dwsy/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 | |------|---------| | memory({ action: "search", query: "<text>" }) | Search all layers. Auto-reinforces high-score matches (≥0.5). Auto-promotes relevant pending memories. | | memory({ action: "list" }) | List all consolidated memories, detect issues | | role_read | Read role file (default: memory/consolidated.md) | | role_search | Full-text search across role files | | knowledge({ action: "search", query: "<text>" }) | Search knowledge base |

Process

Step 1: Targeted search

memory({ action: "search", query: "<user topic or key concept>" })

The search automatically:

• Searches consolidated learnings, preferences, events
• Searches pending memories (auto-promotes score ≥0.5)
• Searches last 7 days of daily files
• 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:

memory({ action: "list" })

Focus on High Priority [3x]+ — these are battle-tested.

Step 3: Deep context (if needed)

• role_search({ query: "<concept>" }) → find related role files
• role_read({ path: "core/constraints.md" }) → read full file

Step 4: Check knowledge base

For technical tasks:

knowledge({ action: "search", query: "<topic>" })

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

• Always start with memory({ action: "search", query: "..." })
• High Priority [3x]+ are most valuable — read first
• User references past work → search for related keywords
• Nothing found → proceed without memory