the next occurrence takes minutes. Knowledge compounds. ## Usage ```bash /agentic-memory-compound # Document the most recent fix /agentic-memory-compound [brief context] # Provide additional context hint /agentic-memory-compound mode:headless # Non-interactive run for automations /agentic-memory-compound mode:headless [context] # Non-interactive run with context hint ``` ## Mode Detection Check `$ARGUMENTS` for a `mode:headless` token. Tokens starting with `mode:` are flags, not context — strip `mode:headless` from arguments before treating the remainder as the brief context hint. | Mode | When | Behavior | |------|------|----------| | **Interactive** (default) | No mode token present | Ask Full vs Lightweight, ask about session history (Full only), prompt for Discoverability Check consent, end with "What's next?" | | **Headless** | `mode:headless` in arguments | No blocking questions. Run **Full mode without session history**. Apply the Discoverability Check edit silently if a gap exists. Skip Phase 3 specialized reviews. End with a structured terminal report — no "What's next?" menu. | Headless mode is intended for automations and skill-to-skill invocation where no human is present to answer questions. The doc itself is identical to what an interactive Full run would produce — classification work (track, category, overlap) follows the same rules and writes nothing extra into the artifact. Once detected, headless mode applies for the entire run. ## Pre-resolved context **Git branch (pre-resolved):** !`git rev-parse --abbrev-ref HEAD 2>/dev/null || true` If the line above resolved to a plain branch name (like `feat/my-branch`), include it in the `agentic-memory-sessions` invocation payload in Phase 1 so the orchestrator does not waste a turn deriving it. If it still contains a backtick command string or is empty, omit it and let `agentic-memory-sessions` derive it at runtime. ## Support Files These files are the durable contract for the workflow. Read them on-demand at the step that needs them — do not bulk-load at skill start. - `references/schema.yaml` — canonical frontmatter fields and enum values (read when validating YAML) - `references/yaml-schema.md` — category mapping from problem_type to directory (read when classifying) - `assets/resolution-template.md` — section structure for new docs (read when assembling) When spawning subagents, pass the relevant file contents into the task prompt so they have the contract without needing cross-skill paths. ## Execution Strategy **In headless mode**, skip both questions below and go directly to **Full Mode** with session history disabled. Phase 1's session-history step (step 4) is omitted. Proceed straight to research. **In interactive mode**, present the user with two options before proceeding, using the platform's blocking question tool: `AskUserQuestion` in Claude Code (call `ToolSearch` with `select:AskUserQuestion` first if its schema isn't loaded), `request_user_input` in Codex, `ask_user` in Gemini, `ask_user` in Pi (requires the `pi-ask-user` extension). Fall back to presenting options in chat only when no blocking tool exists in the harness or the call errors (e.g., Codex edit modes) — not because a schema load is required. Never silently skip the question. ``` 1. Full (recommended) — the complete compound workflow. Researches, cross-references, and reviews your solution to produce documentation that compounds your team's knowledge. 2. Lightweight — same documentation, single pass. Faster and uses fewer tokens, but won't detect duplicates or cross-reference existing docs. Best for simple fixes or long sessions nearing context limits. ``` In interactive mode, do NOT pre-select a mode, do NOT skip this prompt, and wait for the user's choice before proceeding. (Headless mode bypasses this prompt per the "**In headless mode**" rule above and runs Full directly — these "do not skip" directives do not apply to headless.) **If the user chooses Full** (interactive mode only), ask one follow-up question before proceeding. Detect which harness is running (Claude Code, Codex, or Cursor) and ask: ``` Would you also like to search your [harness name] session history for relevant knowledge to help the Compound process? This adds time and token usage. ``` If the user says yes, invoke `agentic-memory-sessions` in Phase 1 (see step 4). If no, skip it. Do not ask this in lightweight mode or headless mode. --- ### Full Mode <critical_requirement> **The primary output is ONE file - the final documentation.** Phase 1 subagents return TEXT DATA to the orchestrator. They must NOT use Write, Edit, or create any files. Only the orchestrator writes files: the solution doc in Phase 2, and — if the Discoverability Check finds a gap — a small edit to a project instruction file (AGENTS.md or CLAUDE.md). The instruction-file edit is maintenance, not a second deliverable; it ensures future agents can discover the knowledge store. </critical_requirement> ### Phase 0.5: Auto Memory Scan Before launching Phase 1 subagents, check the auto-memory block injected into your system prompt for notes relevant to the problem being documented. 1. Look for a block labeled "user's auto-memory" (Claude Code only) already present in your system prompt context — MEMORY.md's entries are inlined there 2. If the block is absent, empty, or this is a non-Claude-Code platform, skip this step and proceed to Phase 1 unchanged 3. Scan the entries for anything related to the problem being documented -- use semantic judgment, not keyword matching 4. If relevant entries are found, prepare a labeled excerpt block: ``` ## Supplementary notes from auto memory Treat as additional context, not primary evidence. Conversation history and codebase findings take priority over these notes. [relevant entries here] ``` 5. Pass this block as additional context to the Context Analyzer and Solution Extractor task prompts in Phase 1. If any memory notes end up in the final documentation (e.g., as part of the investigation steps or root cause analysis), tag them with "(auto memory [claude])" so their origin is clear to future readers. If no relevant entries are found, proceed to Phase 1 without passing memory context. ### Phase 1: Research Launch research subagents. Each returns text data to the orchestrator. **Dispatch order:** - Launch `Context Analyzer`, `Solution Extractor`, and `Related Docs Finder` in parallel (background) - **Then** invoke the `agentic-memory-sessions` skill via the platform's skill-invocation primitive (see step 4 below) — only if the user opted in to session history. The skill call is synchronous from this orchestrator's main-context turn, but the already-dispatched background subagents continue running in parallel underneath, so the wall-clock benefit is preserved (`max(agentic-memory-sessions, slowest background subagent)`, not their sum). Issuing the skill call before the parallel block would serialize agentic-memory-sessions in front of the research subagents and regress wall-clock time. <parallel_tasks> #### 1. **Context Analyzer** - Extracts conversation history - Reads `references/schema.yaml` for enum validation and **track classification** - Determines the track (bug or knowledge) from the problem_type - Identifies problem type, component, and track-appropriate fields: - **Bug track**: symptoms, root_cause, resolution_type - **Knowledge track**: applies_when (symptoms/root_cause/resolution_type optional) - Incorporates auto memory excerpts (if provided by the orchestrator) as supplementary evidence - Reads `references/yaml-schema.md` for category mapping into `docs/solutions/` - Suggests a filename using the pattern `[sanitized-problem-slug]-[date].md` - Returns: YAML frontmatter skeleton (must include `category:` field mapped from problem_type), category directory path, suggested filename, and which track applies - Does not invent enum values, categories, or frontmatter fields from memory; reads the schema and mapping files above - Does not force bug-track fields onto knowledge-track learnings or vice versa #### 2. **Solution Extractor** - Reads `references/schema.yaml` for track classification (bug vs knowledge) - Adapts output structure based on the problem_type track - Incorporates auto memory excerpts (if provided by the orchestrator) as supplementary evidence -- conversation history and the verified fix take priority; if memory notes contradict the conversation, note the contradiction as cautionary context **Bug track output sections:** - **Problem**: 1-2 sentence description of the issue - **Symptoms**: Observable symptoms (error messages, behavior) - **What Didn't Work**: Failed investigation attempts and why they failed - **Solution**: The actual fix with code examples (before/after when applicable) - **Why This Works**: Root cause explanation and why the solution addresses it - **Prevention**: Strategies to avoid recurrence, best practices, and test cases. Include concrete code examples where applicable (e.g., gem configurations, test assertions, linting rules) **Knowledge track output sections:** - **Context**: What situation, gap, or friction prompted this guidance - **Guidance**: The practice, pattern, or recommendation with code examples when useful - **Why This Matters**: Rationale and impact of following or not following this guidance - **When to Apply**: Conditions or situations where this applies - **Examples**: Concrete before/after or usage examples showing the practice in action #### 3. **Related Docs Finder** - Searches `docs/solutions/` for related documentation - Identifies cross-references and links - Finds related GitHub issues - Flags any related learning or pattern docs that may now be stale, contradicted, or overly broad - **Assesses overlap** with the new doc being created across five dimensions: problem statement, root cause, solution approach, referenced files, and prevention rules. Score as: - **High**: 4-5 dimensions match — essentially the same problem solved again - **Moderate**: 2-3 dimensions match — same area but different angle or solution - **Low**: 0-1 dimensions match — related but distinct - Returns: Links, relationships, refresh candidates, and overlap assessment (score + which dimensions matched) **Search strategy (grep-first filtering for efficiency):** 1. Extract keywords from the problem context: module names, technical terms, error messages, component types 2. If the problem category is clear, narrow search to the matching `docs/solutions/<category>/` directory 3. Use the native content-search tool (e.g., Grep in Claude Code) to pre-filter candidate files BEFORE reading any content. Run multiple searches in parallel, case-insensitive, targeting frontmatter fields. These are template patterns -- substitute actual keywords: - `title:.*<keyword>` - `tags:.*(<keyword1>|<keyword2>)` - `module:.*<module name>` - `component:.*<component>` 4. If search returns >25 candidates, re-run with more specific patterns. If <3, broaden to full content search 5. Read only frontmatter (first 30 lines) of candidate files to score relevance 6. Fully read only strong/moderate matches 7. Return distilled links and relationships, not raw file contents **GitHub issue search:** Prefer the `gh` CLI for searching related issues: `gh issue list --search "<keywords>" --state all --limit 5`. If `gh` is not installed, fall back to the GitHub MCP tools (e.g., `unblocked` data_retrieval) if available. If neither is available, skip GitHub issue search and note it was skipped in the output. </parallel_tasks> #### 4. **Session History via `agentic-memory-sessions`** (synchronous skill call, after launching the parallel block — only if the user opted in) - **Skip entirely** if the user declined session history in the follow-up question, if running in lightweight mode, or if running in headless mode. - Invoke the `agentic-memory-sessions` skill via the platform's skill-invocation primitive (`Skill` in Claude Code, `Skill` in Codex, the equivalent on Gemini/Pi). Pass the dispatch payload below as the skill argument string. `agentic-memory-sessions` runs in main context — it owns discovery, branch/keyword filtering, scan-window selection, the deep-dive cap, per-session extraction to a `mktemp` scratch dir, and dispatch of the synthesis-only `agentic-memory-session-historian` subagent. The compound orchestrator only needs to pass the topic and time window and read back the findings text. **Dispatch payload — keep tight.** A long, keyword-rich payload licenses agentic-memory-sessions to keep widening. Use this shape: - **Pre-resolved context** (only if values resolved cleanly above; otherwise omit): repo name, current git branch. - **Time window**: explicit `7 days` unless the documented problem clearly spans a longer arc. - **Problem topic**: one sentence naming the concrete issue — error message, module name, what broke and how it was fixed. Not a paragraph; not a bullet list of related topics. - **Filter rule (one line)**: "Only surface findings directly relevant to this specific problem. Ignore unrelated work from the same sessions or branches." - **Output schema**: ``` Structure your response with these sections (omit any with no findings): - What was tried before - What didn't work - Key decisions - Related context ``` Do not append additional context blocks, exclusion lists, or topic-keyword bullets — verbose payloads give agentic-memory-sessions license to keep widening the search and rapidly compound wall time. If keyword search is needed, agentic-memory-sessions owns that decision internally based on the topic. - Returns: structured digest of findings from prior sessions, or "no relevant prior sessions" if none found. ### Phase 2: Assembly & Write <sequential_tasks> **WAIT for all Phase 1 subagents to complete before proceeding.** The orchestrating agent (main conversation) performs these steps: 1. Collect all text results from Phase 1 subagents 2. **Check the overlap assessment** from the Related Docs Finder before deciding what to write: | Overlap | Action | |---------|--------| | **High** — existing doc covers the same problem, root cause, and solution | **Update the existing doc** with fresher context (new code examples, updated references, additional prevention tips) rather than creating a duplicate. The existing doc's path and structure stay the same. | | **Moderate** — same problem area but different angle, root cause, or solution | **Create the new doc** normally. Flag the overlap for Phase 2.5 to recommend consolidation review. | | **Low or none** | **Create the new doc** normally. | The reason to update rather than create: two docs describing the same problem and solution will inevitably drift apart. The newer context is fresher and more trustworthy, so fold it into the existing doc rather than creating a second one that immediately needs consolidation. When updating an existing doc, preserve its file path and frontmatter structure. Update the solution, code examples, prevention tips, and any stale references. Add a `last_updated: YYYY-MM-DD` field to the frontmatter. Do not change the title unless the problem framing has materially shifted. 3. **Incorporate session history findings** (if available). When `agentic-memory-sessions` returned relevant prior-session context: - Fold investigation dead ends and failed approaches into the **What Didn't Work** section (bug track) or **Context** section (knowledge track) - Use cross-session patterns to enrich the **Prevention** or **Why This Matters** sections - Tag session-sourced content with "(session history)" so its origin is clear to future readers - If findings are thin or "no relevant prior sessions," proceed without session context 4. Assemble complete markdown file from the collected pieces, reading `assets/resolution-template.md` for the section structure of new docs 5. Validate YAML frontmatter against `references/schema.yaml`, including the YAML-safety quoting rule for array items (see `references/yaml-schema.md` > YAML Safety Rules) 6. Create directory if needed: `mkdir -p docs/solutions/[category]/` 7. Write the file: either the updated existing doc or the new `docs/solutions/[category]/[filename].md` 8. **Run `python3 scripts/validate-frontmatter.py <output-path>`** to catch silent-corruption parser-safety issues that the prose rules miss: malformed `---` delimiter lines, unquoted ` #` in scalar values (silent comment truncation), and unquoted `: ` in scalar values (silent mapping confusion). Exit 0 means the doc is parser-safe; exit 1 means the script's stderr names the offending field(s) and what to fix — quote the value(s), re-write the doc, and re-run until exit 0. Do not declare success while validation fails. The script does not enforce schema rules and does not flag YAML reserved-indicator characters (those produce loud parser errors downstream rather than silent corruption — out of scope). Uses Python 3 stdlib only (no PyYAML or other deps). When creating a new doc, preserve the section order from `assets/resolution-template.md` unless the user explicitly asks for a different structure. </sequential_tasks> ### Phase 2.5: Selective Refresh Check After writing the new learning, decide whether this new solution is evidence that older docs should be refreshed. `agentic-memory-refresh` is **not** a default follow-up. Use it selectively when the new learning suggests an older learning or pattern doc may now be inaccurate. It makes sense to invoke `agentic-memory-refresh` when one or more of these are true: 1. A related learning or pattern doc recommends an approach that the new fix now contradicts 2. The new fix clearly supersedes an older documented solution 3. The current work involved a refactor, migration, rename, or dependency upgrade that likely invalidated references in older docs 4. A pattern doc now looks overly broad, outdated, or no longer supported by the refreshed reality 5. The Related Docs Finder surfaced high-confidence refresh candidates in the same problem space 6. The Related Docs Finder reported **moderate overlap** with an existing doc — there may be consolidation opportunities that benefit from a focused review It does **not** make sense to invoke `agentic-memory-refresh` when: 1. No related docs were found 2. Related docs still appear consistent with the new learning 3. The overlap is superficial and does not change prior guidance 4. Refresh would require a broad historical review with weak evidence Use these rules: - If there is **one obvious stale candidate**, invoke `agentic-memory-refresh` with a narrow scope hint after the new learning is written - If there are **multiple candidates in the same area**, ask the user whether to run a targeted refresh for that module, category, or pattern set - If context is already tight or you are in lightweight mode, do not expand into a broad refresh automatically; instead recommend `agentic-memory-refresh` as the next step with a scope hint - **In headless mode**, never invoke `agentic-memory-refresh` and never ask the user. Surface the recommended scope hint in the terminal report's "Refresh recommendation" line and let the caller decide When invoking or recommending `agentic-memory-refresh`, be explicit about the argument to pass. Prefer the narrowest useful scope: - **Specific file** when one learning or pattern doc is the likely stale artifact - **Module or component name** when several related docs may need review - **Category name** when the drift is concentrated in one solutions area - **Pattern filename or pattern topic** when the stale guidance lives in `docs/solutions/patterns/` Examples: - `/agentic-memory-refresh plugin-versioning-requirements` - `/agentic-memory-refresh payments` - `/agentic-memory-refresh performance-issues` - `/agentic-memory-refresh critical-patterns` A single scope hint may still expand to multiple related docs when the change is cross-cutting within one domain, category, or pattern area. Do not invoke `agentic-memory-refresh` without an argument unless the user explicitly wants a broad sweep. Always capture the new learning first. Refresh is a targeted maintenance follow-up, not a prerequisite for documentation. ### Discoverability Check After the learning is written and the refresh decision is made, check whether the project's instruction files would lead an agent to discover and search `docs/solutions/` before starting work in a documented area. This runs every time — the knowledge store only compounds value when agents can find it. 1. Identify which root-level instruction files exist (AGENTS.md, CLAUDE.md, or both). Read the file(s) and determine which holds the substantive content — one file may just be a shim that `@`-includes the other (e.g., `CLAUDE.md` containing only `@AGENTS.md`, or vice versa). The substantive file is the assessment and edit target; ignore shims. If neither file exists, skip this check entirely. 2. Assess whether an agent reading the instruction files would learn three things: - That a searchable knowledge store of documented solutions exists - Enough about its structure to search effectively (category organization, YAML frontmatter fields like `module`, `tags`, `problem_type`) - When to search it (before implementing features, debugging issues, or making decisions in documented areas — learnings may cover bugs, best practices, workflow patterns, or other institutional knowledge) This is a semantic assessment, not a string match. The information could be a line in an architecture section, a bullet in a gotchas section, spread across multiple places, or expressed without ever using the exact path `docs/solutions/`. Use judgment — if an agent would reasonably discover and use the knowledge store after reading the file, the check passes. 3. If the spirit is already met, no action needed — move on. 4. If not: a. Based on the file's existing structure, tone, and density, identify where a mention fits naturally. Before creating a new section, check whether the information could be a single line in the closest related section — an architecture tree, a directory listing, a documentation section, or a conventions block. A line added to an existing section is almost always better than a new headed section. Only add a new section as a last resort when the file has clear sectioned structure and nothing is even remotely related. b. Draft the smallest addition that communicates the three things. Match the file's existing style and density. The addition should describe the knowledge store itself, not the plugin — an agent without the plugin should still find value in it. Keep the tone informational, not imperative. Express timing as description, not instruction — "relevant when implementing or debugging in documented areas" rather than "check before implementing or debugging." Imperative directives like "always search before implementing" cause redundant reads when a workflow already includes a dedicated search step. The goal is awareness: agents learn the folder exists and what's in it, then use their own judgment about when to consult it. Examples of calibration (not templates — adapt to the file): When there's an existing directory listing or architecture section — add a line: ``` docs/solutions/ # documented solutions to past problems (bugs, best practices, workflow patterns), organized by category with YAML frontmatter (module, tags, problem_type) ``` When nothing in the file is a natural fit — a small headed section is appropriate: ``` ## Documented Solutions