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Agent Skill · 研究检索

learn-from-experience(汲取经验)

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。

learn-from-experience 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 34,885。

总安装 2,165周安装 93GitHub Stars 1下载 759核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install learn-from-experience;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

learn-from-experience 怎么安装?

在终端执行 openclaw skills install learn-from-experience。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

learn-from-experience 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

learn-from-experience 需要什么权限?

权限级别是需要联网。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。

learn-from-experience 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

learn-from-experience 用的人多吗?

公开数据是累计安装 2,165 次,近一周安装 93 次,下载 759 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

learn-from-experience 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
learn-from-experience

汲取经验 · 本页

2,1651需要联网MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

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

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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