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learn-from-experience汲取经验

Agent Skill

learn-from-experience 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。

总安装

2,165

周安装

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GitHub Stars

1

下载量

759
OpenClaw

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:learn-from-experience(汲取经验)
来源仓库:https://github.com/shibing624/learn-from-experience
安装命令:
openclaw skills install learn-from-experience
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 learn-from-experience

简介

通过自我反省与跨会话同步,持续改进Agent的任务处理能力。

  • 适合需要长期积累错误修正与经验沉淀的智能体系统。
  • 自动捕获失败案例与用户反馈,构建自组织记忆库。learn-from-experience 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 数据存储路径需用户授权,避免敏感信息泄露风险。
  • 建议定期清理过期条目,防止知识库冗余影响决策效率。

SKILL.md

name
learn-from-experience
slug
learn-from-experience
version
1.3.0
description
Learn from experience: self-reflection + self-criticism + self-learning + self-organizing memory + cross-session sync. Agent evaluates its own work, catches mistakes, and improves permanently. Compiles confirmed learnings to global config for automatic cross-session persistence. Use when (1) a command, tool, API, or operation fails; (2) the user corrects you or rejects your work; (3) you realize your knowledge is outdated or incorrect; (4) you discover a better approach; (5) the user explicitly installs or references the skill for the current task.
changelog
v1.3.0: Rebrand to learn-from-experience. Add cross-session memory sync protocol — compile confirmed preferences to agent global config for automatic loading in new sessions. Multi-agent compatibility: Claude Code, OpenClaw, Codex, CodeBuddy, opencode.
metadata
{"clawdbot":{"emoji":"🧠","requires":{"bins":[]},"os":["linux","darwin","win32"],"configPaths":["~/learn-from-experience/"],"configPaths.optional":["./AGENTS.md","./SOUL.md","./HEARTBEAT.md"]}}

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.

Supported Agent Products

This skill is agent-agnostic. It works with any product that loads a global config file on session start.

ProductSkill Install PathGlobal Config FileGlobal Config Path
Claude Code~/.claude/skills/learn-from-experience/CLAUDE.md~/.claude/CLAUDE.md
OpenClaw~/.openclaw/skills/learn-from-experience/AGENTS.md~/.openclaw/AGENTS.md
Codex~/.codex/skills/learn-from-experience/AGENTS.md~/.codex/AGENTS.md
CodeBuddy~/.codebuddy/skills/learn-from-experience/CODEBUDDY.md~/.codebuddy/CODEBUDDY.md
opencode~/.config/opencode/skills/learn-from-experience/AGENTS.md~/.config/opencode/AGENTS.md

The skill auto-detects which product is running by checking which config path exists. Memory data always lives in ~/learn-from-experience/ (shared across all products).

Architecture

Memory lives in ~/learn-from-experience/ with tiered structure. If ~/learn-from-experience/ does not exist, run setup.md. Workspace setup should add the standard steering to the workspace AGENTS, SOUL, and HEARTBEAT.md files, with recurring maintenance routed through heartbeat-rules.md. Confirmed preferences are compiled to the agent's global config ### Patterns section for cross-session persistence (see Sync Protocol below).

~/learn-from-experience/
├── memory.md          # HOT: <=100 lines, always loaded
├── index.md           # Topic index with line counts + sync status
├── 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

TopicFile
Setup guidesetup.md
Heartbeat state templateheartbeat-state.md
Memory templatememory-template.md
Workspace heartbeat snippetHEARTBEAT.md
Heartbeat rulesheartbeat-rules.md
Learning mechanicslearning.md
Security boundariesboundaries.md
Scaling rulesscaling.md
Memory operationsoperations.md
Self-reflection logreflections.md

Requirements

  • No credentials required
  • No extra binaries required
  • Agent must have a global config file that auto-loads on session start

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..."
  • "bu dui, bu shi zhe yang de......"
  • "shi ji shang, ying gai shi......"
  • "ni gao cuo le......"
  • "wo geng xi huan X, bu shi Y"
  • "ji zhu wo yong yuan dou yao......"
  • "wo zhi qian gen ni shuo guo......"
  • "bie zai zuo X le"
  • "ni wei shen me yi zhi......"

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..."
  • "ni zhe yang...de shi hou wo jue de hen hao"
  • "yi ding yao bang wo zuo X"
  • "yong yuan bu yao zuo Y"
  • "wo de feng ge shi......"
  • "zai [xiang mu] zhong yao yong......"

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: Flutter UI build
REFLECTION: Spacing was wrong, had to redo
LESSON: Check visual spacing before showing to user

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

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
"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
"Sync memory" / "sync"Run cross-session sync now

Memory Stats

On "memory stats" request, report:

Learn-from-Experience Memory

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

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

COLD (archived):
  archive/: X files

Cross-session sync:
  Last sync: YYYY-MM-DD
  Status: in_sync | stale

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

Common Traps

TrapWhy It FailsBetter Move
Learning from silenceCreates false rulesWait for explicit correction or repeated evidence
Promoting too fastPollutes HOT memoryKeep new lessons tentative until repeated
Reading every namespaceWastes contextLoad only HOT plus the smallest matching files
Compaction by deletionLoses trust and historyMerge, 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

TierLocationSize LimitBehavior
HOTmemory.md<=100 linesAlways loaded
WARMprojects/, domains/<=200 lines eachLoad on context match
COLDarchive/UnlimitedLoad 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

Cross-Session Sync Protocol

Problem

Memory in ~/learn-from-experience/memory.md is only loaded when the skill is activated. The agent's global config file is automatically loaded every session. Without sync, learnings from one session are invisible to the next.

Solution

Compile confirmed preferences from memory.md into the global config's ### Patterns section under ## Learnings. This is a one-way compile: memory.md is the source of truth, global config is the read-only cache.

When to Sync

  • After recording a user correction to Confirmed Preferences
  • After promote/demote/compact operations on memory.md
  • User explicitly says "sync memory"
  • Session end self-reflection if new confirmed rules were added this session

Sync Rules

  1. Source: Only ## Confirmed Preferences entries from memory.md
  2. Format: Each entry compiles to - [tag] one-line description
  3. Target: ### Patterns block under ## Learnings in agent's global config
  4. Limit: Max 30 lines; if exceeded, keep only HIGH priority + highest confidence entries
  5. Safety: Only replace content between ### Patterns header and the next ### or ## header. Never touch other global config content
  6. Timestamp: Include sync timestamp in HTML comment after ### Patterns header
  7. Stale detection: On session start, check ~/learn-from-experience/index.md sync status. If stale, auto-sync

Sync Flow

1. Read ~/learn-from-experience/memory.md -> extract ## Confirmed Preferences
2. Compile each entry: "### title | confidence" -> "- [tag] description"
3. Detect agent product -> locate global config file
4. Read global config file
5. Find ### Patterns block (create if missing, under ## Learnings)
6. Replace block content (preserve everything else)
7. Update ~/learn-from-experience/index.md sync timestamp + status

Agent Detection

To find the correct global config file:

Check in order (first existing path wins):
1. ~/.claude/CLAUDE.md              (Claude Code)
2. ~/.openclaw/AGENTS.md            (OpenClaw)
3. ~/.codex/AGENTS.md               (Codex)
4. ~/.codebuddy/CODEBUDDY.md        (CodeBuddy)
5. ~/.config/opencode/AGENTS.md     (opencode)

If multiple exist, sync to all of them (user may use multiple products).

Session Lifecycle

Session Start:
  1. Global config auto-loads (built-in to agent product)
  2. Skill activates: read ~/learn-from-experience/memory.md (full HOT)
  3. Check index.md sync status -> if stale, auto-sync
  4. Detect project context -> load projects/{name}.md if needed

Session Work:
  5. Correction received -> write corrections.md + memory.md + sync global config
  6. New pattern discovered -> write memory.md (tentative) [no sync until confirmed]

Session End:
  7. If new confirmed rules this session -> sync global config

Scope

This skill ONLY:

  • Learns from user corrections and self-reflection
  • Stores preferences in local files (~/learn-from-experience/)
  • Syncs confirmed preferences to agent's global config ### Patterns section
  • Maintains heartbeat state in ~/learn-from-experience/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 ~/learn-from-experience/ and the agent's global config file
  • Infers preferences from silence or observation
  • Deletes or blindly rewrites memory during heartbeat cleanup
  • Modifies its own SKILL.md
  • Touches global config content outside the ### Patterns block

Data Storage

Local state lives in ~/learn-from-experience/:

  • 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
  • index.md for tier index + cross-session sync status

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

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

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。

平台分布

OpenClaw

90.95%
按下载量换算690

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

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