# Memory Recall

> Recall prior role memory when cross-session context materially affects the current task.

- Skill: `dwsy/memory-recall` (Agent Skill)
- Install (CLI): `npx skillmds@latest add dwsy/memory-recall`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dwsy/memory-recall/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: Dwsy (https://skillmd.com/u/dwsy)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/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 |
|------|---------|
| `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