Agent Memory Cleanup
/install agent-memory-cleanup
Agent Memory Cleanup
This skill should be lightweight and mostly invisible. Do not announce that a skill is being used unless the user asks. The user only needs to see a plain memory-quality prompt when action is useful.
Default Flow
- Detect memory pressure or pollution.
- If Python/file access is available, run a cheap summary check first:
python scripts/audit_memory.py memory.md --summary-json
- If
quality.interventionisno_intervention_needed, do not interrupt the user. - If intervention is needed, say briefly that memory appears too long, duplicated, conflicted, stale, or unsafe, and ask whether to review cleanup recommendations.
- After the user agrees, run:
python scripts/audit_memory.py memory.md --mode propose-patch --include-diff
- Apply only after a second explicit approval, unless unattended cleanup was already authorized:
python scripts/audit_memory.py memory.md --mode apply-approved
The apply mode must create timestamped backups before writing.
Trigger Points
Use this flow for:
- Memory write/update rejected, full, over budget, truncated, or too long.
- Short memory with secrets, task-state residue, duplicated facts, or conflicting preferences.
- User says a remembered fact is wrong, outdated, project-only, or should not be remembered.
- Before saving a new global memory candidate:
python scripts/audit_memory.py --candidate "candidate memory text" --summary-json
If candidate lint returns do_not_write_candidate_to_global_memory, do not store it globally. Offer to skip it or keep it as project/task notes.
Intervention Values
prompt_cleanup_now_secret_detected: recommend cleanup immediately; never echo raw secrets.prompt_user_review_conflicting_memory: ask the user to resolve conflicting durable preferences.do_not_write_candidate_to_global_memory: block global memory write.prompt_cleanup_recommended: offer cleanup recommendations.prompt_audit_recommended: mention memory quality may be degrading and ask whether to review.no_intervention_needed: stay silent.
Load Extra Context Only When Needed
Do not read references by default. Load them only for the matching need:
references/default-rules.json: deterministic thresholds and regex rules.references/classification-rubric.md: manual fallback if Python cannot run.references/agent-paths.md: path discovery when memory files are unclear.references/mcp-version.md: MCP wrapper design.
Safety
- Keep only stable global preferences and durable cross-task context.
- Remove or redact secrets, stale task state, branch/PR/debug notes, and one-off plans.
- Do not rewrite clean memory just for style.
- Do not broadly scan the user home directory without explicit request.
- Back up every edited memory file.
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install agent-memory-cleanup - 安装完成后,直接呼叫该 Skill 的名称或使用
/agent-memory-cleanup触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Agent Memory Cleanup 是什么?
Audit, clean, consolidate, and maintain long-term user memory files for OpenClaw, Hermes Agent, Codex, Claude, and other agents. Use when the user asks to cl... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 63 次。
如何安装 Agent Memory Cleanup?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install agent-memory-cleanup」即可一键安装,无需额外配置。
Agent Memory Cleanup 是免费的吗?
是的,Agent Memory Cleanup 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Agent Memory Cleanup 支持哪些平台?
Agent Memory Cleanup 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(windows, macos, linux)。
谁开发了 Agent Memory Cleanup?
由 hollis9087(@hollis9087)开发并维护,当前版本 v0.3.3。