记忆

Memory Audit — DDIA+DDD System Health Check

Audit and repair agent memory systems using DDIA reliability + DDD bounded-context analysis. Triggers when memory feels stale, bloated, inconsistent, or duri...

它能做什么

Audit and repair agent memory systems using DDIA reliability + DDD bounded-context analysis. Triggers when memory feels stale, bloated, inconsistent, or during periodic health checks.

技能文档

Memory Audit — 记忆系统架构审计与修复

当记忆系统出现膨胀、不一致、查询质量差、架构耦合等问题时，用软件工程视角系统化诊断和修复。

When to Trigger

MEMORY.md 或 AGENTS.md 超过 500 行 / 15KB
搜索返回重复结果或无关结果
日志文件缺少 Front Matter
项目状态与实际脱节
定期健康检查（建议每月一次）

Core Framework

Dual lens:

DDIA (Designing Data-Intensive Applications) — 可靠性/可扩展性/可维护性
DDD (Domain-Driven Design) — 有界上下文/聚合根/防腐层

Five problem domains:

域	核心问题	典型症状
Schema	无Schema约束，自由文本退化	字段不一致，无法程序化解析
Consistency	多存储间状态脱节	MEMORY.md写了旧模型，实际已换
Query	无去重、无联合搜索	搜出重复，漏掉归档
Lifecycle	无GC、无过期标记	历史项目堆积，噪音淹没信号
Coupling	配置/运维/理论混杂	改一处牵一片，token浪费

Workflow

Phase 0: Scope & Baseline

python3 scripts/audit_baseline.py --workspace . --report /tmp/audit-baseline.json

Collect: file counts, line counts, Front Matter coverage, duplication rate, staleness score.

Phase 1: Diagnosis (5 domains × N checks)

python3 scripts/audit_diagnose.py --baseline /tmp/audit-baseline.json --output /tmp/audit-report.md

Each finding → priority bucket (P0-P4):

P0 data integrity (broken/dangerous)
P1 consistency (drift/misalignment)
P2 query capability (search quality)
P3 architecture decoupling (coupling/bloat)
P4 knowledge systematization (advanced)

Phase 2: Execute Fixes (by priority)

Work P0 → P4 sequentially. Each fix:

Write script/tool
Execute
Verify (must pass before next)
Record trace

Phase 3: Validate

python3 scripts/audit_validate.py --report /tmp/audit-report.md

Phase 4: Handoff

Update IMPLEMENT.md with completed items
Update MEMORY.md if project status changed
Log to daily memory
Record trace via trace_logger

Key Scripts

Script	Source	Purpose
`audit_baseline.py`	this skill	Collect workspace metrics
`audit_diagnose.py`	this skill	Generate prioritized findings
`audit_validate.py`	this skill	Verify fixes passed
`memory_gc.py`	workspace	Semi-auto GC scan → suggestions
`staleness_check.py`	workspace	Detect stale entries (>60d)
`unified_search.py`	workspace	Cross-store federated search
`knowledge_graph.py`	workspace	Node/edge graph from memory
`gen_references_index.py`	workspace	Auto-generate INDEX.md

Adapting to Other Workspaces

This skill assumes an OpenClaw-style workspace:

workspace/
  MEMORY.md          # long-term semantic memory
  AGENTS.md          # operational handbook
  SOUL.md            # persona/behavior rules
  TOOLS.md           # ops reference
  IMPLEMENT.md       # task tracker
  memory/*.md        # daily episodic logs
  references/*.md    # archived source materials
  traces/            # agent execution traces
  docs/              # migrated detailed docs

For other layouts, adapt audit_baseline.py path constants.

Concept Audit

五维审计存量工程：漂移、边界、判据、组合、依赖，只读输出修复路由

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Audit and maintain OpenClaw-style long-term memory. Use for MEMORY.md cleanup, daily-note digestion, duplicate detection, stale-memory review, and promoting...

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Audit a principal AI agent or coordinator bot: review memory, learnings, recent errors, installed skills, operational risks, delegation posture, and propose...

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Design System Audit

Audit a design system for consistency, coverage, and quality. Use when asked to audit a design system, review a component library, assess design token covera...

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Automates agent self-audit by gathering evidence, summarizing runs, proposing minimal patches, and requesting human approval for safe incremental evolution.

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Audit agentic framework directories, prompt systems, skills, planner files, workflow guidance, memory-like files, configs, and agent-facing documentation for...

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