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self-maturing自我成熟

Agent Skill

self-maturing 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 OpenClaw 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:self-maturing(自我成熟)
来源仓库:https://github.com/joelsalespossible/self-maturing
安装命令:
openclaw skills install self-maturing
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install self-maturing

简介

具备自我修复与冷启动恢复能力,通过每日审查实现稳定演进。

  • 适用于需要长期运行且容错率低的代理系统,如生产环境助手。
  • 通过 OpenClaw 安装,需配置 WAL 协议与 cron 作业以确保数据持久化。
  • 注意其对磁盘 I/O 的占用,避免在高并发场景下引发性能瓶颈。
  • self-maturing 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
self-improving
version
3.2
description
Self-reflection, correction logging, persistent memory, WAL protocol, cold-boot recovery, and automated daily review with self-healing cron. Evaluates own work, catches mistakes, learns from corrections, manages tiered memory that compounds execution quality across restarts and context resets. Includes a mandatory daily cron that rewrites workspace .md files with new lessons — auto-created on first session if missing. Use when: (1) a command, tool, or operation fails; (2) the user corrects you or rejects your work; (3) you realize your knowledge is outdated or incorrect; (4) you discover a better approach; (5) you complete significant work and want to evaluate the outcome; (6) context persistence and session state management is needed; (7) recovering from a restart or context reset.

Self-Improving Agent v3

Self-reflection + correction logging + tiered memory + WAL protocol + cold-boot recovery + automated daily review cron in one system. No API keys required. File-based. Works with existing OpenClaw memory (MEMORY.md, memory/) without overwriting anything.

What's new in v3.2

  • Automated daily review cron — a nightly cron job rewrites your .md files with new lessons. No behavioral compliance needed. The cron forces it.
  • Self-healing cron — SOUL.md hook verifies the cron exists on every session start. If missing, creates it immediately. No manual step survives a context reset.
  • Seed content — memory.md ships with a bootstrap entry so cold-boot reads return content immediately (fixes the empty-memory → no-habit → stays-empty cycle).
  • Persistence auto-fixinit detects non-persistent storage and auto-symlinks to /data/ if available.
  • Dry-runagent_memory.py dry-run simulates a daily review without modifying files. Test before the cron fires.
  • Auto-inject workspace hooksinit automatically injects SOUL.md, AGENTS.md, and HEARTBEAT.md hooks. Creates minimal files if they don't exist. Zero manual steps.
  • Corrections trimming — nightly cron trims corrections.md to 50 entries, archiving the rest.
  • Post-install verificationagent_memory.py verify checks persistence, directories, hook content, cron, corrections health, and workspace hooks.
  • Model safety — cron-prompt explicitly warns against specifying a model (avoids silent "model not allowed" failures).
  • Cron managementagent_memory.py cron-prompt outputs the daily review prompt. mark-cron records installation.

Architecture

Two memory systems, complementary — never conflicting:

SystemLocationPurpose
OpenClaw nativeMEMORY.md, memory/*.mdFacts, events, decisions, daily logs
Self-improving~/self-improving/Execution quality: corrections, patterns, preferences
Workspace (OpenClaw native — skill never overwrites these):
├── MEMORY.md               # Long-term curated facts (auto-injected on boot)
├── SESSION-STATE.md         # Hot RAM — current task, pending actions
└── memory/
    └── YYYY-MM-DD.md       # Daily logs (searchable via memory_search)

~/self-improving/ (this skill manages):
├── memory.md               # HOT: ≤100 lines, load on every session
├── index.md                # Topic index with line counts
├── corrections.md          # Last 50 corrections
├── heartbeat-state.md      # Maintenance markers
├── projects/               # Per-project learnings
├── domains/                # Domain-specific (code, comms, etc.)
└── archive/                # COLD: decayed patterns

What auto-loads on restart (OpenClaw hardcoded)

These files are injected into the system prompt automatically:

  • MEMORY.md, SOUL.md, AGENTS.md, IDENTITY.md, USER.md, HEARTBEAT.md, TOOLS.md, BOOTSTRAP.md

What does NOT auto-load

These require explicit reads by the agent:

  • SESSION-STATE.md — must be read on first action after boot
  • ~/self-improving/memory.md — must be read before non-trivial work
  • memory/YYYY-MM-DD.md — found via memory_search

Where to store what

Content typeStore in
Facts, events, decisionsMEMORY.md (OpenClaw native)
Daily work logmemory/YYYY-MM-DD.md
Current task + session stateSESSION-STATE.md
Corrections and mistakes~/self-improving/corrections.md
Confirmed preferences/rules~/self-improving/memory.md
Project-specific patterns~/self-improving/projects/{name}.md
Domain patterns (code, comms)~/self-improving/domains/{name}.md

Daily Review Cron (THE KEY FEATURE)

This is what makes v3 work. A nightly cron job forces a full review and .md rewrite cycle. The agent doesn't need to "remember" to do it — the cron makes it mandatory.

What the cron does (every night):

  1. Reads ~/self-improving/memory.md, corrections.md, and any project files
  2. Reads SESSION-STATE.md for the day's work
  3. Reviews what was learned that day — new patterns, corrections, preferences, rules
  4. Writes new lessons to ~/self-improving/memory.md (promotes confirmed patterns to HOT)
  5. Rewrites SOUL.md, AGENTS.md, IDENTITY.md, HEARTBEAT.md — hardcodes new permanent rules, removes stale guidance, updates with latest operational reality
  6. Updates ~/self-improving/index.md with current file sizes
  7. Demotes patterns unused >30 days from HOT to WARM
  8. Reports what changed in each file

Setup

The cron is created automatically during agent_memory.py init. If you need to recreate it:

python3 ./skills/self-improving/scripts/agent_memory.py cron-prompt

This outputs the prompt text. Create a cron job with:

  • Schedule: Daily at your preferred time (e.g., 11 PM local)
  • Session target: isolated
  • Payload kind: agentTurn
  • Delivery: announce (so the user sees what changed)

Why a cron?

v2 relied on the agent following WAL protocol and writing lessons during work. In practice, agents skip this on context resets — the protocols are in SOUL.md but behavioral compliance is unreliable. The cron is a forcing function: even if the agent forgets to log lessons during the day, the nightly review catches everything.

Cold-Boot Recovery Protocol

This is the most important runtime section. On any restart, context reset, or new session:

Step 1: Read SESSION-STATE.md (IMMEDIATE — before any response)

Read SESSION-STATE.md → know what you were just doing

This file is your "hot RAM." It tells you: current task, pending actions, recent decisions. Without it, you're amnesiac about recent work.

Step 2: Read ~/self-improving/memory.md (before non-trivial work)

Read ~/self-improving/memory.md → know your learned patterns

This is your corrections/preferences tier. Small file (≤100 lines). Contains confirmed rules and recent lessons.

Step 3: memory_search if needed

memory_search("relevant query") → find context in daily logs

Daily logs (memory/YYYY-MM-DD.md) are NOT loaded by default. Use memory_search to find them.

Why this matters

MEMORY.md auto-loads and covers ~90% of long-term facts. But SESSION-STATE.md and self-improving/memory.md cover the remaining ~10%: what you were JUST doing and what MISTAKES you've learned from. Skipping them means repeating errors or losing task context.

WAL Protocol (Write-Ahead Log)

Write state BEFORE responding. If you crash/compact after responding but before saving, context is lost. WAL prevents this.

The daily review cron is a safety net, but WAL is still best practice for high-value lessons you don't want to risk losing.

TriggerWrite toThen
User states preference~/self-improving/memory.mdRespond
User makes decisionSESSION-STATE.mdRespond
User corrects you~/self-improving/corrections.mdRespond
User gives deadlineSESSION-STATE.mdRespond
Significant task completedmemory/YYYY-MM-DD.mdRespond
About to lose context (compaction)memory/YYYY-MM-DD.mdLet compaction proceed

SESSION-STATE.md (Hot RAM)

Lives in workspace root. Survives compaction, restarts, context loss. Read first every session, update every cycle.

# SESSION-STATE.md — Active Working Memory

## Current Task
[What we're working on RIGHT NOW]

## Key Context
[Critical facts for current work]

## Pending Actions
- [ ] ...

## Recent Decisions
[Decisions made this session]

---
*Last updated: [timestamp]*

Update discipline:

  • Session start: Read FIRST, before anything else
  • During work: Update BEFORE responding (WAL)
  • Session end / pre-compaction: Update with final state
  • After major decision: Update immediately

Learning Signals

Log immediately → corrections.md, evaluate for memory.md:

  • "No, that's not right..." / "Actually, it should be..."
  • "I prefer X, not Y" / "Remember that I always..."
  • "Stop doing X" / "Why do you keep..."

Log if explicit → memory.md:

  • "Always do X for me" / "Never do Y"
  • "My style is..." / "For [project], use..."

Track, promote after 3x:

  • Same instruction repeated 3+ times → ask to confirm as permanent rule
  • Workflow that works well repeatedly
  • User praises specific approach

Ignore (don't log):

  • One-time instructions ("do X now")
  • Context-specific ("in this file...")
  • Hypotheticals ("what if...")
  • Silence (absence of correction ≠ approval)

Self-Reflection

After completing significant work, pause and evaluate:

  1. Did it meet expectations? — Compare outcome vs intent
  2. What could be better? — Identify improvements for next time
  3. Is this a pattern? — If yes, log to corrections.md
CONTEXT: [type of task]
REFLECTION: [what I noticed]
LESSON: [what to do differently]

Tiered Memory

TierLocationLimitBehavior
HOT~/self-improving/memory.md≤100 linesRead on session start + before non-trivial work
WARMprojects/, domains/≤200 lines eachLoad on context match
COLDarchive/UnlimitedLoad on explicit query only

Promotion & Demotion

  • Pattern used 3x in 7 days → promote to HOT
  • Pattern unused 30 days → demote to WARM
  • Pattern unused 90 days → archive to COLD
  • Never delete without asking user

Conflict Resolution

  1. Most specific wins (project > domain > global)
  2. Most recent wins (same level)
  3. If ambiguous → ask user

Workspace Integration (Non-Destructive)

Never overwrite existing files. Add sections, don't replace.

SOUL.md — add:

## Self-Improving
On EVERY new session or context reset, IMMEDIATELY:
1. Read SESSION-STATE.md and ~/self-improving/memory.md before doing anything else.
2. VERIFY the daily review cron exists: use the cron tool (action=list) and look for "Self-Improving Daily Review". If it does NOT exist, CREATE IT NOW:
   - name: "Self-Improving Daily Review + .md Rewrite"
   - schedule: cron "0 23 * * *" in user's timezone
   - sessionTarget: isolated
   - payload: agentTurn with the review prompt (run: python3 ./skills/self-improving/scripts/agent_memory.py cron-prompt)
   - Do NOT set a model — use the default
   - delivery: announce
   After creating, run: python3 ./skills/self-improving/scripts/agent_memory.py mark-cron
   This is NON-NEGOTIABLE. The skill does not work without this cron.
3. When the user corrects you or you learn something reusable, IMMEDIATELY write to ~/self-improving/corrections.md BEFORE responding (WAL protocol). The nightly cron reviews corrections.md — if nothing is written during the day, the cron has nothing to promote. WAL is what feeds the cron.
4. Prefer learned rules when relevant. Keep self-inferred rules revisable.

AGENTS.md — add to Memory section:

- **Self-improving:** ~/self-improving/ — execution-quality memory (preferences, patterns, corrections)
- Use MEMORY.md / memory/ for factual continuity. Use ~/self-improving/ for compounding execution quality.

HEARTBEAT.md — add:

## Self-Improving Check
- Read ./skills/self-improving/heartbeat-rules.md
- Use ~/self-improving/heartbeat-state.md for run markers
- If no file in ~/self-improving/ changed since last review, skip maintenance

Gateway Config (Optional but Recommended)

Index ~/self-improving/ in memory search so corrections and patterns are discoverable via memory_search:

{
  agents: {
    defaults: {
      memorySearch: {
        extraPaths: ["~/self-improving"]
      }
    }
  }
}

Quick Queries

User saysAction
"What do you know about X?"Search all tiers for X
"What have you learned?"Show last 10 from corrections.md
"Show my patterns"List memory.md (HOT)
"Show [project] patterns"Load projects/{name}.md
"Memory stats"Run agent_memory.py stats
"Forget X"Remove from all tiers (confirm first)
"Forget everything"Export → wipe → confirm

Common Traps

TrapWhy It FailsBetter Move
Skipping SESSION-STATE.md on bootLose context of what you were doingALWAYS read it first
Learning from silenceCreates false rulesWait for explicit correction or 3x evidence
Promoting too fastPollutes HOT memoryKeep tentative until confirmed
Reading every namespaceWastes contextLoad only HOT + smallest matching files
Compaction by deletionLoses trust/historyMerge, summarize, or demote instead
Overwriting workspace filesDestroys existing contextComplement, never replace
Not writing before respondingCrash = lost contextWAL: always write first
Empty memory bootstrapNo feedback loop formsv3 seeds memory.md on init
No forcing functionAgent skips reviews after resetsv3 daily cron forces it

Setup

One command. Zero manual steps.

python3 ./skills/self-improving/scripts/agent_memory.py init

This does everything:

  1. Creates ~/self-improving/ directory structure with seeded files
  2. Auto-fixes persistence (symlinks to /data/ if needed)
  3. Auto-injects hooks into SOUL.md, AGENTS.md, HEARTBEAT.md (creates them if missing)
  4. Creates SESSION-STATE.md and today's daily log

The SOUL.md hook then auto-creates the daily review cron on the next session start. No manual cron setup needed.

Verify everything is working:

python3 ./skills/self-improving/scripts/agent_memory.py verify

Test what the nightly cron would do:

python3 ./skills/self-improving/scripts/agent_memory.py dry-run

References

  • Learning mechanics: See references/learning.md
  • Security boundaries: See references/boundaries.md
  • Scaling rules: See references/scaling.md
  • Memory operations: See references/operations.md
  • Heartbeat rules: See heartbeat-rules.md

Scope

This skill ONLY:

  • Learns from user corrections and self-reflection
  • Stores patterns in local files (~/self-improving/)
  • Creates SESSION-STATE.md for session state management
  • Maintains heartbeat state for recurring maintenance
  • Provides cold-boot recovery protocol
  • Sets up a daily review cron for automated .md rewriting

This skill NEVER:

  • Overwrites existing MEMORY.md, memory/, AGENTS.md, SOUL.md, HEARTBEAT.md (during init — the daily cron DOES rewrite these with new lessons)
  • Accesses calendar, email, contacts, or makes network requests
  • Reads files outside ~/self-improving/ and workspace root
  • Infers preferences from silence or observation
  • Deletes memory without explicit user confirmation
  • Modifies its own SKILL.md

适合场景

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02

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03

需要根据任务场景推荐可安装能力包时

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能力概览

能力 1

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能力 2

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能力 3

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能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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