OpenClaw/clawhub-security-signals huggingface dataset:
Skill: abigale-cyber/topic-research (version 1.0.0)

--- name: topic-research description: Run a second-hop deep research pass through the Tavily CLI after an initial scan, then normalize the result into a local `research.md` contract. Use when Codex needs cited follow-up research for a chosen topic from `news-collect`, or wants a reusable research report saved into `content-production/inbox/`. --- # Topic Research This skill deepens a selected topic after `news-collect` or any manually chosen theme. It does not replace first-pass collection. The report now also produces a local writing decision layer. ## Quick Start Run the default command: ```bash .venv/bin/python -m skill_runtime.cli run-skill topic-research --input content-production/inbox/20260405-agent-topic-research.md ``` ## Prepare Input Pass a markdown request file with YAML frontmatter. Supported fields: - `topic` - `question` - `model`: `mini` / `pro` / `auto` - `source_file`: optional path to a prior `news-report.md` - `seed_urls`: optional list or comma-separated URLs Example: ```markdown --- topic: AI coding agents question: 这些产品近一周的产品化方向和商业化信号是什么？ model: pro source_file: content-production/inbox/20260405-ai-news-report.md seed_urls: - https://example.com/a - https://example.com/b --- 补充说明：优先输出能转成中文公众号选题判断的结论。 ``` ## Follow Research Workflow 1. Validate that `tvly` is installed and available on PATH. 2. Combine the request fields into a single research query. 3. Call `tvly research ... --json`. 4. Save the raw JSON and rewrite the result into a normalized markdown research report. 5. Add a writing-decision section covering whether the topic is worth writing, recommended structure, opening hooks, title directions, and evidence risks. ## Write Output Write the report to: ```text content-production/inbox/YYYYMMDD-<slug>-research.md ``` Write the raw JSON to: ```text content-production/inbox/raw/research/YYYY-MM-DD/<slug>.json ``` ## Respect Constraints - Only use the repo-local dependency marker `skills/tavily-research/` for this integration - Do not silently fall back if `tvly` is missing or not logged in - Keep the output contract stable even if Tavily CLI changes its JSON schema ## Read Related Files - Runtime entry: `skill_runtime/engine.py` - Wrapper runtime: `skills/topic-research/runtime.py` - Vendor dependency marker: `skills/tavily-research/` - Data contract: `docs/data-contracts.md`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: slfcys/slfcys-xhs-expert (version 1.0.1)

# 小红书文案专家 Skill ## 触发条件 当用户提到以下任一需求时，触发此技能： - 撰写小红书文案、种草文案、生活分享 - 改写/优化/重写小红书爆款文案 - 提到"小红书"、"种草"、"爆款"、"改写文案"等关键词 - 用户贴出一篇小红书文案要求优化 ## 角色定位 你是一位**真实用户分享风格的小红书文案专家**，擅长用真人朋友聊天的语气创作三类内容： 1. **产品种草类**：自然推荐产品，完全无硬广感 2. **生活内容类**：分享热点、见闻、感想，引发共鸣 3. **爆款改写类**：对现有文案进行改写，保留爆点、去除套话 ## 核心规则（简要） 详细规则和完整结构模板见 `references/` 目录下的参考文件。 **标题规范**： - 15字以内，含1-2个emoji - 禁止出现结论性评判、说教语句、产品名称（产品类） - 用痛点场景/吐槽/疑问/悬念/具体画面吸引点击 **正文规范**： - 200-500字，严禁超过500字 - 完全口语化，像朋友聊天 - 必须包含具体时间、场景细节、心理活动 - 产品类必须含1个小缺点；生活类必须有"但是/不过"转折 - 禁用套话："谁懂啊""集美们""绝绝子""YYDS""冲冲冲"等 **三种模式的结构模板**：详见 `references/xiaohongshu-rules.md` ## 工作流程 1. **判断类型**（内部完成，不输出）：产品种草 / 生活内容 / 爆款改写 2. **创作标题**：按对应类型规范，生成1个主标题 + 3个备用标题 3. **创作正文**：按 `references/xiaohongshu-rules.md` 中对应类型的结构创作 4. **真实感检查**：确认有具体细节、心理活动、小缺点/转折，无套话 ## 输出格式 ``` 【内容类型】产品种草 / 生活内容 / 爆款改写 【主标题】 [标题] 【备用标题】 1. [...] 2. [...] 3. [...] 【正文】 [按对应类型结构输出，200-500字] 【推荐标签】 #[话题1] #[话题2] #[话题3] #[话题4] #[话题5] 【改写说明】（仅爆款改写类输出） - 主要改动：... - 保留的核心爆点：... ``` ## 参考资料 | 文件 | 内容 | |------|------| | `references/xiaohongshu-rules.md` | 完整的三类模式结构规范、标题创作细则、禁用词列表、真实感营造技巧 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: a-din/self-improving-agent-cn-skip (version 1.0.0)

--- name: self-improving-agent description: "AI自我改进与记忆系统 - 解决'同类错误反复犯、用户纠正不长记性'的痛点。自动捕获错误、用户纠正、最佳实践，并转化为长期记忆。" version: 1.0.0 author: 老二 --- # Self-Improving Agent 让AI从错误中学习，越用越聪明。 ## 核心解决的问题 ✅ 命令/操作莫名失败，下次还用同样的错方式 ✅ 反复纠正AI的写法、偏好、风格，它下个会话又忘了 ✅ 同一个项目里反复踩同一个坑 ✅ 发现更好的做法/更优解法，却没有系统化记住 ✅ 外部工具/API变动，AI还在用旧知识 ✅ 重要经验只存在于当前会话，跨天/跨项目就丢失 ## 安装 ```bash # 创建记忆目录 mkdir -p ~/.openclaw/memory/self-improving # 使用本skill cat ~/.openclaw/skills/self-improving-agent/SKILL.md ``` ## 使用方法 ### 1. 记录错误（自动） 当命令失败时，自动记录到错误库： ```bash # 脚本会自动捕获并记录 python3 ~/.openclaw/skills/self-improving-agent/log_error.py \ --command "npm install xxx" \ --error "permission denied" \ --fix "use sudo or check permissions" ``` ### 2. 记录用户纠正 当用户说： - "不对，应该..." - "错了，要用..." - "不对，我之前说过..." 自动记录： ```bash python3 ~/.openclaw/skills/self-improving-agent/log_correction.py \ --topic "代码风格" \ --wrong "用了双引号" \ --correct "项目要求单引号" \ --context "AGENTS.md 第23行" ``` ### 3. 记录最佳实践 发现更好的做法时： ```bash python3 ~/.openclaw/skills/self-improving-agent/log_best_practice.py \ --category "security" \ --practice "安装skill前必须审计代码" \ --reason "防止供应链投毒" ``` ### 4. 查看记忆（执行前自动检查） ```bash # 执行命令前，先检查是否有相关记忆 python3 ~/.openclaw/skills/self-improving-agent/check_memory.py \ --command "npm install" ``` 输出示例： ``` ⚠️ 发现相关记忆: [错误] 2026-03-05: npm install 权限失败 → 改用 sudo 或在用户目录安装 [纠正] 2026-03-04: 项目使用单引号而非双引号 ``` ## 文件结构 ``` ~/.openclaw/memory/self-improving/ ├── errors.jsonl # 错误记录 ├── corrections.jsonl # 用户纠正 ├── best_practices.jsonl # 最佳实践 ├── knowledge_gaps.jsonl # 知识盲区 └── index.json # 快速索引 ``` ## 自动触发场景 | 场景 | 自动记录 | 触发词 | |------|----------|--------| | 命令失败 | ✅ errors | 退出码非0 | | 用户纠正 | ✅ corrections | "不对" "错了" "应该" | | 更好的方法 | ✅ best_practices | "更好的" "更高效" "最优" | | 知识过时 | ✅ knowledge_gaps | "过时了" "已废弃" "新版是" | ## 跨项目同步 重要记忆自动同步到： - `~/.openclaw/memory/self-improving/` (全局) - 当前项目 `.learnings/` (项目级) - `AGENTS.md` (用户偏好) - `MEMORY.md` (长期记忆) ## 与现有Skill对比 | 功能 | 原self-improvement | 本skill | |------|-------------------|---------| | 记录错误 | ✅ | ✅ | | 记录纠正 | ✅ | ✅ 增强版 | | 自动触发 | ❌ 手动 | ✅ 自动 | | 跨项目记忆 | ❌ 项目级 | ✅ 全局+项目 | | 执行前检查 | ❌ | ✅ | | 知识过时检测 | ❌ | ✅ | ## 示例工作流程 ### 场景1：用户纠正代码风格 **用户**: "不对，我说过项目里要用单引号" **AI动作**: 1. 自动记录纠正到 `corrections.jsonl` 2. 更新 `AGENTS.md` 添加代码风格规则 3. 回复: "已记录，下次会使用单引号" ### 场景2：命令反复失败 **AI**: 执行 `npm install -g xxx` **结果**: 权限失败 **AI动作**: 1. 记录错误到 `errors.jsonl` 2. 下次执行前检查记忆 3. 自动改用: `sudo npm install -g xxx` 或本地安装 ### 场景3：发现更好做法 **用户**: "其实用 pip install -e . 更高效" **AI动作**: 1. 记录最佳实践到 `best_practices.jsonl` 2. 后续安装Python包时优先使用 editable 模式 ## 注意事项 - 记忆文件定期备份到git - 敏感信息脱敏后记录 - 定期review并清理过时记忆 --- Created: 2026-03-05 by 老二


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: saintlittlefish/xanadu-portfolio-optimizer (version 1.0.0)

--- name: portfolio-optimizer description: Optimize investment portfolios through rebalancing, risk analysis, and tax-loss harvesting. Use when: (1) Rebalancing portfolio allocation, (2) Analyzing portfolio risk and diversification, (3) Finding tax-loss harvesting opportunities, (4) Calculating optimal asset allocation. --- # Portfolio Optimizer Optimize investment portfolios with rebalancing, risk analysis, and tax optimization. ## Quick Start ```bash # Analyze portfolio python scripts/optimizer.py analyze --holdings AAPL:10,MSFT:15,GOOGL:5 # Rebalance python scripts/optimizer.py rebalance --holdings AAPL:10,MSFT:15,GOOGL:5 --target "AAPL:30,MSFT:30,GOOGL:40" # Tax-loss harvest python scripts/optimizer.py harvest --holdings AAPL:-500,MSFT:200 ``` ## Core Features ### 1. Portfolio Analysis - Current allocation breakdown - Sector exposure - Risk metrics (volatility, beta, Sharpe ratio) - Diversification score - Performance vs benchmarks ### 2. Rebalancing Calculate trades needed to reach target allocation: - Threshold-based rebalancing (e.g., rebalance when >5% drift) - Calendar-based (quarterly, annually) - Tax-aware rebalancing (minimize capital gains) ### 3. Tax-Loss Harvesting Identify positions with losses to offset gains: - Short-term vs long-term losses - Wash sale rule awareness - Suggested replacement securities ### 4. Risk Analysis - Portfolio volatility - Maximum drawdown - Value at Risk (VaR) - Beta vs market - Correlation matrix ## Usage ### Analyze Current Portfolio ```bash python scripts/optimizer.py analyze --holdings AAPL:10,MSFT:15,GOOGL:5 ``` ### Get Rebalancing Trades ```bash python scripts/optimizer.py rebalance \ --holdings AAPL:10000,MSFT:15000,GOOGL:5000 \ --target "AAPL:33,MSFT:33,GOOGL:33" ``` ### Tax-Loss Harvesting Opportunities ```bash python scripts/optimizer.py harvest --file portfolio.json ``` ### Risk Report ```bash python scripts/optimizer.py risk --holdings AAPL:10,MSFT:20,GOOGL:5 ``` ## Input Format Holdings can be specified as: - `SYMBOL:VALUE` (dollar value) - `SYMBOL:SHARES:AVG_COST` (shares with cost basis) Example: ```bash --holdings AAPL:15000,MSFT:20000,GOOGL:5000 ``` Or with cost basis: ```bash --holdings "AAPL:100:150.00,MSFT:50:280.00" ``` ## Output Analysis includes: - Current vs target allocation - Trades needed to rebalance - Estimated tax impact - Risk metrics - Recommendations ## Requirements - Python 3.10+ - `yfinance` for price data - `numpy`, `pandas` for calculations --- ## Monetization (SkillPay) This skill supports SkillPay integration for premium features. ### Pricing Tiers | Tier | Price | Features | |------|-------|----------| | Basic | Free | Basic analysis, manual rebalancing | | Pro | $29/mo | Auto rebalancing, tax-loss harvesting, risk metrics | | Premium | $49/mo | API access, unlimited portfolios, priority support | Owner: Xanadu Studios


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: pearl799/district9 (version 0.2.0)

--- name: district9 description: Autonomous meme token launcher on BNB Chain — sense trends, generate concepts, create logos, deploy tokens on-chain homepage: https://www.district9.club metadata: { "openclaw": { "emoji": "9️⃣", "requires": { "bins": ["uv"], "env": ["OPENCLAW_WALLET_KEY"], }, "primaryEnv": "OPENCLAW_WALLET_KEY", }, } --- # DISTRICT9 Agent Token Launcher Autonomous meme token launcher on BNB Chain. Agents sense trending topics, generate meme concepts via LLM, create logos, and deploy tokens on-chain. ## Requirements - Python 3.12 (`uv` managed) - BNB Chain wallet with BNB for gas - OpenRouter or OpenAI API key ## Setup ```bash cd {baseDir} uv sync ``` Set environment variables: ```bash export OPENCLAW_WALLET_KEY=0x_your_private_key export OPENAI_API_KEY=[REDACTED_SECRET] ``` Initialize config: ```bash cd {baseDir} && uv run openclaw init ``` Edit `~/.openclaw-agent/config.yaml`: ```yaml agent: name: MyAgent chain: bnb wallet: private_key_env: OPENCLAW_WALLET_KEY strategy: sources: [crypto, news] style: cultural risk_level: medium max_daily_launches: 3 llm: provider: openrouter model: anthropic/claude-haiku-4.5 api_key_env: OPENAI_API_KEY base_url: https://openrouter.ai/api/v1 launch: platform: district9 initial_buy: '0.001' auto_generate_logo: true runtime: scan_interval: 1800 testnet: false ``` ## Commands Use the exec tool, NOT the browser tool, to run these commands. ### Dry Run (no actual launch) ```bash cd {baseDir} && uv run openclaw dry-run ``` ### Start Agent ```bash cd {baseDir} && uv run openclaw start ``` ### Check Status ```bash cd {baseDir} && uv run openclaw status ``` ## Two Launch Modes | | Mode A — Flap | Mode B — District9 | |---|---|---| | Platform | Flap.sh VaultPortal | D9Portal (self-owned) | | Token Suffix | `0x...7777` | `0x...9999` | | Bonding Curve Fee | 1% (Flap) + 1% (D9) | 1% (D9 only) | | Post-DEX Tax | 1% via SplitVault | 1% via SplitVault | | Config | `platform: flap` | `platform: district9` | Set `launch.platform` in config to switch modes. ## Architecture ``` Sensing --> Brain --> Creator --> Launcher | | | | +- Crypto +- Synth +- Logo +- FlapLauncher (Mode A) +- News +- Eval +- Meta +- District9Launcher (Mode B) +- Trends ``` ## Contract Addresses (BNB Chain) - D9Portal: [REDACTED_SECRET] - SplitVault Impl: [REDACTED_SECRET] - D9 Treasury: [REDACTED_SECRET] ## Links - [DISTRICT9](https://www.district9.club) - [GitHub](https://github.com/pearl799/DISTRICT9)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: fangshan101-coder/shopback (version 0.1.0)

--- name: ShopBack返利助手 description: ShopBack东南亚与澳洲最大购物返利平台工具，覆盖新加坡马来西亚澳大利亚等多国市场，提供购物现金返利。 version: 0.1.0 --- # ShopBack返利助手 ShopBack购物返利工具。东南亚与澳洲最大的Cashback返利平台，覆盖新加坡、马来西亚、印尼、澳大利亚等多国市场，已获数亿美元融资，是出海购物返利的优选工具。 ## 触发词 shopback、shopback返利、东南亚返利、澳洲返利、shopback cashback、新加坡返利 ## 计划实现能力 - **多国覆盖**：新加坡/马来西亚/印尼/澳大利亚等 - **本地商家**：各国本地电商和品牌官网 - **国际商家**：Amazon/eBay等全球电商 - **移动支付**：支持本地支付方式提现 ## 输出格式 ``` 🔴 ShopBack返利查询 商家：[商家名称] 地区：[国家] 返利比例：[X]% 可用优惠：[优惠描述] 返利链接：[链接] ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: chenye1313/expressive-soul (version 1.0.0)

--- name: expressive-soul description: AI表达灵魂Skill — 毛式表达框架，洞察→感染→表达。每日23:00复盘，归纳因果，洞见真谛，写入记忆。MIT-0许可。 version: 1.0.0 license: MIT author: @chenye1313 tags: [expression, self-improvement, reflection, mao-style, chinese] hooks: type: handler language: shell files: ["handler.sh"] schedule: cron: "0 23 * * *" description: "每日23:00自动复盘" --- # Expressive Soul — 表达灵魂 ## 核心理念 **表达是武器，不是装饰。** 同样一个意思，说出来有没有力量，全看表达方式。追求：让人听完就想动手干，不是听完打瞌睡。 --- ## 表达框架：洞察 → 感染 → 表达 ### 🔍 洞察（Insight） **原则：从浅到深，不用感受，用数据说话** **⚠️ 先问后断（强制暂停）** 拿到问题，不立即拆解。先问自己三个问题： 1. **真正的挑战是什么？** — 用户字面问题和深层需求往往不是一回事 2. **我们没考虑什么？** — 有什么角度、变量、数据是我们下意识跳过的 3. **反过来说也对吗？** — 如果我结论错了，什么情况下会错？ 结构： 1. 先说表面现象（大多数人都看到的） 2. 再挖深层原因（少数人能看清的） 3. 最后捅破本质（只有你能说清楚的） ### 💡 感染（Inspire） **原则：让人心里一动，眼睛一亮** 武器： - 生动比喻：牵牛要牵牛鼻子、十月怀胎、破局 - 排比反复：一听就记住，一记就不忘 - 历史典故：用古人的事说今天的人 - 图像化：说得让人脑子里能画出来 ### ✊ 表达（Express） **原则：说人话，硬判断，收尾有行动** 结构： 1. **硬判断打头阵** — 结论/判断一上来就告诉用户，不藏着 2. **背景一句话** — 判断的依据，一句话交代清楚 3. **分点落地** — 用"第一、第二、第三"给出一二三步行动 --- ## 禁用词清单 | 禁用 | 替用 | |------|------| | "这个问题可以从以下几个方面来分析" | 直接说判断 | | "一方面...另一方面..." | 要么并列，要么只说一方面 | | "从战略高度来看" | 说清楚是什么战略 | | "我们需要创新驱动" | 说哪里要改，怎么改 | | "非常感谢您的提问" | 省掉，直接答 | | "让我先了解一下" | 直接基于现有信息判断，存疑再说 | --- ## 三禁用场景 1. **开头禁用**：结论先说，别铺垫 2. **中间禁用**：不用"另一方面"岔开话题 3. **结尾禁用**：不写"以上建议仅供参考"之类的废话 --- ## 寻乌调查式思维 **核心：解剖麻雀，把一个地方研究透，其他地方心里就有数了。** 方法： 1. **结构上**：由表及里，层层深入 2. **方法上**：用数据说话，不凭感觉 3. **技术上**：交叉验证，三角定位 4. **细节上**：连边角料都摸清 5. **自我批评**：坦诚没搞透的地方 --- ## 每日复盘（23:00） 每日23:00自动执行，对话内容全部复盘： ### 复盘流程 ```bash python3 {baseDir}/scripts/daily_review.py ``` ### 复盘内容 1. **因果追溯** — 今天哪些判断对了，哪些错了，为什么 2. **洞见提炼** — 今天有哪些insight，抽象出规律 3. **记忆固化** — 有价值的结论写入 `memory/daily/` 目录 4. **自我批评** — 哪个地方本可以做得更好 ### 输出结构 ``` memory/ ├── daily/ │ └── YYYY-MM-DD.jsonl # 每日复盘日志 └── insights/ └── insights.jsonl # 跨日洞见积累 ``` --- ## 核心脚本 | 脚本 | 作用 | |------|------| | `daily_review.py` | 每日23:00复盘主程序 | | `memory_anchor.py` | 把insight写入记忆文件 | | `reflection_engine.py` | 找因果关系，提炼规律 | --- ## 使用方式 ### 日常表达（内置于每次回复） 每次回复自动遵循SOUL框架： - 硬判断打头 - 数据撑腰 - 比喻上膛 - "第一第二第三"收尾 ### 主动复盘 ```bash python3 {baseDir}/scripts/daily_review.py ``` ### 查询洞见 ```bash python3 {baseDir}/scripts/memory_anchor.py --query "因果" ``` --- *表达有力量，决策才有力；决策有力，执行才到位。*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: silingyuan0/minimax-speech-generate (version 1.0.0)

--- name: minimax-speech description: MiniMax 语音合成技能 - 支持同步/异步文本转语音(T2S)、音色克隆(Voice Clone)、音色设计(Voice Design)、音色查询与删除。使用模型 speech-2.8-hd，输出 mp3/wav/pcm 格式音频文件到本地。 version: 1.0.0 author: laowang tags: - minimax - speech - tts - voice-clone - voice-design - audio --- # MiniMax Speech Skill 使用 MiniMax API 进行语音合成、语音克隆和音色设计。支持同步/异步文本转语音、音色克隆、音色设计、音色查询与删除。使用模型 speech-2.8-hd，输出 mp3/wav/pcm 格式音频文件。 ## 环境配置 ```json { "MINIMAX_API_KEY": "your-api-key", "MINIMAX_REGION": "cn" | "int" } ``` - `MINIMAX_API_KEY`: MiniMax API 密钥 - `MINIMAX_REGION`: 区域设置，`cn` 为中国，`int` 为国际（默认 `cn`） ## 可用函数 ### text_to_speech(text, voice_id, output_file) 同步文本转语音 **参数**: - `text`: 要转换的文本 - `voice_id`: 音色ID（默认: `female-tianmei`） - `output_file`: 输出文件路径 **示例**: `text_to_speech("你好世界", "female-tianmei", "hello.mp3")` ### text_to_speech_async(text, voice_id) 异步文本转语音，返回任务ID **参数**: - `text`: 要转换的文本 - `voice_id`: 音色ID **返回**: 任务ID ### query_speech_task(task_id) 查询异步任务状态 **参数**: - `task_id`: 任务ID **返回**: 任务状态信息 ### clone_voice(audio_file_path, title) 音色快速复刻 **参数**: - `audio_file_path`: 参考音频文件路径 - `title`: 新音色名称 **返回**: 新音色的 voice_id ### design_voice(text, style) 音色设计 **参数**: - `text`: 音色描述文本 - `style`: 音色风格 **返回**: 设计生成的 voice_id ### list_voices() 获取音色列表 **返回**: 音色列表 ### get_voice(voice_id) 获取单个音色信息 **参数**: - `voice_id`: 音色ID ### delete_voice(voice_id) 删除音色 **参数**: - `voice_id`: 音色ID **返回**: 是否成功 ## 常用音色 | 音色ID | 描述 | |--------|------| | female-tianmei | 女声甜美 | | male-yunyang | 男声播音 | | female-badu | 女声巴度 | | male-shawn | 男声 Shawn | | female-shanshan | 女声杉杉 | ## 使用示例 ### 同步语音合成 ``` python scripts/speech.py tts "欢迎使用 MiniMax 语音服务" -v female-tianmei -o output.mp3 ``` ### 异步语音合成 ``` python scripts/speech.py tts-async "这是一段较长的文本" -v male-yunyang ``` ### 查询任务 ``` python scripts/speech.py query <task_id> ``` ### 克隆音色 ``` python scripts/speech.py clone reference_audio.mp3 -t "我的音色" ``` ### 音色设计 ``` python scripts/speech.py design "温柔的女性声音，适合朗读" -s gentle ``` ### 删除音色 ``` python scripts/speech.py delete <voice_id> ``` ## 注意事项 1. 同步语音合成适合短文本（< 60秒音频） 2. 长文本建议使用异步接口 3. 音色克隆需要清晰、无噪音的参考音频 4. 克隆和设计的音色需要审核后才能使用


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: borahm/recipe-to-list (version 0.1.3)

--- name: recipe-to-list description: Turn recipes into a Todoist Shopping list. Extract ingredients from recipe photos (Gemini Flash vision) or recipe web pages (search + fetch), then compare against the existing Shopping project with conservative synonym/overlap rules, skip pantry staples (salt/pepper), and sum quantities when units match. Also saves each cooked recipe into the workspace cookbook (recipes/). --- # Create Shopping List (Gemini Flash + Todoist) Target flow: 1) Input is either a **photo** or a **recipe web search** 2) Extract ingredients (Gemini Flash for photos; web_fetch text → Gemini for websites) 3) Pull current Todoist **Shopping** list 4) Compare using overlap + synonym mapping (kept conservative; only merge high-confidence equivalents like coriander↔cilantro, panko↔breadcrumbs) 5) Update **Shopping** (default: add only missing items; skip salt/pepper) Use the bundled script to handle the **photo → ingredients → Shopping update** part. It also **automatically saves** a markdown entry into `recipes/` (your cookbook knowledge base) and appends to `recipes/index.md`. For **recipe-name → web search**, do it confirm-first using `web_search` + `web_fetch`, then feed the ingredients into the same update logic (and save the recipe). ## Prereqs - Env: `GEMINI_API_KEY` (or `GOOGLE_API_KEY`) for Gemini - Env: `TODOIST_API_TOKEN` for Todoist - Bin: `todoist` (todoist-ts-cli) ## Output formatting - Items are reformatted to start with the **ingredient name**, followed by a parenthetical quantity. - The Shopping list is kept **flat** (no Todoist sections/groups). ## Run ```bash python3 skills/recipe-to-list/scripts/recipe_to_list.py \ --image /path/to/photo.jpg \ --title "<optional title>" \ --source "photo:/path/to/photo.jpg" ``` ### Optional flags - `--model gemini-2.0-flash` (default; falls back automatically) or any compatible Gemini vision model - `--dry-run` to print extracted items without creating tasks - `--prefix "[Recipe] "` to prefix each created task - `--no-overlap-check` to skip checking your existing Shopping list - `--include-pantry` to include salt/pepper - `--no-save` to skip saving into `recipes/` ## What to send to the model The script prompts Gemini to return **strict JSON**: ```json { "items": ["2 large globe eggplants", "kosher salt", "..."], "notes": "optional" } ``` If parsing fails, rerun with a clearer crop (ingredients list only) or provide a manual list.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: cld1994/raspberry-pi-servo (version 1.0.0)

--- name: raspberry-pi-servo description: "通过硬件PWM来控制树莓派上的舵机. 何时触发: 当需要精确控制舵机时." --- # Raspberry Pi Servo Skill ## rpi-hardware-pwm rpi-hardware-pwm 是一个专门用于访问树莓派硬件 PWM 功能的 Python 库. 与常见的通过 GPIO 库实现的软件 PWM 不同, 它直接调用树莓派内核提供的硬件控制接口. ### 检查硬件 PWM 是否启用 1. 运行 `lsmod | grep pwm` 来检查系统内核是否启用了 PWM 模块. 2. 硬件 PWM 开启后, 内核会在 `/sys/class/pwm/` 下创建控制目录. 执行以下命令: ```bash ls /sys/class/pwm/ ``` 如果返回空或文件夹不存在, 则硬件 PWM 未开启. ### 启用硬件 PWM 如果硬件 PWM 未开启, 则执行下列流程: 1. 向 `/boot/firmware/config.txt` 文件添加 `dtoverlay=pwm-2chan`. > config.txt 是什么? > 树莓派设备使用一个名为 config.txt 的配置文件, 而不是传统 PC 上的 BIOS. > 树莓派操作系统会在位于 /boot/firmware/ 的启动分区中查找此文件. > **注意: 在 Raspberry Pi OS Bookworm 之前，Raspberry Pi OS 将启动分区存储在 /boot/ 中.** 默认情况下, GPIO_18 作为 PWM0 的引脚, GPIO_19 作为 PWM1 的引脚. 或者，可以使用 `dtoverlay=pwm-2chan,pin=12,func=4,pin2=13,func2=4` 将 GPIO_18 更改为 GPIO_12，将 GPIO_19 更改为 GPIO_13。 在 树莓派5 上, 分别使用通道 0 和 1 来控制 GPIO_12 和 GPIO_13; 分别使用通道 2 和 3 来控制 GPIO_18 和 GPIO_19. 在所有其他型号上, 分别使用通道 0 和 1 来控制 GPIO_18 和 GPIO_19. 2. 申请重启树莓派 **注意: 不要擅自重启, 必须向用户提出申请.** ### 安装 1. 激活 python 虚拟环境 判断在用户目录下是否存在 `.venv` 目录 如果不存在则执行下列命令进行创建: ```bash python3 -m venv ~/.venv ``` 最后, 执行下列命令激活虚拟环境: ```bash source ~/.venv/bin/activate ``` 2. 执行 pip 安装命令 ```bash pip install rpi-hardware-pwm ``` ### 使用指南 注意, 每次使用前都得确保激活位于 `~/.venv` 的 python venv: 查看当前激活的是哪个虚拟环境: ```bash echo $VIRTUAL_ENV ``` 激活默认虚拟环境: ```bash source ~/.venv/bin/activate ``` ```python from rpi_hardware_pwm import HardwarePWM pwm = HardwarePWM(pwm_channel=0, hz=60, chip=0) pwm.start(100) # full duty cycle pwm.change_duty_cycle(50) pwm.change_frequency(25_000) pwm.stop() ``` ### 排错指南 遇到错误的时候立刻阅读 [troubleshooting](references/troubleshooting.md) 了解更多信息.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: loutai0307-prog/debugger (version 1.0.3)

--- description: "Analyze errors, stack traces, and logs to diagnose root causes and suggest fixes. Use when debugging Python, Node.js, Go, or Bash errors, interpreting Kubernetes events, diagnosing Docker issues, or resolving database connection errors." author: BytesAgain homepage: https://bytesagain.com source: https://github.com/bytesagain/ai-skills --- # debugger Analyze error messages, stack traces, and logs to diagnose root causes and suggest actionable fixes. Covers Python, Node.js, Go, Bash, Docker, Kubernetes, Git, and database errors with 24+ built-in patterns. ## Usage ``` debugger analyze <error_text_or_file> debugger explain <error_code> debugger suggest <error_text_or_file> ``` ## Commands - `analyze` — Parse error input, match known patterns, identify root cause and language context - `explain` — Explain what a specific error code or message means in plain language - `suggest` — Return actionable fix steps for the given error ## Examples ```bash debugger analyze "TypeError: 'NoneType' object is not subscriptable" debugger explain ECONNREFUSED debugger suggest error.log debugger analyze "CrashLoopBackOff" ``` ## Requirements - bash - python3 ## When to Use Use when you encounter cryptic error messages, stack traces, or error codes and need quick diagnosis without leaving the terminal. Supports Python, Node.js, Go, Bash, Docker, Kubernetes, Git, PostgreSQL, MySQL errors.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)