Dwsy 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
usedcount +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 filesrole_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