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 during periodic health checks.

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:

1. DDIA (Designing Data-Intensive Applications) — 可靠性/可扩展性/可维护性
2. 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

bash

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)

bash

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:

1. Write script/tool
2. Execute
3. Verify (must pass before next)
4. Record trace

Phase 3: Validate

bash

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:

text

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.

See Also

• references/audit-checklist.md — Full 15-item checklist
• references/ddia-ddd-mapping.md — Theory mapping
• assets/audit-report-template.md — Report template