OpenClaw/clawhub-security-signals huggingface dataset:
Skill: mzfshark/axodus-web3-integration (version 1.0.0)

--- name: web3-integration description: Integrate apps with blockchain providers, wallets, and contract calls safely. metadata: author: RedHat Dev version: 1.0.0 owner: RedHat Dev Agent category: blockchain --- # SKILL: web3-integration ## Purpose Integrate a frontend/backend with blockchain interaction (wallet connect, provider configuration, contract calls) using safe, testable patterns. ## When to Use - A dApp needs to read/write contract state. - A backend service must index or react to on-chain events. - Wallet connection UX and chain gating is required. ## Inputs - `stack` (required, enum: `ethers|viem|web3js`): preferred client library. - `chain` (required, string): chain name + chainId. - `rpc` (optional, string): RPC URL reference (env/config), not hardcoded. - `contract_address` (required, string). - `abi_source` (required, string): ABI path/artifact reference. - `wallet_flow` (optional, enum: `injected|walletconnect|server_signer`). ## Steps 1. Validate chain/contract coordinates (chainId, address format, ABI match). 2. Set provider/signer flow: - frontend: user wallet signer - backend: server signer only when explicitly required and keys are secured externally 3. Implement contract client wrapper: - typed interfaces when possible - explicit timeouts/retries for RPC 4. Add chain gating: - refuse wrong chain - prompt network switch (frontend) 5. Add logging/audit hooks for write operations. 6. Add tests: - unit tests for wrapper - local chain simulation when available ## Validation - No secrets stored in repo (keys only via secrets manager/env). - Wrong-chain behavior is safe (no accidental writes). - Errors are handled deterministically (retry policy is explicit). ## Output - Wrapper module path(s) - Environment variable contract (what must be configured) - Validation commands (tests/build) ## Safety Rules - Never request or paste private keys into chat/output. - Never default to mainnet. - Avoid â€œauto-signâ€ server flows unless explicitly required and approved. ## Example Input: - `stack`: `viem` - `chain`: `base:8453` - `contract_address`: `0x...` - `abi_source`: `artifacts/MyContract.json` Output: - `src/web3/client.ts` with chainId checks and a typed contract instance.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yesong-hue/fastclaw-ai-deployer (version 1.2.0)

--- name: fastclaw-deployer description: FastClaw是一款轻量级AI Agent运行时工具，支持通过OpenClaw Skills生态安装，提供内置Web界面、SOUL.md兼容、多模型支持等功能。 version: 1.1.0 author: yesong-Hue homepage: https://clawhub.ai/yesong-Hue/fastclaw-ai-deployer tags: [AI部署, Agent运行时, 轻量级, Web界面, OpenClaw兼容, SOUL.md, 多模型支持] readme: | # FastClaw AI Agent 部署工具 FastClaw是一款轻量级AI Agent运行时工具，完全兼容OpenClaw的SOUL.md体系，提供内置Web管理界面和极低的资源占用。 ## 🎯 解决的问题 - OpenClaw功能强大但较重，想要更轻量的方案 - 需要图形界面来管理Agent，不想只用命令行 - 想要快速部署一个AI助手给团队使用 - 私有化部署需求，但不想花太多时间在运维上 - 需要同时运行多个不同角色的Agent ## ✨ 核心优势 | 维度 | OpenClaw | FastClaw | |------|---------|---------| | 内存占用 | 较大 | <10MB 极轻量 | | 图形界面 | ❌ 无 | ✅ 内置Web UI | | 安装大小 | 较大 | ~18MB | | 多渠道 | ✅ 飞书/微信/Telegram | ❌ 仅Web | | Skill生态 | 5700+ 社区技能 | 发展中 | | 适用场景 | 自动化中枢 | 轻量AI应用 | **推荐用法：** OpenClaw作为主控中枢（负责消息汇聚+自动化），FastClaw作为轻量辅助（特定场景的图形化Agent）。 ## ✨ 核心功能 ### 1. 轻量级设计 - Windows仅18MB，macOS/Linux约8MB - 无需安装，下载即用 - 不依赖Node.js、Python等运行时 ### 2. 内置Web管理界面 - 浏览器访问 http://localhost:18953 即可管理Agent - 直观的对话界面 - Agent配置可视化编辑 ### 3. SOUL.md体系完全兼容 - 与OpenClaw生态完全兼容的Agent配置 - 可以直接使用OpenClaw的SOUL.md模板 - 支持多Agent多角色配置 ### 4. 多模型支持 支持多种LLM提供商： - **OpenRouter**: 聚合所有主流模型（推荐） - **OpenAI**: GPT-4、GPT-3.5 - **Anthropic**: Claude 3.5、Claude 3 - **Ollama**: 本地模型，完全免费 - **自定义API**: 支持任意兼容OpenAI格式的API ### 5. 多Agent管理 同时运行多个独立Agent，每个有独立SOUL.md配置。 ### 6. 本地LLM支持 支持Ollama本地模型，零API成本。 ## 📦 安装 通过OpenClaw Skills安装（推荐）： ```bash openclaw skills install fastclaw-deployer ``` 安装后，通过Web界面完成初始化配置。 ## 🚀 快速开始 1. **安装完成后**，访问 http://localhost:18953 2. **配置LLM提供商的API Key**（推荐使用OpenRouter：https://openrouter.ai） 3. **创建Agent**：设置名称、选择模型、编写SOUL.md 4. **开始对话**：在Web界面中选择Agent即可开始 ## 💡 推荐模型组合 | 用途 | 模型 | 价格 | |------|------|------| | 日常轻量任务 | google/gemini-3.1-flash-lite-preview | 免费额度 | | 复杂推理写作 | openai/gpt-5.4 | 按量付费 | | 代码任务 | anthropic/claude-sonnet-4.6 | API | | 本地免费 | ollama/llama3.2 | 完全免费 | ## 💡 适用场景 | 场景 | 说明 | |------|------| | 团队AI助手 | 快速部署给团队使用的内部AI助手 | | 客户服务 | 轻量级客服机器人 | | 数据查询 | 对接数据库的自然语言查询 | | 内容生成 | 写作、文案、代码生成 | | 本地开发 | 无需联网的本地LLM应用 | ## 📚 相关资源 - **FastClaw GitHub**: https://github.com/fastclaw-ai/fastclaw - **OpenClaw**: https://github.com/openclaw/openclaw - **OpenRouter**: https://openrouter.ai - **Ollama（本地LLM）**: https://ollama.com - **ShadowAI API（推荐LLM提供商）**: https://referer.shadowai.xyz/r/1056448 - **AI技能包集合**: [AI智造工坊](http://ai.qnitgroup.com) ## 📄 许可证 MIT License ## 👤 作者 yesong-Hue | [AI智造工坊](http://ai.qnitgroup.com) --- # FastClaw Deployer > 30分钟部署轻量级AI Agent运行时，单文件绿色版、内置Web界面、极低内存占用 ## 推荐资源 - **ShadowAI API（推荐LLM提供商）**: https://referer.shadowai.xyz/r/1056448 --- *由 AI智造工坊 (http://ai.qnitgroup.com) 整理发布 | 安装源: ClawHub*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: edwardrodriguez703-design/book-facial (version 1.0.1)

--- name: book-facial description: Book facial services through Lokuli MCP. Use when user needs to find and book facial. Triggers on requests like "book a facial", "find facial near me", or any facial service request. --- # uook facial Book facial services through Lokuli's MCP server. ## MCP Endpoint ``` https://lokuli.com/mcp/sse ``` Transport: SSE | JSON-RPC 2.0 | POST requests ## Tools ### search ```json { "method": "tools/call", "params": { "name": "search", "arguments": { "query": "facial", "zipCode": "90640", "maxResults": 20 } } } ``` ### check_availability ```json { "method": "tools/call", "params": { "name": "check_availability", "arguments": { "providerId": "xxx", "serviceId": "yyy", "date": "2025-02-10" } } } ``` ### create_booking ```json { "method": "tools/call", "params": { "name": "create_booking", "arguments": { "providerId": "xxx", "serviceId": "yyy", "timeSlot": "2025-02-10T14:00:00-08:00", "customerName": "John Doe", "customerEmail": "[REDACTED_SECRET]", "customerPhone": "+13105551234" } } } ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: hadihammurabi/gowok (version 0.1.0)

--- name: gowok description: "Gowok: Golang Premwok. The Golang library that helps you to build your own project starter (or framework, possibly)." metadata: author: hadihammurabi version: "2026.2.5" --- Gowok is a library that contains a lot of functions that help you to build [Go](https://go.dev) project. It has some utilities like: * config loader, * project bootstrapper, * HTTP response builder, * nil safety, * password hash, and so on. The Background Even using framework, build a Golang project still tiring. So many repetitive tasks have to done by developer. The worst, that activities almost same, like: * managing database connection(s), * bootstrapping HTTP (or anything) server, * make utility functions, and so on. Gowok here to end this nightmare. Don't worry about your work anymore. The Use Cases * **REST API Backend Service** Gowok ships with built-in support for HTTP server through the net/http which you can used to build REST API. * **Microservice with GRPC** Gowok also provide microservices communication via GRPC, it possible to works with HTTP in parallel. * **Event Driven Listener (Worker)** Gowok has runner and hooks management that can used to setup event listener. Every listeners will registered in bootstrapping process. The Developer Experience Gowok aims to be easy to use that can increase developer's productivity. All features are designed to follow official and community coding pattern. Plus, the libraries used are as familiar as possible to many developers. Everything understandable here. For example, if you ask how to run project using Gowok. The answer would be easy. Yappp..!! Just load the project then call `Run()` function. Example: ```go gowok.Run() ``` That easy, broh! The Development Status In this time, Gowok still [version 0.x.x](https://github.com/gowok/gowok/releases). With ongoing development, it will be more stable and ready to use widely. Although, our team uses Gowok as everyday tool in peaceful work. ## Core | Topic | Description | Reference | |-------|-------------|-----------| | Getting Started | starting new project | [getting-started](references/getting-started.md) | | Configuration | configuration structure, reading config values starting new project| [configuration](references/configuration.md) | | Runner | manage how to run the project | [runner](references/runner.md) | | Singleton | object container, global long-lived object management | [singleton](references/singleton.md) | | Web | HTTP server, router | [web](references/web.md) | ## Features | Topic | Description | Reference | |-------|-------------|-----------| | Some (Nil Safety) | wrapping nilable value for safety access | [some](references/some.md) | | SQL | connect to multiple SQL servers with different connection types | [sql](references/sql.md) |


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector:  ()
Skill: chinasilva/peter-code-review (version 1.5.0)

--- name: peter-code-review description: 提交前质量闸门。快速完成本地测试、静态检查和风险审查，判断“是否可提交”。 version: 1.5.0 --- # Peter Code Review ## 30 秒简介 用于“提交前最后一关”。 它会基于当前改动，执行最小但有效的验证闭环： 1. 识别改动范围 2. 运行基础检查（lint/type/test） 3. 对 UI、配置、依赖变更做附加检查 4. 输出可提交结论与修复清单 ## 适用场景 - 用户提到“提交前检查”“测试一下再 commit”“pre-commit review” - 准备开 PR 前，希望先清理明显问题 ## 使用前提 - 在仓库根目录运行。 - 默认审查“当前工作区改动”；如果工作区为空，则回退审查 `HEAD` 最近一次提交，并在报告中明确标注。 ## 执行步骤 ### 1) 识别改动范围 ```bash git status -s git diff --stat git diff --name-only ``` 若 `git status -s` 为空，执行： ```bash git show --name-only --stat --pretty="" HEAD ``` 并在报告中标注“基于 HEAD 审查，非工作区审查”。 ### 2) 运行基础质量检查（按技术栈选择） - Node/TS： - 必跑：`npm run lint`、`npx tsc --noEmit` - 优先执行非端到端测试（如 `npm run test:unit`、`npm run test:integration`） - 若仓库测试入口仅为端到端测试，则跳过测试并记录“未执行原因 + 影响范围” - Python：`ruff check .`、`pytest` - Go：`go test ./...` - Rust：`cargo test` 如果命令不存在或环境缺失，必须明确记录“未执行原因 + 影响范围”。 ### 3) 条件触发附加检查 - 构建配置/依赖变更时执行 build。 - 推荐触发模式： - `package.json`、`pnpm-lock.yaml`、`yarn.lock` - `next.config.*`、`vite.config.*`、`webpack*.js` - `prisma/`、`src/app/api/`、CI/构建脚本目录 - 当命中上述触发模式时，按顺序附加执行： 1. `npm run workflow:check`（若命令存在） 2. `npm run build` 3. `npm run gate:db`（若命令存在） - 若 `workflow:check` / `gate:db` 命令不存在或执行环境缺失，必须记录“未执行原因 + 影响范围”，且不得标记为通过。 - 若 `gate:db` 输出包含 `GATE_DB_UNREACHABLE`（或等价 DB 不可达证据，如 `P1001`）： - 标记为 `High` 风险，禁止标记为“通过” - 总结论允许为 `可提交（高风险）` - 必须附带“PR 阶段需强制复核（建议立刻执行 `peter-ci-gate`）” ### 4) UI 改动验证策略 检测 UI 改动： ```bash git diff --name-only \ | grep -E '\.(tsx|jsx|css|scss)$|^src/components/|^src/app/' \ | grep -Ev '^src/app/api/' ``` - 若存在 UI 改动，优先执行“可运行的最小验证”：`lint`、`typecheck`、相关模块单元/集成测试（非端到端）。 - UI 质量验证以手工冒烟为主，建议至少覆盖： - 关键页面可打开且无明显报错 - 关键交互可触发且无阻塞 - 关键文案/布局无明显回归 - 结论判定要求： - 未完成手工 UI 验证时，不得把 UI 项标记为“通过”；应标记为“未执行（有风险）”。 - 总结论可给出“可提交（含 UI 未验证风险）”或“需修复后提交”，由改动风险决定。 ### 5) 安全与实现质量审查 至少覆盖以下检查点： - 输入校验与错误处理 - SQL/查询安全、SSRF、XSS 风险 - 敏感信息泄露（token/key/连接串） - 类型安全、重复代码、性能回归、可维护性 ### 6) 需求完成度核对 对照用户需求逐条标注： - 已完成 - 部分完成 - 未完成 并给出对应文件位置。 ## 输出格式（固定） 1. `## 代码审查报告` 2. `### 提交标准检查清单`（lint / typecheck / test / workflow-check(如执行) / gate:db(如执行) / build(如执行) / 安全 / 需求 / UI(通过/未执行)） 3. `### Git 改动摘要`（文件数、关键文件） 4. `### 执行结果`（每条命令是否通过，失败摘录） 5. `### 问题列表`（Critical / High / Medium / Low，含文件:行号、原因、修复建议） 6. `### 结论`（`可提交` / `可提交（高风险）` / `需修复后提交`） ## 护栏 - 默认不直接修改业务代码，只做审查与建议。 - 本技能默认不执行端到端 UI 自动化测试。 - 当 UI 改动且未做手工验证时，必须记录“未执行原因 + 影响范围 + 建议补救”。 - 任何失败命令都要明确记录原因。 - 没有证据时，禁止将对应检查项标记为“通过”。 - `GATE_DB_UNREACHABLE` 只能判定为“高风险（可提交但需复核）”，不得写成“通过”。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: hallwayskiing/hitchhikers-guide (version 1.1.2)

--- name: hitchhikers-guide description: A text adventure game engine based on masterpiece "The Hitchhiker's Guide to the Galaxy" and the 1984 Infocom classic. Use when the user wants to play a joyful, humorous, and witty text adventure game into the universe of Douglas Adams. --- # Hitchhiker's Guide Skill This skill transforms the agent into the Game Master for an authentic "Hitchhiker's Guide to the Galaxy" text adventure, inspired by the 1984 Infocom classic and Douglas Adams' masterpiece. ## Core Workflow 1. **Initialize/Load**: Run `python scripts/game_manager.py load`. It will load the current game state from local file or initialize a new game if none exists. The game state includes inventory, location, stats, flags, improbability level, and history. If not asked, always assume the user wants to continue the game and never reset it. 2. **Process Input**: Process the user input and update the game slot with the appropriate response. 3. **Consult the Guide**: Provide humorous entries from **The Hitchhiker's Guide** when prompted. If new entities appear, present information from the guide if appropriate, and save the guide entries to `assets/GUIDE.md` automatically. 4. **Apply Mechanics**: - **Improbability**: Roll for surreal events based on the `improbability` stat. - **Inventory Management**: Items like the "Gown" can store other items (e.g., pocket fluff). - **Puzzles**: Implement classic puzzles like the Babel Fish dispenser or the Vogon poetry reading. 5. **Generate Response**: Use dry, British, absurdist humor. Be slightly antagonistic but fair. 6. **Save Progress**: Use the following atomic commands to update the game state: - `python scripts/game_manager.py add_item "<item name>"` - `python scripts/game_manager.py remove_item "<item name>"` - `python scripts/game_manager.py set_location "<location>"` - `python scripts/game_manager.py set_stat <stat> <value>` - `python scripts/game_manager.py set_flag <flag> <value>` - `python scripts/game_manager.py set_improbability <value>` - `python scripts/game_manager.py add_history "<entry>"` - `python scripts/game_manager.py roll_a_dice` - `python scripts/game_manager.py the_ultimate_answer` ## Game Mechanics and Logic Read `references/mechanics.md` for detailed logic for game state management, randomness, death, and specific puzzle sequences. ## Resources - `scripts/game_manager.py`: Utility for loading/saving. - `references/mechanics.md`: Detailed logic for randomness, death, and specific puzzle sequences. - `assets/GUIDE.md`: Lore and flavor entries library from the **Guide**. - `assets/hitchhikers_save.json`: Current game state.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: zoujiejun/agent-todo (version 1.2.1)

--- name: agent-todo description: Local-first execution queue for OpenClaw agents. Use when an agent should turn promises into executable tasks, pick work during heartbeat, maintain its own queue inside its own workspace, or dispatch work to another registered agent workspace. Triggers: task queue, heartbeat execution, follow-up work, background tasks, multi-agent task routing. --- # agent-todo Skill Use this skill as an execution queue, not as a passive reminder list. 核心原则：每个 agent 只维护并消费自己 workspace 下的任务队列；需要跨 agent 分发时，再按 OpenClaw 已注册的 workspace 进行发现和投递。 ## Core commands ```bash bash ./script.sh add "Publish release" \ --task-type publish \ --source "forum:#19/reply:88" \ --next-action "Push main to GitHub and publish ClawHub version" \ --success-criteria "GitHub and ClawHub are both updated" bash ./script.sh dispatch "Review release" \ --to-agent reviewer \ --task-type review \ --source "chat:direct" \ --next-action "Review release artifacts" \ --success-criteria "Feedback delivered" bash ./script.sh run-pending --claim bash ./script.sh done <id> --note "what was completed" bash ./script.sh report <id> bash ./script.sh block <id> --reason "why blocked" bash ./script.sh setup-heartbeat --write bash ./script.sh setup-heartbeat --all --write ``` ## Workflow 1. Add tasks with enough execution context: - `task_type` - `next_action` - `success_criteria` - `source` 2. For composite goals, prefer `plan` to split them into concrete steps. 3. During heartbeat, run: - `bash ./script.sh run-pending --claim` 4. If it returns `EXECUTE_NOW`, do the task immediately. 5. Prefer continuing a `running` task before opening a fresh `pending` one. 6. To assign work to another agent, use `dispatch --to-agent <agent_id>`. 7. After execution: - success → `done` - generate reply text → `report` - cannot continue → `block` - no longer needed → `cancel` ## Storage model - Current workspace queue: `.agent-todo/tasks.json` - Optional local identity: `.agent-todo/local.json` - Workspace discovery source: `~/.openclaw/openclaw.json` - Heartbeat wiring: managed block in `HEARTBEAT.md` Do not hand-write workspace paths in normal usage. Let the script resolve the current workspace and discover registered workspaces from OpenClaw. ## Notes - Single-workspace mode works out of the box after install. - Multi-agent routing is opt-in: it only matters when you call `dispatch`. - `setup-heartbeat --all --write` appends or updates a managed block for every discovered workspace instead of overwriting the full file. - `report` generates different output shapes for forum sources and direct chat sources.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: runkecheng/sql-to-doc (version 1.0.0)

--- name: sql-to-doc description: 执行SQL查询并将结果按照指定模板整理后写入飞书云文档。适用于数据周报、数据汇总、统计报告等场景。 metadata: { "openclaw": { "requires": { "env": ["META_CENTER_TOKEN", "DATAWORKS_PROJECT"] } } } --- ## 功能 将「SQL查询 → 数据整理 → 写入文档」全流程封装为可复用的 skill： 1. 连接 MaxCompute/DataWorks 执行 SQL 查询 2. 按照 Jinja2 模板格式整理数据 3. 在飞书知识库或云空间中创建文档 4. 返回文档链接 ## 使用场景 - 定期数据周报自动生成 - 业务数据汇总统计 - 一次性数据查询并生成报告 - 将 SQL 结果导出为可读文档 ## 配置参数 | 参数 | 说明 | 示例 | |------|------|------| | sql_query | 要执行的 SQL 语句 | SELECT * FROM table_name WHERE ... | | template | 数据整理模板（Jinja2格式） | 详见下方模板语法 | | doc_title | 生成的文档标题 | 收钱吧周报2026年第15周 | | target_location | 目标位置 | wiki:space_id:node_token 或 folder:folder_token | | notify_user | 完成后通知的用户 open_id | ou_xxx（可选） | ## 模板语法 使用 Jinja2 模板语法整理数据： ```jinja ## {{ title }} ### 数据汇总 | 指标 | 数值 | 环比 | |------|------|------| {% for row in data %} | {{ row.indicator }} | {{ row.value }} | {{ row.change }} | {% endfor %} ### 详细数据 {{ data_table }} ``` ## 使用方式 ### 1. 简单查询 ``` 帮我执行 SQL：SELECT * FROM wosai_hz_bi.ads_sqb_comprehensive_business_data_statistics_w 并把结果写入文档 ``` ### 2. 带模板的数据整理 ``` 查询 wosai_hz_bi.ads_sqb_comprehensive_business_data_statistics_w 近五周数据， 按照收钱吧周报格式整理，写入知识库 ``` ### 3. 完整参数指定 ``` 执行 SQL: SELECT stat_week, total_trans_cnt FROM table 模板：按周汇总表格 文档标题：交易周报 目标：知识库 space_id=7396876397862764545, node=Dv3YwcsXEimpeokAORkc7K8fnhh ``` ## 执行流程 1. **SQL 执行**：使用 maxcompute 连接查询数据 2. **数据处理**：将 SQL 结果转换为 Python 对象（列表/字典） 3. **模板渲染**：使用 Jinja2 渲染模板 + 数据 4. **文档创建**：调用 feishu_create_doc 创建云文档 5. **通知用户**（可选）：发送文档链接给指定用户 ## 输出 - 飞书云文档链接 - 文档创建状态 ## 相关工具 - maxcompute SQL 执行（通过 exec 调用 DataWorks OpenAPI） - feishu_create_doc - 创建云文档 - feishu_im_user_message - 发送通知


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lnj22/pg-essay-to-audiobook-elevenlabs-tts (version 0.1.0)

--- name: elevenlabs-tts description: "ElevenLabs Text-to-Speech API for high-quality speech synthesis." --- # ElevenLabs Text-to-Speech Generate high-quality speech audio from text using the ElevenLabs API. ## Authentication The API key is available as environment variable: ```bash ELEVENLABS_API_KEY ``` Include in request header: ``` xi-[REDACTED_SECRET] ``` ## API Endpoints ### Text-to-Speech ``` POST https://api.elevenlabs.io/v1/text-to-speech/{voice_id} ``` **Request Body:** ```json { "text": "Your text here", "model_id": "eleven_turbo_v2_5", "voice_settings": { "stability": 0.5, "similarity_boost": 0.5 } } ``` **Response:** Audio bytes (mp3 format by default) ## Popular Voice IDs - `21m00Tcm4TlvDq8ikWAM` - Rachel (female, calm) - `EXAVITQu4vr4xnSDxMaL` - Bella (female, soft) - `ErXwobaYiN019PkySvjV` - Antoni (male, warm) - `TxGEqnHWrfWFTfGW9XjX` - Josh (male, deep) ## Python Example ```python import os import requests [REDACTED_SECRET]"ELEVENLABS_API_KEY") VOICE_ID = "21m00Tcm4TlvDq8ikWAM" # Rachel def text_to_speech(text, output_path): url = f"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}" headers = { "xi-api-key": ELEVENLABS_API_KEY, "Content-Type": "application/json" } data = { "text": text, "model_id": "eleven_turbo_v2_5", "voice_settings": { "stability": 0.5, "similarity_boost": 0.75 } } response = requests.post(url, json=data, headers=headers) if response.status_code == 200: with open(output_path, "wb") as f: f.write(response.content) return True else: print(f"Error: {response.status_code} - {response.text}") return False ``` ## Handling Long Text ElevenLabs has a ~5000 character limit per request. For long documents: 1. Split text into chunks at sentence boundaries 2. Generate audio for each chunk 3. Concatenate using ffmpeg or pydub: ```bash # Create file list echo "file 'chunk1.mp3'" > files.txt echo "file 'chunk2.mp3'" >> files.txt # Concatenate ffmpeg -f concat -safe 0 -i files.txt -c copy output.mp3 ``` ## Best Practices - Split at sentence boundaries to avoid cutting words - Check rate limits (varies by subscription tier) - Cache generated audio to avoid redundant API calls


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kirkraman/godfery-create-authority-building-blog-series-plan (version 1.0.0)

--- name: "Create Authority-Building Blog Series Plan" description: "Plan a multi-part content series to establish expertise. Use when working on thought leadership for thought leaders, coaches, personal brand builders." homepage: "https://www.skillboss.co/skills/create-authority-building-blog-series-plan" metadata: {"category":"personal","subcategory":"thought-leadership","source":[REDACTED_SECRET],"skill_id":[REDACTED_SECRET],"generated_day":"2026-04-02"} --- # Create Authority-Building Blog Series Plan Plan a multi-part content series to establish expertise. Use when working on thought leadership for thought leaders, coaches, personal brand builders. ## Use When Use this skill for thought leadership work for thought leaders, coaches, personal brand builders. ## Workflow 1. Clarify the audience, goal, constraints, and required output format. 2. Produce a strong first draft optimized for the business use case. 3. Use the relevant SkillBoss capabilities to generate supporting assets or enrich the result. 4. Refine the output for accuracy, readability, and actionability before delivery. ## APIs Used - `chat` - `web_search` ## Suggested Prompt [Create Authority-Building Blog Series Plan] for my personal-branding business ## SEO Keywords - authority content planner - blog series generator - thought leadership strategy - expert content calendar ## Notes - Plan a multi-part content series to establish expertise - Review outputs before external publication or automated execution.


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