据 AlphaMoat 统计,截至 2026-09,Self-Improvement Agent 累计下载 3,923 次、获 2 个星标,在全部 5,078 个 AI 技能中排名第 2,640 位,在「AI 智能体」分类(969 个)中排名第 517 位。
| 平台 | 累计安装 | 累计下载 | 星标 | 评论 |
|---|---|---|---|---|
| ClawHub ↗ | 42 | 1,598 | 2 | - |
| SkillHub ↗ | 280 | 3,923 | 1 | — |
各平台的计数口径不同,统计时间不一致。
按模板将错误写入 .learnings/ERRORS.md,将纠正和方法经验写入 .learnings/LEARNINGS.md,将缺失功能写入 .learnings/FEATURE_REQUESTS.md。可关联重复条目、更新状态并定期回顾;只有广泛适用的经验才提升到项目指导文件。
资料发布于 2026-10-26
以下是根据技能文档生成的示例,不代表实际运行结果。
2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。
| 月份 | 累计下载 | 环比 | 星标 |
|---|---|---|---|
| 2026-09 | 3,923 | — | 2 |
| 2026-08 | 2,649 | +36.3% | 2 |
| 2026-07 | 1,944 | — | 2 |
| 2026-05 | 1,088 | — | - |
| 2026-03 | 364 | — | - |
以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。
---
name: self-improving-agent
description: "Captures learnings, errors, and corrections for continuous improvement. Use when: (1) A command fails unexpectedly, (2) User corrects you ('No, that's wrong...', 'Actually...'), (3) User requests a missing capability, (4) An external API or tool fails, (5) You realize your knowledge is outdated, (6) A better approach is found for a recurring task. Also review `.learnings/` before major tasks. Trigger words: 'actually', 'wrong', 'outdated', 'I wish you could', 'why can't you', non-zero exit code."
---
# Self-Improvement Skill
Log learnings and errors to markdown files. Important patterns get promoted to project memory files.
## Quick Reference| Situation | Action | |-----------|--------| | Command/operation fails | Log to `.learnings/ERRORS.md` | | User corrects you | Log to `.learnings/LEARNINGS.md` (category: `correction`) | | User wants missing feature | Log to `.learnings/FEATURE_REQUESTS.md` | | API/external tool fails | Log to `.learnings/ERRORS.md` | | Knowledge was outdated | Log to `.learnings/LEARNINGS.md` (category: `knowledge_gap`) | | Found better approach | Log to `.learnings/LEARNINGS.md` (category: `best_practice`) | | Similar to existing entry | Link with `**See Also**`, bump priority | | Broadly applicable learning | Promote to workspace files (see below) | ## Promotion Targets When a learning proves broadly applicable, elevate it: | Learning Type | Promote To | |---------------|------------| | Behavioral patterns | `SOUL.md` | | Workflow patterns | `AGENTS.md` | | Tool gotchas | `TOOLS.md` | | Project facts | `CLAUDE.md` or workspace prompt files | ## Logging Format ### Learning Entry ```markdown ## [LRN-YYYYMMDD-XXX] category **Logged**: ISO-8601 timestamp **Priority**: low | medium | high | critical **Status**: pending | resolved | promoted | in_progress ### Summary One-line description of what was learned ### Details Full context: what happened, what was wrong, what's correct ### Suggested Action Concrete fix or improvement ### Metadata - Source: conversation | error | user_feedback - Related Files: path/to/file.ext - Tags: tag1, tag2 - See Also: LRN-YYYYMMDD-XXX (if related) ``` ### Error Entry ```markdown ## [ERR-YYYYMMDD-XXX] command_or_tool **Logged**: ISO-8601 timestamp **Priority**: high **Status**: pending ### Summary Brief description of what failed ### Error ``` Paste actual error output here ``` ### Context - Command/operation attempted - Input or parameters used - Environment details ### Suggested Fix If identifiable, how to resolve it ### Metadata - Reproducible: yes | no | unknown - Related Files: path/to/file.ext ``` ### Feature Request Entry ```markdown ## [FEAT-YYYYMMDD-XXX] capability_name **Logged**: ISO-8601 timestamp **Priority**: medium **Status**: pending ### Requested Capability What the user wanted to do ### User Context Why they needed it ### Complexity Estimate simple | medium | complex ``` ## ID Generation `TYPE-YYYYMMDD-XXX` — e.g. `LRN-20250322-001`, `ERR-20250322-A3F` ## Resolving Entries When fixed: 1. Set **Status**: `resolved` or `promoted` 2. Add resolution block: ```markdown ### Resolution - **Resolved**: 2025-01-16T09:00:00Z - **Commit/PR**: abc123 or #42 - **Notes**: What was done ``` ## Promoting to Workspace Files **When**: Learning applies across files, prevents recurring mistakes, or any contributor should know it. **How**: Distill into a concise rule. Add to target file. Set original entry to `promoted`. Example learning: > Project uses pnpm workspaces. `npm install` fails because lock file is `pnpm-lock.yaml`. Promote to `CLAUDE.md`: ```markdown ## Build & Dependencies - Package manager: **pnpm** (not npm) — always use `pnpm install` ``` ## Recurring Pattern Detection Before logging, search for related entries: ```bash grep -r "keyword" .learnings/ ``` If similar entry exists: - Link with `**See Also**: LRN-...` - Increment `Recurrence-Count` - Consider systemic fix (promote to AGENTS.md, create tech debt ticket) **Promote to permanent prompt guidance** when a pattern: - Occurs 3+ times - Spans 2+ distinct tasks - Within a 30-day window ## OpenClaw Inter-Session Tools Share learnings across sessions: | Tool | Use | |------|-----| | `sessions_list` | View active/recent sessions | | `sessions_history(sessionKey, limit)` | Read another session's transcript | | `sessions_send(sessionKey, message)` | Send a learning to another session | | `sessions_spawn(task, label, mode)` | Spawn a background sub-agent | **Example**: After a session ends, promote its key learnings to workspace files. Use `sessions_history` to review what another session discovered. ## Periodic Review Check `.learnings/` at natural breakpoints: - Before starting a new major task - After completing a feature - Weekly during active development Quick status: ```bash grep -h "Status.*pending" .learnings/*.md | wc -l # count pending grep -B3 "Priority.*high" .learnings/*.md | grep "^##" # high priority items ``` ## Detection Triggers **Log immediately when you notice:** | Signal | → Entry | |--------|---------| | Non-zero exit code, exception, timeout | `ERRORS.md` | | User says "No, that's wrong...", "Actually..." | `LEARNINGS.md` (correction) | | User provides info you didn't know | `LEARNINGS.md` (knowledge_gap) | | "Can you also...", "I wish you could..." | `FEATURE_REQUESTS.md` | | Found a better approach mid-task | `LEARNINGS.md` (best_practice) | | Repeated mistake (2+ times) | Promote to workspace file | ## Best Practices 1. **Log immediately** — context is freshest right after the issue 2. **Be specific** — future you needs to understand quickly 3. **Suggest concrete fixes** — not just "investigate" 4. **Link related files** — makes fixes easier 5. **Promote aggressively** — broadly applicable learnings belong in workspace files, not hidden in `.learnings/` 6. **Use consistent IDs** — enables `See Also` linking ## Installation (OpenClaw) ```bash clawhub install self-improving-agent ``` Manual: ```bash git clone https://github.com/pskoett/self-improving-agent.git ~/.openclaw/skills/self-improving-agent ``` Create log directory: ```bash mkdir -p ~/.openclaw/workspace/.learnings ``` ## Detailed Topics For complete documentation, see: - **OpenClaw integration**: `references/openclaw-integration.md` - **Hook setup**: `references/hooks-setup.md` - **Full entry examples**: `references/examples.md` - **Skill extraction workflow**: `references/skill-extraction.md` - **Other agents (Claude Code, Codex, Copilot)**: `references/other-agents.md`
记录自身发现以实现自我改进的技能
帮助用户发现和安装智能体技能,当用户询问如「如何做X」、「找X的技能」、「有能做...的吗」等问题时
给 AI agent 用的浏览器自动化命令行技能。
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。
在安装第三方技能之前,按清单审查来源、权限与危险模式的技能。
类型化知识图谱,用于结构化智能体记忆与可组合技能。适用于以下场景:创建/查询实体(人物、项目、任务、事件、文档)、关联相关对象、强制执行约束、将多步操作规划为图谱变换,或当技能需要共享状态时。触发关键词包括"记住""我知道关于什么""将X链接到Y""显示依赖关系"、实体增删改查操作,以及跨技能数据访问。
将 AI 代理从任务执行者转变为能够预见需求、持续优化的主动伙伴。现已支持 WAL Protocol、Working Buffer、Autonomous Crons 及经过实战验证的模式。属于 Hal Stack 🦞。
创建有效技能指南。当用户希望创建新技能(或更新现有技能)以利用专业知识、工作流程或工具集成扩展 Claude 的能力时,应使用此技能。
数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。