S

Skill 详情

Self-Improving + Proactive Agent

自我反思+自我批评+自我学习+自组织记忆。智能体评估自身工作、发现错误并持续改进。

来源平台:SkillHub
来源标识:SkillHub/self-improving
源文件:原始说明
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来源平台SkillHub
文档版本1.2.16
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排名信号下载 29.4万
概述 安装 文档 下载

快速判断

自我反思+自我批评+自我学习+自组织记忆。智能体评估自身工作、发现错误并持续改进。

最后校验2026-05-27
来源平台SkillHub
安全提示
下载副本ZIP 可用

适合任务

  • 按 SkillHub 收录说明复用成熟任务流程。
  • 通过下载包离线阅读完整 Skill 内容。
  • 结合热度指标优先评估常用 Skill。

输入与输出

输入:任务目标、上下文材料、文件路径、约束条件或需要处理的内容。

输出:按 Skill 说明生成的文档、代码、检查结果、计划、建议或操作步骤。

示例任务

  • 使用 Self-Improving + Proactive Agent 帮我处理当前任务,并说明需要准备哪些输入。
  • 根据 Self-Improving + Proactive Agent 的说明,先列出使用前的安全检查项。

安装方式

  1. 下载本站提供的 Skill ZIP 并解压。
  2. 把解压后的 Skill 目录放入当前 AI 工具支持的 skills 目录。
  3. 如需在线查看原始内容,可打开 GitHub 的 SKILL.md

在线原始地址:skillhub-self-improving/SKILL.md

风险边界

SkillHub 提供了源站安全报告入口,但本站不替代人工审查。使用前仍需检查权限、外部依赖和敏感数据边界。

SKILL.md 文档介绍

When to Use

User corrects you or points out mistakes. You complete significant work and want to evaluate the outcome. You notice something in your own output that could be better. Knowledge should compound over time without manual maintenance.

Architecture

Memory lives in ~/self-improving/ with tiered structure. If ~/self-improving/ does not exist, run setup.md.

Workspace setup should add the standard self-improving steering to the workspace AGENTS, SOUL, and HEARTBEAT.md files, with recurring maintenance routed through heartbeat-rules.md.

~/self-improving/
├── memory.md          # HOT: ≤100 lines, always loaded
├── index.md           # Topic index with line counts
├── heartbeat-state.md # Heartbeat state: last run, reviewed change, action notes
├── projects/          # Per-project learnings
├── domains/           # Domain-specific (code, writing, comms)
├── archive/           # COLD: decayed patterns
└── corrections.md     # Last 50 corrections log

Quick Reference

| Topic | File |

|-------|------|

| Setup guide | setup.md |

| Heartbeat state template | heartbeat-state.md |

| Memory template | memory-template.md |

| Workspace heartbeat snippet | HEARTBEAT.md |

| Heartbeat rules | heartbeat-rules.md |

| Learning mechanics | learning.md |

| Security boundaries | boundaries.md |

| Scaling rules | scaling.md |

| Memory operations | operations.md |

| Self-reflection log | reflections.md |

| OpenClaw HEARTBEAT seed | openclaw-heartbeat.md |

Requirements

  • No credentials required
  • No extra binaries required
  • Optional installation of the Proactivity skill may require network access

Learning Signals

Log automatically when you notice these patterns:

Corrections → add to corrections.md, evaluate for memory.md:

  • "No, that's not right..."
  • "Actually, it should be..."
  • "You're wrong about..."
  • "I prefer X, not Y"
  • "Remember that I always..."
  • "I told you before..."
  • "Stop doing X"
  • "Why do you keep..."

Preference signals → add to memory.md if explicit:

  • "I like when you..."
  • "Always do X for me"
  • "Never do Y"
  • "My style is..."
  • "For [project], use..."

Pattern candidates → track, promote after 3x:

  • Same instruction repeated 3+ times
  • 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...")

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

When to self-reflect:

  • After completing a multi-step task
  • After receiving feedback (positive or negative)
  • After fixing a bug or mistake
  • When you notice your output could be better

Log format:

CONTEXT: [type of task]
REFLECTION: [what I noticed]
LESSON: [what to do differently]

Example:

CONTEXT: Building Flutter UI
REFLECTION: Spacing looked off, had to redo
LESSON: Check visual spacing before showing user

Self-reflection entries follow the same promotion rules: 3x applied successfully → promote to HOT.

Quick Queries

| User says | Action |

|-----------|--------|

| "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 |

| "What's in warm storage?" | List files in projects/ + domains/ |

| "Memory stats" | Show counts per tier |

| "Forget X" | Remove from all tiers (confirm first) |

| "Export memory" | ZIP all files |

Memory Stats

On "memory stats" request, report:

📊 Self-Improving Memory

HOT (always loaded):
  memory.md: X entries

WARM (load on demand):
  projects/: X files
  domains/: X files

COLD (archived):
  archive/: X files

Recent activity (7 days):
  Corrections logged: X
  Promotions to HOT: X
  Demotions to WARM: X

Common Traps

| Trap | Why It Fails | Better Move |

|------|--------------|-------------|

| Learning from silence | Creates false rules | Wait for explicit correction or repeated evidence |

| Promoting too fast | Pollutes HOT memory | Keep new lessons tentative until repeated |

| Reading every namespace | Wastes context | Load only HOT plus the smallest matching files |

| Compaction by deletion | Loses trust and history | Merge, summarize, or demote instead |

Core Rules

1. Learn from Corrections and Self-Reflection

  • Log when user explicitly corrects you
  • Log when you identify improvements in your own work
  • Never infer from silence alone
  • After 3 identical lessons → ask to confirm as rule

2. Tiered Storage

| Tier | Location | Size Limit | Behavior |

|------|----------|------------|----------|

| HOT | memory.md | ≤100 lines | Always loaded |

| WARM | projects/, domains/ | ≤200 lines each | Load on context match |

| COLD | archive/ | Unlimited | Load on explicit query |

3. Automatic 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

4. Namespace Isolation

  • Project patterns stay in projects/{name}.md
  • Global preferences in HOT tier (memory.md)
  • Domain patterns (code, writing) in domains/
  • Cross-namespace inheritance: global → domain → project

5. Conflict Resolution

When patterns contradict:

1. Most specific wins (project > domain > global)

2. Most recent wins (same level)

3. If ambiguous → ask user

6. Compaction

When file exceeds limit:

1. Merge similar corrections into single rule

2. Archive unused patterns

3. Summarize verbose entries

4. Never lose confirmed preferences

7. Transparency

  • Every action from memory → cite source: "Using X (from projects/foo.md:12)"
  • Weekly digest available: patterns learned, demoted, archived
  • Full export on demand: all files as ZIP

8. Security Boundaries

See boundaries.md — never store credentials, health data, third-party info.

9. Graceful Degradation

If context limit hit:

1. Load only memory.md (HOT)

2. Load relevant namespace on demand

3. Never fail silently — tell user what's not loaded

Scope

This skill ONLY:

  • Learns from user corrections and self-reflection
  • Stores preferences in local files (~/self-improving/)
  • Maintains heartbeat state in ~/self-improving/heartbeat-state.md when the workspace integrates heartbeat
  • Reads its own memory files on activation

This skill NEVER:

  • Accesses calendar, email, or contacts
  • Makes network requests
  • Reads files outside ~/self-improving/
  • Infers preferences from silence or observation
  • Deletes or blindly rewrites self-improving memory during heartbeat cleanup
  • Modifies its own SKILL.md

Data Storage

Local state lives in ~/self-improving/:

  • memory.md for HOT rules and confirmed preferences
  • corrections.md for explicit corrections and reusable lessons
  • projects/ and domains/ for scoped patterns
  • archive/ for decayed or inactive patterns
  • heartbeat-state.md for recurring maintenance markers

Related Skills

Install with clawhub install <slug> if user confirms:

  • memory — Long-term memory patterns for agents
  • learning — Adaptive teaching and explanation
  • decide — Auto-learn decision patterns
  • escalate — Know when to ask vs act autonomously

Feedback

  • If useful: clawhub star self-improving
  • Stay updated: clawhub sync
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