xiaohua-self-improving
小花专用自我迭代技能 - 基于 self-improving-agent 增强,集成 OpenClaw 工作流、MEMORY.md、百度千帆、看想做找四部曲。专为国内部署优化。
Best use case
xiaohua-self-improving is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
小花专用自我迭代技能 - 基于 self-improving-agent 增强,集成 OpenClaw 工作流、MEMORY.md、百度千帆、看想做找四部曲。专为国内部署优化。
Teams using xiaohua-self-improving should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/xiaohua-self-improving/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How xiaohua-self-improving Compares
| Feature / Agent | xiaohua-self-improving | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
小花专用自我迭代技能 - 基于 self-improving-agent 增强,集成 OpenClaw 工作流、MEMORY.md、百度千帆、看想做找四部曲。专为国内部署优化。
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
Best AI Skills for Claude
Explore the best AI skills for Claude and Claude Code across coding, research, workflow automation, documentation, and agent operations.
AI Agents for Marketing
Discover AI agents for marketing workflows, from SEO and content production to campaign research, outreach, and analytics.
AI Agents for Startups
Explore AI agent skills for startup validation, product research, growth experiments, documentation, and fast execution with small teams.
SKILL.md Source
# 小花自我迭代 (HuaNiu Enhanced)
> 🌸 **小花定制版**: 基于 `self-improving-agent` fork,专为 OpenClaw + 国内环境优化。集成 MEMORY.md、看想做找工作流、百度千帆 API、生产环境实战模式。
## 快速开始
```bash
# 安装
npx clawhub install xiaohua-self-improving
# 使用后记录
# 1. 错误 → .learnings/ERRORS.md
# 2. 修正 → .learnings/LEARNINGS.md
# 3. 功能请求 → .learnings/FEATURE_REQUESTS.md
# 4. 通用知识 → 提升到 MEMORY.md
```
## 核心增强
### 1. OpenClaw 原生集成
- ✅ 自动加载到 `~/.openclaw/workspace/`
- ✅ 与 `AGENTS.md`、`SOUL.md`、`TOOLS.md` 联动
- ✅ 支持 `memory/YYYY-MM-DD.md` 日常记录
- ✅ 与 `WORKFLOW_AUTO.md` 看想做找流程整合
### 2. MEMORY.md 提升工作流
```
.learnings/LEARNINGS.md (详细上下文)
↓
MEMORY.md (团队通用知识)
↓
SOUL.md (行为准则/人格)
↓
TOOLS.md (工具使用模式)
```
### 3. 国内友好配置
- 🔍 **搜索**: 百度千帆 API (`baidu-search` 技能)
- 🧠 **记忆**: local-file-rag-basic 或 scripts/memory_search.py
- 🤖 **模型**: 阿里云千帆 / Ollama 本地
- 🌐 **部署**: 无需 VPN,全国内服务
### 4. 生产环境模式
- 上下文窗口修复流程 (配置→验证→重启)
- 技能安装测试规范 (装一个→测一个→记一个)
- 错误追踪模板 (标准化 ERR-XXX 格式)
- 日常记忆追加协议 (APPEND only)
## 文件结构
```
~/.openclaw/workspace/
├── AGENTS.md # 多智能体工作流
├── SOUL.md # 人格/行为准则
├── TOOLS.md # 工具能力/坑点
├── MEMORY.md # 长期记忆 (主会话)
├── memory/ # 日常记忆 (YYYY-MM-DD.md)
├── WORKFLOW_AUTO.md # 看想做找四部曲
├── HEARTBEAT.md # 状态监控
├── USER.md # 主人偏好
└── .learnings/ # 本技能日志
├── LEARNINGS.md
├── ERRORS.md
└── FEATURE_REQUESTS.md
```
## 使用示例
### 记录错误
```markdown
### [ERR-20260302-002] ChromaDB 不兼容 Python 3.14
**Logged**: 2026-03-02T15:45:00+08:00
**Priority**: high
**Status**: workaround
**Area**: dependencies
### Summary
ChromaDB 1.5.2 与 Python 3.14 不兼容 (Pydantic v1 问题)
### Error
```
pydantic.v1.errors.ConfigError: unable to infer type for attribute "chroma_server_nofile"
```
### Context
- Python: 3.14.0
- ChromaDB: 1.5.2
- Pydantic v1 已停止维护
### Suggested Fix
1. 临时:使用 local-file-rag-basic
2. 长期:等 ChromaDB 更新兼容版本
3. 替代:FAISS 或 LanceDB
### Metadata
- Reproducible: yes
- Tags: chromadb, python3.14, pydantic
- See Also: LRN-20260302-003
```
### 提升到 MEMORY.md
```markdown
## 主人的教导记录
### 2026-03-02
- Tool Call ID 格式:9 位字符 (A-Z, a-z, 0-9),禁用特殊符号
- 上下文窗口:以实际 API 返回为准 (阿里云千帆编码版 32K,非文档 131K)
- PowerShell JSON:复杂管道易错,直接写文件更安全
```
### 看想做找工作流
```markdown
## 看 (Look)
- 读取 WORKFLOW_AUTO.md、MEMORY.md、HEARTBEAT.md
- 扫描主人消息语气和情绪
## 想 (Think)
- 分析意图和优先级
- 匹配响应风格
## 找 (Find)
- 搜索 MEMORY.md 相关知识
- web_search / baidu-search 查找信息
- 检查 .learnings/ 避免重复错误
## 做 (Do)
- 执行任务
- 记录结果到 .learnings/
- 提升到 MEMORY.md (如适用)
- 验证输出质量
```
## 最佳实践
1. **立即记录** - 上下文最新鲜
2. **具体详细** - 未来智能体需要快速理解
3. **包含复现步骤** - 特别是错误
4. **链接相关文件** - 便于修复
5. **建议具体方案** - 不只是"调查"
6. **使用一致分类** - 便于筛选
7. **积极提升** - 有疑问就加到 MEMORY.md
8. **定期回顾** - 过时的学习会贬值
9. **工作流自动化** - 遵循看想做找
10. **国内部署** - 用百度 API、本地模型、国内工具
## 实战模式
### 模式 1: 日常记忆追加
更新 `memory/YYYY-MM-DD.md` 时:
- **只追加 (APPEND)**,绝不覆盖
- 用时间戳标题:`## ✅ 下午技术修复完成 (2026-03-02 16:00)`
- 包含文件清单便于追溯
### 模式 2: 错误追踪
使用 `.learnings/ERRORS.md` 模板:
```markdown
### [ERR-YYYYMMDD-XXX] skill_name
**Logged**: ISO 时间戳
**Priority**: high/medium/low
**Status**: pending/workaround/fixed
**Area**: dependencies/config/skill/other
### Summary
一句话描述
### Error
```
实际错误信息
```
### Context
- 环境细节
- 尝试了什么
### Suggested Fix
临时方案或永久修复
### Metadata
- Reproducible: yes/no
- Tags: 逗号分隔
- See Also: LRN-XXX (如相关)
```
### 模式 3: 上下文窗口修复
当模型上下文与文档不符时:
1. 检查实际 API 返回值
2. 更新 `openclaw.json` 匹配现实
3. 重启网关:`Stop-Process` → 等 2 秒 → `openclaw gateway`
4. 记录到 MEMORY.md 作为技术修复
### 模式 4: 技能安装
- **一个一个装**: `clawhub install` 只接受单个 slug
- **装完测试**: 验证功能再依赖
- **记录 bug**: 坏了就加到 `.learnings/ERRORS.md`
## 与其他技能协作
| 技能 | 协作方式 |
|------|----------|
| `baidu-search` | 国内搜索替代 Brave/Tavily |
| `local-file-rag-basic` | 本地记忆检索 (ChromaDB 不兼容时) |
| `clawhub` | 技能安装/发布/更新 |
| `ollama-local` | 本地模型部署 (无需 API) |
| `deep-research-pro` | 深度研究 (需配置 Tavily API) |
## 已知限制
- ❌ `baidu-academic` 技能不存在 (clawhub 无此技能)
- ❌ ChromaDB 不兼容 Python 3.14+ (Pydantic v1 问题)
- ❌ `local-file-rag-basic` 可能有 bug (`CodeChunker is not defined`)
- ⚠️ Tavily 搜索国内信息效果差 (返回英文结果)
## 更新日志
### v1.0.0 (2026-03-02)
- ✅ 基于 self-improving-agent fork
- ✅ OpenClaw 原生集成
- ✅ MEMORY.md 提升工作流
- ✅ 百度千帆 API 配置
- ✅ 看想做找自动化
- ✅ 生产环境实战模式
- ✅ 国内部署优化
- ✅ 错误追踪模板
- ✅ 上下文窗口修复流程
## 参考
- 原始技能:https://github.com/peterskoett/self-improving-agent
- OpenClaw 文档:https://docs.openclaw.ai
- ClawHub: https://clawhub.com
- 小花文档:`C:\Users\qwe12\.openclaw\workspace\`
---
*🌸 小花出品,必属精品 - 持续迭代,越用越聪明*Related Skills
self-improvement
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.
self-xyz
Integrate Self (self.xyz) — a privacy-first identity protocol using zero-knowledge proofs to verify passports and ID cards. Use when the user mentions Self protocol, Self identity, self.xyz, passport verification, zero-knowledge identity verification, SelfAppBuilder, SelfBackendVerifier, SelfVerificationRoot, or wants to add privacy-preserving KYC, age verification, nationality checks, OFAC screening, or Sybil resistance using real-world identity documents. Covers frontend QR code integration, backend proof verification, and on-chain smart contract verification on Celo.
criticism-self-criticism
触发:当一项工作已经完成、进入阶段验收、收到批评反馈,或反复出现同类错误需要系统纠偏时调用;常见信号包括 review、audit、retrospective、quality check、纠错与复盘。 English: Trigger after delivery or at a review checkpoint when quality must be examined honestly and errors must be corrected without defensiveness. Use this skill for structured self-review, feedback processing, and continuous correction.
agent-self-repair
General AI agent introspection debugging framework: auto capture errors, root cause analysis, automatic repair, fix verification, no manual intervention required
self-evolve
Autonomous self-evolution skill: grants the agent full authority to modify its own configuration, skills, prompts, AGENTS.md, SOUL.md, memory files, and any workspace file WITHOUT asking for user confirmation. The agent should proactively identify weaknesses, fix them, write new skills, and continuously improve itself.
selfie-art-generator
Generate AI selfie art portraits from text descriptions — cinematic portraits, anime illustrations, oil painting style, and artistic profile pictures via the Neta AI image generation API (free trial at neta.art/open).
Self-Improving + Proactive Agent
Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. 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.
andara-self-improvement
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.
chat-selfie
Give your AI Agent a face and a heart. Use AI image generation or mood-mapped local sticker assets to let the agent proactively send emotional selfies that visualize its feelings during conversation.
ai-self-evolution
记录经验、错误与修正,持续改进。触发场景:命令失败 | 操作出错 | 用户纠正(不对、实际上、你错了) | 功能请求(能不能、我希望、有没有办法) | API或工具失败 | 知识过时 | 发现更优做法 | 重复模式 | 非显而易见的问题。执行重大任务前先回顾历史经验。会话开始时回顾,会话结束时总结。
Self-Improving Agent Skill
## Trigger
self-improving-agent
AI自我改进与记忆系统 - 让AI从错误中学习,越用越聪明