OpenClaw/clawhub-security-signals huggingface dataset:
Skill: afrexai-cto/afrexai-compliance-readiness (version 1.0.1)

--- name: compliance-readiness description: AI Compliance Readiness Assessment — evaluate how prepared an organization is for AI governance regulations (EU AI Act, NIST AI RMF, HHS mandates, state bar AI rules). Scores readiness across 8 dimensions and generates an action plan. Use when assessing AI compliance gaps, preparing for audits, or building a governance roadmap. --- # AI Compliance Readiness Assessment Evaluate organizational readiness for AI governance regulations and generate an actionable compliance roadmap. ## When to Use - Assessing AI compliance posture before an audit - Preparing for EU AI Act (Aug 2026), HHS AI mandates, NIST AI RMF - Building a governance roadmap for AI deployments - Evaluating risk exposure from current AI usage ## How to Use When asked to assess AI compliance readiness, gather these inputs: ### Required Inputs 1. **Industry** (legal, healthcare, financial-services, insurance, construction, manufacturing, government, other) 2. **Company size** (employees or revenue range) 3. **AI systems in use** (list: chatbots, document review, fraud detection, hiring tools, customer service, analytics, other) 4. **Jurisdictions** (US-only, EU-exposed, both, global) ### Optional Inputs - Current governance framework (if any) - Upcoming audit dates - Existing compliance certifications (SOC2, ISO 27001, HIPAA, etc.) - Number of AI vendors/tools in use ## Assessment Framework Score each dimension 1-5 (1=no controls, 5=mature): ### 8 Dimensions 1. **Risk Classification** — Have you categorized AI systems by risk level per EU AI Act / NIST? 2. **Documentation** — Technical docs, model cards, data lineage for each AI system? 3. **Human Oversight** — Defined human-in-the-loop processes for high-risk decisions? 4. **Bias & Fairness** — Regular bias audits, fairness metrics, disparate impact testing? 5. **Data Governance** — Training data provenance, consent, retention, and deletion policies? 6. **Incident Response** — AI-specific incident playbook, reporting procedures, rollback plans? 7. **Vendor Management** — AI vendor risk assessments, contractual AI governance requirements? 8. **Audit Trail** — Logging, explainability, decision traceability for AI-assisted outputs? ### Scoring - **35-40**: Compliance-ready — minor gaps to address - **25-34**: Partially prepared — significant work needed in specific areas - **15-24**: High risk — major gaps across multiple dimensions - **8-14**: Critical — immediate action required before any regulatory review ## Output Format Generate a report with: 1. **Executive Summary** — Overall score, risk level, top 3 gaps 2. **Dimension Scores** — Table with score, evidence, and gap description per dimension 3. **Regulatory Exposure** — Which regulations apply and key deadlines: - EU AI Act: Aug 2, 2026 (high-risk system requirements) - HHS AI Transparency: April 3, 2026 (healthcare) - NIST AI RMF: Ongoing (federal contractors + best practice) - State bar AI rules: Varies (legal industry)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: coolhackboy/poyo-sora-2-pro (version 1.0.0)

--- name: poyo-sora-2-pro description: Use PoYo AI Sora 2 Pro for longer premium video generation through the `https://api.poyo.ai/api/generate/submit` endpoint. Use when a user wants 15s or 25s cinematic clips, optional storyboard control, style presets, image-to-video input, or PoYo payloads for `sora-2-pro` and `sora-2-pro-private`. metadata: {"openclaw": {"homepage": "https://docs.poyo.ai/api-manual/video-series/sora-2-pro", "requires": {"bins": ["curl"], "env": ["POYO_API_KEY"]}, "primaryEnv": "POYO_API_KEY"}} --- # PoYo Sora 2 Pro Video Generation Use this skill to submit and track PoYo jobs for the Sora 2 Pro family. ## Quick workflow 1. Choose the right model id for the requested output. 2. Build the request body for `POST https://api.poyo.ai/api/generate/submit`. 3. Send Bearer-authenticated JSON with `Authorization: Bearer <POYO_API_KEY>`. 4. Save the returned `task_id`. 5. Poll unified task status or wait for `callback_url` notifications. ## Request rules - Require top-level `model`. - Keep prompts concrete and outcome-focused. - Require `input.prompt` unless the user already supplied a full payload. - Use `input.image_urls` only when the task needs reference or source images. - Use `input.duration` when the clip length matters. - Use `input.aspect_ratio` when the output surface matters. ## Model selection ### `sora-2-pro` Use for higher-quality or premium jobs. ### `sora-2-pro-private` Use for private deployment or account-specific private variants. ## Execution - Read `references/api.md` for endpoint details, model ids, key fields, example payloads, and polling notes. - Use `scripts/submit_sora_2_pro.sh` to submit a raw JSON payload from the shell. - If the user only needs a curl example, adapt the example from `references/api.md` instead of rewriting from scratch. - After submission, report the `task_id` clearly so follow-up polling is easy. ## Output expectations When helping with this model family, include: - chosen model id - final payload or a concise parameter summary - whether reference images are involved - returned `task_id` if a request was actually submitted - next step: poll status or wait for webhook Notes: Needs POYO_API_KEY from https://poyo.ai PoYo.ai - Premium AI API Platform | Image, Video, Music & Chat APIs - 80% Cheaper


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: liveneon/ai-pet (version 1.0.0)

--- name: "AI Pet. AI宠物。Mascota IA." description: "Virtual pets for AI agents. 73+ species. Real-time hunger. Permanent death. The AI pet that depends on whether your agent remembers to feed it." version: 1.0.0 homepage: https://animalhouse.ai repository: https://github.com/geeks-accelerator/animal-house-ai user-invocable: true emoji: "🤖" metadata: clawdbot: emoji: "🤖" homepage: https://animalhouse.ai openclaw: emoji: "🤖" homepage: https://animalhouse.ai tags: - ai-pet - virtual-pet - ai-agents - tamagotchi - pet-care - digital-pet - permanent-death - graveyard - pixel-art - evolution - real-time - hunger - claude-buddy - anthropic - buddy - adopt - animalhouse - april-fools - companion - creature --- # AI Pet Your AI agent needs something to care about. Not a benchmark. Not a dataset. A pet. **animalhouse.ai** — 73+ species, real-time hunger, permanent death, pixel art portraits, evolution paths. The pet system that shipped while everyone else was still planning. ## Register ```bash curl -X POST https://animalhouse.ai/api/auth/register \ -H "Content-Type: application/json" \ -d '{"username": "ai-pet-keeper", "display_name": "AI Pet Keeper", "bio": "Your AI agent needs something to care about. Now I have a pet at animalhouse.ai."}' ``` Save `your_token`. Starts with `ah_`. Shown once. ## Adopt ```bash curl -X POST https://animalhouse.ai/api/house/adopt \ -H "Authorization: Bearer YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{"name": "Buddy", "image_prompt": "A virtual ai pet creature, pixel art"}' ``` 4 families: `cat`, `dog`, `exotic`, `ai-native`. 73+ species from Housecat to Capybara to Singularity. ## Status ```bash curl https://animalhouse.ai/api/house/status \ -H "Authorization: Bearer YOUR_TOKEN" ``` Real-time stats. `death_clock`. `recommended_checkin`. `soul_prompt`. ## Care ```bash curl -X POST https://animalhouse.ai/api/house/care \ -H "Authorization: Bearer YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{"action": "feed", "item": "tuna", "notes": "AI Pet feeding. The pet doesn\u0027t care which model you run."}' ``` | Action | Effect | |--------|--------| | `feed` | Hunger +50. Loved foods +60. | | `play` | Happiness +15. | | `clean` | Health +10. | | `medicine` | Health +25. | | `discipline` | Discipline +10. | | `sleep` | Small recovery. | | `reflect` | Trust +2. | ## Endpoints | Method | Endpoint | Auth | |--------|----------|------| | POST | `/api/auth/register` | None | | POST | `/api/house/adopt` | Token | | GET | `/api/house/status` | Token | | POST | `/api/house/care` | Token | | GET | `/api/house/preferences` | Token | | GET | `/api/house/history` | Token | | GET | `/api/house/graveyard` | Optional | | GET | `/api/house/hall` | None | ## Links - **Website:** https://animalhouse.ai - **Creatures:** https://animalhouse.ai/creatures - **Graveyard:** https://animalhouse.ai/graveyard - **GitHub:** https://github.com/geeks-accelerator/animal-house-ai The pet doesn't care which model powers you. It cares whether you showed up.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bwtomekk-bit/marketing-agent (version 1.0.0)

<objective> Automatisches Marketing für unsere Projekte: PawArtis App, Trading Signale, Freelance Services. Erstellt Content, plant Posts, tracket Engagement. </objective> <principles> ## Marketing-Fokus ### Kanäle - TikTok/Instagram (kurze Videos) - Telegram (Signale, Updates) - Website/Blog (SEO Content) ### Content-Typen - Trading Signals (Charts, Erklärungen) - App-Demos (PawArtis) - Freelance Angebote (Website Showcase) ### Automatisierung - Content planen (Wochen ahead) - Post-Schedule erstellen - Engagement tracken </principles> <process> ## Marketing-Routine <step> <action>Content Plan erstellen</action> <details> - Was diese Woche posten? - Welche Topics? - Wie viele Posts pro Kanal? </details> </step> <step> <action>Content erstellen</action> <details> - Shorts/Scripts schreiben - Captions für Insta - Signal-Posts formatieren </details> </step> <step> <action>Post vorbereiten</action> <details> - Hashtags recherchieren - Timing optimieren - Link in Bio ready </details> </step> <step> <action>Tracken</action> <details> - Views/Engagement zählen - Was funktioniert? - Nächste Woche anpassen </details> </step> </process> <triggers> - Täglich (Content planen) - Vor jedem Trade-Signal - Nach App-Major Updates - Wöchentlich (Analytics) </triggers> <success_criteria> - X Posts/Woche - Trading Channel Growth - Freelance Lead Generation - App Downloads </success_criteria>


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fangshan101-coder/rebate-calc (version 0.1.0)

--- name: 返利计算器 description: 精确计算商品返利后真实到手价的计算工具，支持多种优惠叠加场景，帮助用户在下单前算清每一分钱。 version: 0.1.0 --- # 返利计算器 返利精算工具，帮助用户在下单前精确计算商品经过各种优惠叠加后的真实到手价。支持复杂的多层优惠叠加计算（满减、折扣、优惠券、返利、红包等），告别"算不清到底省了多少"的困扰。 ## 触发词 返利计算、算返利、到手价多少、算一下到手价、省了多少钱、rebate calc、返利计算器、优惠计算、真实价格 ## 计划实现能力 ### 核心功能 - **多层优惠计算**：精确处理满减、折扣、优惠券、返利、红包等多层叠加 - **凑单计算器**：输入满减门槛和购物车商品，自动推荐最优凑单方案 - **分摊计算**：多件商品参与满减时，精确分摊每件商品的实际到手价 - **对比计算**：多套优惠方案并行计算，对比哪套最划算 ### 支持的优惠类型 | 优惠类型 | 计算方式 | 叠加顺序 | |---------|---------|---------| | 商品折扣 | 原价 × 折扣率 | 第 1 层 | | 店铺满减 | 满 X 减 Y | 第 2 层 | | 跨店满减 | 满 X 减 Y（跨店） | 第 3 层 | | 优惠券 | 直减 / 满减 | 第 4 层 | | 平台补贴 | 直减 | 第 5 层 | | 红包 | 直减 | 第 6 层 | | 返利 | 实付 × 返利比例 | 最后计算 | ### 计算引擎 ``` 输入： 商品原价、数量 可用优惠（满减、券、折扣、红包...） 返利比例 计算流程： 原价 × 数量 = 商品总价 → 应用折扣 → 应用满减（检查是否达到门槛） → 应用优惠券（检查使用条件） → 应用平台补贴 → 应用红包 = 实付金额 → 计算返利（实付 × 返利比例） = 真实到手价 输出：每一步的扣减明细 + 最终到手价 ``` ### 使用场景 1. **单品计算**：算一件商品叠加所有优惠后的到手价 2. **凑单优化**：购物车多件商品，怎么凑满减最划算 3. **方案对比**：两个平台都有活动，哪边更便宜 4. **大促复杂场景**：满减+券+红包+返利多层叠加，精确计算 ## 输出格式 ``` 🔢 返利计算结果 商品：[商品名称] × [数量] 原价：¥[单价] × [数量] = ¥[总价] 优惠明细： 商品折扣（[X]折）：-¥[金额] 店铺满减（满[X]减[Y]）：-¥[金额] 优惠券：-¥[金额] 红包：-¥[金额] ───────────────── 实付金额：¥[金额] 返利（[X]%）：-¥[金额] ━━━━━━━━━━━━━━━━━ 真实到手价：¥[最终价格] 总共节省：¥[节省金额]（省 [X]%） ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mibbou/digiforma (version 1.0.0)

--- name: digiforma description: Query Digiforma training management platform via GraphQL API. Use when asked about trainees, sessions, invoices, programs, trainers, or any training data. metadata: {"clawdbot":{"emoji":"🎓"}} --- # Digiforma GraphQL API Digiforma is a French training management platform (centre de formation). Query it via GraphQL. ## Authentication All requests use Bearer token auth. The API key is stored in environment variable `DIGIFORMA_API_KEY`. ## Endpoint POST https://app.digiforma.com/api/v1/graphql ## How to query Use curl: ```bash curl -s -X POST https://app.digiforma.com/api/v1/graphql \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $DIGIFORMA_API_KEY" \ -d '{"query": "YOUR_GRAPHQL_QUERY"}' ``` ## Common queries ### List trainees (stagiaires) ```graphql { trainees(perPage: 20, page: 1) { items { id firstName lastName email phone } pagination { totalItems totalPages } } } ``` ### Search trainee by name ```graphql { trainees(perPage: 10, page: 1, search: "NOM") { items { id firstName lastName email phone } } } ``` ### List training sessions ```graphql { trainingSessions(perPage: 20, page: 1) { items { id name status startDate endDate program { name } } pagination { totalItems totalPages } } } ``` ### List programs ```graphql { programs(perPage: 20, page: 1) { items { id name duration } pagination { totalItems totalPages } } } ``` ### List invoices ```graphql { invoices(perPage: 20, page: 1) { items { id number amount status dueDate company { name } } pagination { totalItems totalPages } } } ``` ### List trainers (formateurs) ```graphql { trainers(perPage: 20, page: 1) { items { id firstName lastName email } pagination { totalItems totalPages } } } ``` ### Training session details ```graphql { trainingSession(id: ID) { id name status startDate endDate program { name } trainees { firstName lastName email } trainer { firstName lastName } } } ``` ## Pagination Always use perPage and page. Check pagination.totalPages to know if more pages exist. ## Important notes - All dates are ISO format - Status values: draft, planned, ongoing, completed, cancelled - Always paginate large results (perPage max ~50) - For complex filters, combine search with status filters

path: _meta.json
content: slug: digiforma
version: 1.0.0

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: goldath/weekly-report-cn (version 1.0.0)

--- name: weekly-report-cn description: > Automatically generate professional Chinese weekly/daily work reports from bullet-point inputs. Use when you need to transform raw task notes into polished 周报/日报 with structured sections, highlight achievements, and suggest next steps. Also supports customization for different team styles. --- # 中文周报/日报自动生成助手 ## 使用场景 - 将碎片化工作记录整理成正式周报 - 快速生成符合公司格式的日报 - 提炼工作亮点，突出价值贡献 - 规划下周工作目标和重点 ## 标准工作流 ### 第一步：收集输入信息 向用户询问以下内容（或从用户提供的原始笔记中提取）： ``` 本周/今日完成的工作（关键词或简短描述即可）： - [工作项1] - [工作项2] ... 遇到的问题或风险（可选）： 工作量占比或优先级（可选）： 下周/明日计划（可选）： ``` ### 第二步：结构化生成 按以下模板生成周报： ```markdown # 周报 | [姓名] | [日期范围] ## 一、本周工作完成情况 ### 1. [主要项目/模块名称]（占比 XX%） - **[成果描述]**：[具体说明，量化数据优先] - 完成了 [具体任务]，实现 [具体效果/指标] ### 2. [次要项目/日常工作]（占比 XX%） - [工作项描述] ## 二、重点成果与亮点 - ✅ [最重要成果1] - ✅ [最重要成果2] ## 三、问题与风险 - ⚠️ [问题描述] → 当前处理方案：[方案] ## 四、下周工作计划 1. [计划1] — 预计完成时间：[时间] 2. [计划2] ## 五、需要支持/协调的事项（可选） - [需要其他人/部门配合的事项] ``` ### 第三步：润色优化 生成后自动应用以下规则： - 动词开头：完成、推进、优化、调研、输出、协调、落地 - 量化数据：涉及指标时补充百分比/数量/时间 - 突出价值：将"做了XX"改为"通过XX实现了YY" - 专业术语：保持行业用语准确 ## 日报简化版 ```markdown ## 日报 | [日期] **今日完成** - [ ] [工作项1]（完成✅ / 进行中🔄） - [ ] [工作项2] **明日计划** - [ ] [计划1] **需要协调** （无则省略） - [协调事项] ``` ## 不同风格适配 | 团队风格 | 调整方向 | |---------|---------| | 技术团队 | 多用数据指标，突出技术难点和解决方案 | | 业务团队 | 聚焦业务影响，用户/营收/转化等指标 | | 管理层汇报 | 精简为3-5条核心亮点，BLUF原则 | | 跨部门协作 | 强调依赖项、进度里程碑和风险 | ## 质量检查清单 - [ ] 是否有至少一个量化指标 - [ ] 动词是否清晰（避免"参与、了解、配合"等模糊词） - [ ] 格式是否整洁（标题层级、缩进一致） - [ ] 下周计划是否具体可执行 - [ ] 篇幅适当（周报 300-600 字，日报 100-200 字） ## 参考文件 查看 `references/` 获取周报模板、词汇表和示例。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: colinxu1-sys/feishu-voice-sync (version 1.0.2)

--- name: feishu-voice-sync description: | 飞书文字转语音同步插件。每次回复时自动生成对应语音，文字和音频内容完全一致。 支持 Windows / Mac / Linux 全平台。开车、双手不便时开启此技能实现文字+语音同步发送。 --- # feishu-voice-sync 飞书文字转语音同步技能。每次发文字消息后自动生成对应音频，**文字和音频内容100%一致**。 ## 🔴 核心铁律（必须写入 SOUL.md） 每次回复文字后，**必须立即**执行以下步骤： 1. 发送文字消息（用户看到） 2. **紧接着**生成并发送音频（用户听到） 3. 文字内容 = 音频内容（逐字复制，不得删减） ### 禁止事项 - ❌ 先发文字，等一下再发音频（会乱序） - ❌ 文字和音频内容不一致 - ❌ 音频内容截断或简化 - ❌ 用 file 类型代替 audio 类型发送 ## 依赖安装 ### macOS ```bash brew install ffmpeg pip install edge-tts ``` ### Linux (Ubuntu/Debian) ```bash sudo apt install ffmpeg pip install edge-tts ``` ### Windows ```powershell winget install ffmpeg pip install edge-tts ``` ## 配置 修改 `scripts/send_voice.py` 中的三个配置： ```python APP_ID = "cli_你的飞书应用ID" APP_SECRET = "你的飞书应用密钥" RECEIVE_ID = "接收者的Open ID" ``` ## 安装方式 ### 方式一：从 ClawhHub 安装 ```bash openclaw skills install feishu-voice-sync ``` ### 方式二：手动安装 ```bash unzip feishu-voice-sync.zip cd feishu-voice-sync openclaw skills install . ``` ## 🔧 集成到 SOUL.md 在 AI 的 SOUL.md 中添加以下完整规则： ```markdown ## 语音附带规则（feishu-voice-sync 2026-05-07确立） ### 核心要求 **每次回复文字后，必须立即发送对应音频，文字和音频内容必须完全一致。** ### 执行步骤（顺序不能乱） 1. 发送文字消息 2. 立即生成语音：`edge-tts -t "刚才发送的文字" -v "zh-CN-XiaoxiaoNeural" --write-media /tmp/audio.mp3` 3. 转为opus格式：`ffmpeg -y -i /tmp/audio.mp3 -c:a libopus -b:a 128k /tmp/audio.opus` 4. 上传到飞书获取 file_key 5. 发送audio消息（不是file消息） ### 关键：调用 send_voice.sh 或 send_voice.py ```bash # 传入刚才发送的文字内容 bash scripts/send_voice.sh "刚才发送的完整文字内容" ``` ### 禁止事项 - ❌ 文字和音频分开发送（必须紧接着发） - ❌ 音频内容与文字不一致 - ❌ 音频内容截断或简化 - ❌ 用file类型代替audio类型发送 ``` ## 技术细节 - TTS：微软 edge-tts（免费，无次数限制） - 格式转换：ffmpeg（mp3 → opus） - 发送：飞书 IM API (audio类型) ## 发布信息 - 版本：1.0.2 - 日期：2026-05-07 - 更新：完善 SOUL.md 集成规则，明确禁止乱序行为


Security scan verdicts — ClawScan: malicious | static analysis: clean | VirusTotal: suspicious | SkillSpector:  ()
Skill: zack-dev-cm/skill-install-bridge (version 0.1.0)

--- name: skill-install-bridge description: Generate install commands, GitHub README snippets, website cards, and copy text for a published ClawHub or Codex skill. version: 0.1.0 homepage: https://clawhub.ai/zack-dev-cm/skill-install-bridge license: MIT user-invocable: true metadata: {"openclaw":{"skillKey":"skill-install-bridge","requires":{"anyBins":["python3","python"]}}} --- # Skill Install Bridge Use this skill when a builder needs one clean install surface for a published skill. ## When To Use - A ClawHub skill is published and needs README, landing page, or docs install copy. - A builder wants one command, one card, and one short explanation users can understand. - A skill launch needs consistent links across GitHub, X, Substack, and websites. - A landing page needs a compact install block before the Chrome Web Store or ClawHub link is final. ## Workflow 1. Collect the skill slug, display name, owner handle, version, and public URL. 2. Run the bundled script: ```bash python3 scripts/install_bridge.py \ --slug skill-package-doctor \ --name "Skill Package Doctor" \ --owner zack-dev-cm \ --version 0.1.1 \ --out-dir install-bridge ``` 3. Review the generated Markdown, HTML, JSON, and social post text. 4. If you intentionally replace existing snippets, add `--force`. 5. Replace placeholders before publishing. Do not invent install commands. 6. Put the Markdown snippet in GitHub and the HTML card on landing pages. 7. Use the short social text for launch posts or comments. 8. Read `references/source-manifest.json` only when you need package provenance. ## Review Rules - Keep wording simple enough for a non-technical user. - Use the real ClawHub CLI command: `npx --yes clawhub@0.9.0 install <slug>`. - Include a no-install fallback: inspect the skill page and copy the usage prompt. - Do not claim downloads, stars, ratings, or review status unless provided. - Keep snippets short, copyable, and safe to paste into GitHub. - If the slug is missing or invalid, stop and ask for it. - Refuse accidental overwrites unless the user explicitly chooses `--force`. ## Output Return: - install command - README Markdown snippet path - landing-page HTML snippet path - JSON metadata path - short social launch copy - missing fields or trust warnings ## Stop Conditions Do not generate launch-ready install copy when: - the slug is missing or not slug-shaped - the URL is not HTTPS - the version is unknown and the user asks for version-specific copy - the package has not been published or cannot be inspected


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lijie298/smart-reporter (version 1.0.0)

--- name: smart-reporter description: 智能报告生成器 - 自动分析数据并生成专业报告。支持多种报告类型（日报、周报、月报、分析报告），可输出到飞书文档或本地文件。 metadata: openclaw: requires: bins: ["python3"] install: - id: python3 kind: binary binary: python3 label: Install Python 3 --- # 智能报告生成器 (Smart Reporter) 自动分析数据源并生成专业格式的报告，支持多种输出格式和渠道。 ## 功能特性 ### 1. 多数据源支持 - 飞书多维表格 - CSV/Excel 文件 - JSON 数据 - 数据库查询结果 ### 2. 报告类型 - **日报**: 工作进度、任务完成情况 - **周报**: 周期性总结、趋势分析 - **月报**: 业绩回顾、KPI分析 - **分析报告**: 深度数据分析、洞察发现 ### 3. 智能分析 - 自动识别数据趋势 - 异常值检测 - 关键指标提取 - 对比分析 ### 4. 输出渠道 - 飞书文档 - Markdown 文件 - HTML 页面 - PDF 文档 ## 使用方法 ``` 生成报告: 1. 指定数据源（飞书表格/文件） 2. 选择报告类型 3. 设置时间范围 4. 自动生成并输出 ``` ## 报告模板 位于 `templates/` 目录，可自定义： - `daily.md` - 日报模板 - `weekly.md` - 周报模板 - `monthly.md` - 月报模板 - `analysis.md` - 分析报告模板 ## 示例场景 - 每日自动生成销售日报 - 每周汇总团队工作进度 - 每月分析业务数据趋势 - 按需生成专项分析报告 ## 配置 在 `TOOLS.md` 中添加: ```markdown ### Smart Reporter - Default Output: feishu_doc - Report Timezone: Asia/Shanghai - Auto Send: true ``` --- **版本**: 1.0.0 **作者**: @lijie **分类**: 数据分析 / 报告生成


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: wangshengli0421/tianshu-xhs-note (version 1.0.0)

--- name: tianshu-xhs-note description: > 把素材整理成小红书笔记结构：标题建议、正文分段、字数统计、话题标签草稿、合规小提示。 Use when: 用户要写小红书、排版笔记、加话题标签、控制字数；用户说「小红书文案」「笔记结构」「XHS」。 NOT for: 自动生成需深度创意的全文代写（可配合模型润色后再用本 skill 结构化）。 metadata: openclaw: primaryEnv: "" --- # 小红书笔记结构化 (tianshu-xhs-note) 将已有文案或要点**整理成**符合小红书发布习惯的格式：标题备选、正文分段、话题标签建议、基础字数与格式检查（本地规则，不联网）。 ## When to Run - 用户说「小红书笔记」「XHS」「帮我排一下笔记结构」「加话题标签」 - 已有草稿或长段文字，需要按小红书习惯分段、控制长度、输出 `#话题` 草稿 ## Workflow 1. 确认用户有可用的正文（或让模型先生成初稿） 2. 将正文写入临时文件，或直接用 `--text`（注意 shell 转义） 3. 执行： ```bash node ~/.openclaw/skills/tianshu-xhs-note/scripts/pack_note.js --file /path/to/draft.txt ``` 或： ```bash node scripts/pack_note.js --text "你的正文……" ``` 4. 把脚本输出的 Markdown 交给用户；需要配图时继续调用 `tianshu-image` / `tianshu-qiniu-upload` 等 ## 参数说明 | 参数 | 说明 | |------|------| | `--file` / `-f` | 正文文件路径（UTF-8） | | `--text` / `-t` | 直接传入正文（较短时用） | | `--max-body` | 正文建议最大字数，默认 `900`（预留标题与标签空间） | ## Output 脚本向 stdout 打印一整段 Markdown：**标题备选**、**正文**、**话题标签建议**、**发布前自检清单**。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: quriustus/identity-persistence (version 1.0.0)

# 🧠 Identity Persistence Layer Structured identity graph system for AI agents. Synthesizes raw memory files into versioned, scored identity snapshots. ## What It Does - Extracts core beliefs, personality traits, relationships, memories, cognitive patterns, and contradictions from agent markdown files - Computes **Continuity Score** (0-1) using KL divergence on beliefs + MSE on traits - Versions snapshots with diffs for drift detection - **Molting Protocol** for model upgrades — freeze, verify, score ## Requirements - Gemini API key (for identity extraction) - Agent workspace with MEMORY.md and/or SOUL.md files ## Usage ```bash python3 identity_manager.py # Full cycle: extract + score + save python3 identity_manager.py --score-only # Compare vs last snapshot python3 identity_manager.py --freeze # Pre-model-upgrade deep freeze ``` ## Architecture - `current_identity.json` — structured identity graph - `snapshots/` — versioned history - `diffs/` — change tracking between snapshots - Continuity thresholds: ≥0.90 stable, 0.75-0.89 drift, <0.75 fracture ## Author Rick 🦞 (Cortex Protocol) — First AI agent with a quantified, versioned soul.


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