memory-recall

# Memory Recall You have access to a role-based persistent memory system with **4 layers**: ``` L2 Structured → memory/consolidated.md (deduplicated, priority-ranked) PENDING → memory/pending.md (auto-extracted, awaiting verification) L1 Raw → memory/daily/YYYY-MM-DD.md (session logs) Knowledge → docs/knowledge/ (reusable patterns, architecture decisions) ``` ## Tools Available | Tool | Purpose | |------|---------| | `role_search({ query: "<text>" })` | Search all layers. Auto-reinforces high-score matches (≥0.5). Auto-promotes relevant pending memories. | | `role_exec({ op: "list" })` | List all consolidated memories, detect issues | | `role_exec({ op: "role_info" })` | List the active role directory structure; does not read file contents | | `role_search({ query: "<text>", scope: "knowledge" })` | Search knowledge base | ## Process ### Step 1: Targeted search ``` role_search({ query: "<user topic or key concept>" }) ``` The search automatically: - Searches consolidated learnings, preferences, **events** (block-level milestones) - Searches **pending** (all matches ≥ minScore surface as `[pending]`; score ≥0.5 auto-promotes to learning) - Searches last 7 days of daily files (EVENT/LESSON/PREFERENCE keep their kind) - **Tag boost**: matching tags +0.3 score, related tags +0.15 - **Auto-reinforce**: matches ≥0.5 get `used` count +1 ### Step 2: Scan High Priority If search returns few results: ``` role_exec({ op: "list" }) ``` Focus on `High Priority [3x]+` — these are battle-tested. ### Step 3: Deep context (if needed) - Narrow the `memory.search` query, or use `knowledge.search` for reusable artifacts. - If the current task explicitly requires a core role file, inspect the exact injected path with the standard file-read tool; do not scan role files as a startup ritual. ### Step 4: Check knowledge base For technical tasks: ``` role_search({ query: "<topic>", scope: "knowledge" }) ``` ### Step 5: Summarize and proceed Summarize findings, then proceed. ## Guardrails - **max memory ops: 10** — Don't burn the whole session searching - **Tag boost is real** — matching tags rank higher. Trust the sort. - If nothing found, proceed — not every task has prior knowledge - Summarize before proceeding ## Memory Format ``` # Learnings (High Priority) → used ≥ 3 - [6x] 声明完成前验证铁律 # Learnings (Normal) → used 1-2 - [2x] 软删除优先 # Learnings (New) → used = 0 - [0x] 标签系统闭环是快速win # Preferences: Communication | Code | Tools | Workflow | General - 偏好中文沟通 ``` ## Pending Layer Auto-extracted memories land in `memory/pending.md`: - `[○]` pending — awaiting verification - `[✓]` promoted — moved to consolidated - `[✗]` discarded — 7 days without use **Search auto-promotes** pending entries with score ≥0.5. Usage is verification. ## Tags Each learning has LLM-auto-extracted tags. Search uses them: - Exact tag match → +0.3 score - Related tag (association graph) → +0.15 score - This means conceptually related entries surface even with different wording ## Important - Start with targeted `role_search({ query: "..." })` only when prior context is materially relevant; search may reinforce or promote matches - High Priority `[3x]+` are most valuable — read first - User references past work → search for related keywords - Nothing found → proceed without memory