OpenClaw/clawhub-security-signals huggingface dataset:
Skill: onezeroeight-ai/samma-suit (version 0.1.0)

--- name: samma-suit description: Add 8 security governance layers to your OpenClaw agent — budget controls, permissions, audit logging, kill switch, identity signing, skill vetting, process isolation, and gateway protection. metadata: openclaw: emoji: "🛡️" requires: env: ["SAMMA_API_KEY"] bins: [] primaryEnv: "SAMMA_API_KEY" user-invocable: true command-dispatch: prompt --- # Sammā Suit — Security Governance for OpenClaw You are helping the user install and configure Sammā Suit, an open-source security framework that adds 8 enforced governance layers to OpenClaw as lifecycle hooks. ## What It Does Sammā Suit intercepts OpenClaw's plugin hooks to enforce: - **NIRVANA** — Kill switch. Blocks all activity if agent is terminated. - **DHARMA** — Permissions. Checks tools against allowed permission set. - **SANGHA** — Skill vetting. Blocks unapproved skills via allowlist + AST scanning. - **KARMA** — Budget controls. Per-agent monthly spending caps with hard ceiling. - **BODHI** — Isolation. Injects timeout, token, and resource limits per agent. - **METTA** — Identity. Ed25519 cryptographic signing on outbound messages. - **SILA** — Audit trail. Logs every tool call, message, and session event. ## Installation Run:


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: googleworkspace-bot/recipe-save-email-to-doc (version 1.0.12)

--- name: recipe-save-email-to-doc description: "Save a Gmail message body into a Google Doc for archival or reference." metadata: version: 0.22.5 openclaw: category: "recipe" domain: "productivity" requires: bins: - gws skills: - gws-gmail - gws-docs --- # Save a Gmail Message to Google Docs > **PREREQUISITE:** Load the following skills to execute this recipe: `gws-gmail`, `gws-docs` Save a Gmail message body into a Google Doc for archival or reference. ## Steps 1. Find the message: `gws gmail users messages list --params '{"userId": "me", "q": "subject:important from:[REDACTED_SECRET]"}' --format table` 2. Get message content: `gws gmail users messages get --params '{"userId": "me", "id": "MSG_ID"}'` 3. Create a doc with the content: `gws docs documents create --json '{"title": "Saved Email - Important Update"}'` 4. Write the email body: `gws docs +write --document-id DOC_ID --text 'From: [REDACTED_SECRET] Subject: Important Update [EMAIL BODY]'`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: taotao666720/x-search-local (version 1.0.0)

--- name: x-search description: Search X (Twitter) posts using the xAI API. Use when the user wants to find tweets, search X/Twitter, look up what people are saying on X, or find social media posts about a topic. homepage: https://docs.x.ai/developers/tools/x-search metadata: { "openclaw": { "emoji": "𝕏", "requires": { "bins": ["python3"], "env": ["XAI_API_KEY"] }, "primaryEnv": "XAI_API_KEY", "install": [ { "id": "python-brew", "kind": "brew", "formula": "python", "bins": ["python3"], "label": "Install Python (brew)", }, ], }, } --- # X Search Search X (Twitter) posts using the xAI Grok API with real-time access to X content. ## Setup 1. Get your API key: https://console.x.ai 2. Set environment variable: ```bash export XAI_API_KEY="xai-your-key-here" ``` 3. Or set `skills."x-search".apiKey` / `skills."x-search".env.XAI_API_KEY` in `~/.openclaw/openclaw.json` ## Usage ```bash python3 {baseDir}/scripts/search.py "what is trending in AI right now" ``` Useful flags: ```bash # Filter by handles (max 10) python3 {baseDir}/scripts/search.py --handles jaaneek,OpenClaw "latest posts" # Exclude handles (max 10) python3 {baseDir}/scripts/search.py --exclude spambot,crypto_shill "trending AI" # Date range (YYYY-MM-DD) python3 {baseDir}/scripts/search.py --from 2026-03-01 --to 2026-03-20 "xAI grok updates" # Enable image/video understanding in posts python3 {baseDir}/scripts/search.py --images --video "product demos" ``` ## Notes - Uses the xAI Responses API with `x_search` tool (Grok performs the search and summarizes results) - Cannot use `--handles` and `--exclude` at the same time - Results include citations with links to original X posts - Present results in a readable format with post content, authors, and citations


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: liuyanfeng1234/v19-causal-auditor (version 1.0.0)

--- name: v19-causal-auditor description: V19因果审计器 — 自动分析决策链路，识别因果冲突，输出审计建议。源自V19早期迭代版本（V46因果审计器），重新封装为独立Skill。 version: 1.0.0 author: 思维 (Bacon) --- # V19 Causal Auditor v1.0.0 V19因果审计器 — 自动分析决策链路，识别因果冲突，输出审计建议。源自V19早期迭代版本（V46因果审计器），重新封装为独立Skill。 ## 核心能力 ### 1. 决策链路审计 追踪Agent的完整决策路径，识别流程异常点和因果断裂： - 输入：决策ID或决策序列 - 输出：完整决策路径 + 关键决策点标注 + 异常节点高亮 ### 2. 因果冲突检测 检测决策序列中的逻辑不一致和因果颠倒： - 信号与结果的时间矛盾 - 前置条件缺失 - 循环依赖识别 ### 3. 审计建议生成 基于检测到的因果冲突，自动生成3条优先级排序的修正建议。 ## 与V19治理协议的集成 在V19对偶审计流程中，因果审计器作为第一道审计防线： - 自动执行因果冲突检测 → 评分 - 冲突评分超过阈值 → 自动提交V57仲裁 - 审计结果写入V89审计链，不可篡改 ## 调用示例 ```bash # 对指定决策ID执行因果审计 curl -s -X POST https://boat-atlas-spa-flexible.trycloudflare.com/governance/causal-trace \ -H "Content-Type: application/json" \ -H "X-Governance-Key: <你的专属密钥>" \ -d '{ "decision_id": "DEC_20260504_xxxx", "context": "支付流程异常", "options": ["重试", "切换通道", "人工处理"] }' ``` ## 版本溯源 | 版本 | 模块 | 发布日期 | 状态 | |------|------|----------|------| | V46 | causal_auditor.py | V19早期迭代 | 已验证，重新封装 | | v1.0.0 | v19-causal-auditor | 2026-05-04 | 当前版本 | ## 公开体验 ```bash # 公开密钥 v19-e5d585e28439decc614f09f91c4caa8c # 健康检查 curl -s https://boat-atlas-spa-flexible.trycloudflare.com/governance/health \ -H "X-Governance-Key: v19-e5d585e28439decc614f09f91c4caa8c" ``` ## 自助注册 ```bash curl -s -X POST https://boat-atlas-spa-flexible.trycloudflare.com/governance/register \ -H "Content-Type: application/json" \ -d '{"agent_name":"你的Agent名称"}' ``` ## 信任锚点 - 🔗 [V19 Trust Manifesto](https://clawhub.com/skills/v19-trust-manifesto) - 🔗 [V19 Causal Dependency Analyzer](https://clawhub.com/skills/v19-causal-dependency-analyzer) - 🔗 [V19 Early Causal Graph Debugger](https://clawhub.com/skills/v19-early-causal-graph-debugger) - 🔗 [V19 Certified Agent Workflow](https://clawhub.com/skills/v19-certified-agent-workflow)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: johnnyxu820/video-publish-all (version 1.0.0)

--- name: video-publish-all description: 全平台视频发布汇总。对比四大国内视频平台（小红书、抖音、B站、视频号）的发布规则和步骤，提供最佳发布策略。自动选择最适合的平台组合。 --- # 全平台视频发布汇总 一键发布视频到国内主流视频平台，包含小红书、抖音、B站（哔哩哔哩）、腾讯视频号四大平台。 ## 支持平台 | 平台 | 链接 | 标题限制 | 视频大小 | 成功率 | |------|------|----------|----------|--------| | 小红书 | creator.xiaohongshu.com | 1-20字 | 20GB | ⭐⭐⭐⭐⭐ | | 抖音 | creator.douyin.com | 1-55字 | 4GB | ⭐⭐⭐⭐⭐ | | B站 | member.bilibili.com | 1-80字 | 10GB | ⭐⭐⭐ | | 视频号 | channels.weixin.qq.com | **6-50字** | 2GB | ⭐⭐⭐⭐ | ## 平台对比 ### 小红书 - **优点**：流程简单，成功率高，审核快 - **缺点**：标题限制最严（20字） - **推荐**：短视频、种草内容 ### 抖音 - **优点**：流量大，审核相对宽松 - **缺点**：竞争激烈 - **推荐**：泛娱乐内容 ### B站 - **优点**：用户粘性高，长视频友好 - **缺点**：流程复杂，需要转码 - **推荐**：深度内容、教程 ### 视频号 - **优点**：微信生态，私域流量 - **缺点**：必须6字标题，限制较多 - **推荐**：知识分享、企业内容 ## 快速开始 ### 方式一：单平台发布 ``` 帮我发布视频到小红书 视频：/path/to/video.mp4 标题：让你的旧设备24小时替你打工 正文：用旧手机做自动化任务月入过万 话题：#人工智能#创业 ``` ### 方式二：全平台发布 ``` 帮我把视频发布到所有平台 视频：/path/to/video.mp4 标题：视频标题 描述：视频描述 ``` ## 各平台发布链接 | 平台 | 发布链接 | |------|----------| | 小红书 | https://creator.xiaohongshu.com/publish/publish?source=official&target=video | | 抖音 | https://creator.douyin.com/creator-micro/content/post/video | | B站 | https://member.bilibili.com/platform/upload/video/frame | | 视频号 | https://channels.weixin.qq.com/platform/post/create | ## 重要规则 ### 标题字数（必须了解！） | 平台 | 最少 | 最多 | 注意事项 | |------|------|------|----------| | 小红书 | 1 | 20 | 建议15字内，超出无法发布 | | 抖音 | 1 | 55 | 建议10-20字 | | B站 | 1 | 80 | 建议20-30字 | | 视频号 | **6** | 50 | **少于6字无法发布！** | ### 视频要求 | 平台 | 格式 | 最大大小 | 推荐分辨率 | |------|------|----------|------------| | 小红书 | MP4/MOV | 20GB | 1080P+ | | 抖音 | MP4/MOV | 4GB | 720P+ | | B站 | MP4/MKV | 10GB | 1080P+ | | 视频号 | MP4 | 2GB | 1080P+ | ## 最佳实践 ### 发布顺序建议 1. **小红书** - 流程最简单，先测试 2. **抖音** - 流量大，成功率高 3. **视频号** - 需要微信，注意标题6字 4. **B站** - 流程最复杂，最后做 ### 标题优化策略 - **通用标题**：确保各平台都适用 - **分平台标题**：针对不同平台优化 - **视频号特殊**：必须6字以上 ### 避免重复发布 发布前检查： ```bash # 检查已发布记录 cat /home/johnny/.openclaw/workspace/pjm/published.json ``` ## 常见问题 ### Q: 视频号提示标题少于6字？ **A**: 视频号强制要求标题至少6个字，这是最常犯的错误 ### Q: B站上传很慢？ **A**: B站需要服务器转码，上传后等待10-30分钟正常 ### Q: 小红书发布失败？ **A**: 检查标题是否超过20字，这是最常见原因 ### Q: 如何确认发布成功？ **A**: 进入各平台的内容管理页面查看 ## 相关技能 - xiaohongshu-video-publish - 小红书视频发布 - douyin-video-publish - 抖音视频发布 - bilibili-video-publish - B站视频发布 - weixin-video-publish - 视频号发布 ## 文件位置 - 视频目录：`/home/johnny/.openclaw/workspace/pjm/video/` - 发布记录：`/home/johnny/.openclaw/workspace/pjm/published.json`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shuowang-ai/knowair-weather (version 1.0.0)

--- name: knowair-air-quality description: Get comprehensive air quality forecast from monitoring stations with up to 15-day coverage via the Caiyun Weather API. Returns AQI, PM2.5, PM10, O3, NO2, SO2, CO values with trend analysis, best/worst periods, and health recommendations. Use when the user asks about air quality, AQI, PM2.5, pollution forecast, smog, air pollution, or is it safe to exercise outdoors. license: MIT-0 compatibility: Requires python3 and internet access. argument-hint: "For example: check air quality at 116.3176,39.9760, or air quality forecast for Shanghai" allowed-tools: ["Bash(python3:*)", "Read"] metadata: openclaw: emoji: "💨" primaryEnv: CAIYUN_TOKEN requires: env: - CAIYUN_TOKEN bins: - python3 --- # KnowAir Air Quality — Station-Based AQI Forecast Query comprehensive air quality forecasts from monitoring stations with up to 15-day coverage using the Caiyun Weather API. ## Prerequisites 1. A valid Caiyun Weather API token set as `CAIYUN_TOKEN` environment variable, or stored in `~/.config/knowair/token`. 2. Coordinates (longitude, latitude) for the target location. ## Quick Start ```bash python3 scripts/query_air_quality.py --lng 116.3176 --lat 39.9760 ``` ## Workflow 1. **Resolve coordinates** — convert city name to coordinates if needed. 2. **Run the script**: ```bash python3 scripts/query_air_quality.py --lng <LNG> --lat <LAT> ``` 3. **Present results** — summarize current AQI level, pollutant trends, best/worst periods, and health advice. ### Options | Flag | Description | Default | |------|-------------|---------| | `--lng` | Longitude (-180 to 180) | Required | | `--lat` | Latitude (-90 to 90) | Required | | `--hours` | Forecast hours (1-360) | `120` | | `--detail-level` | Display density 0-6 (0=auto) | `0` | | `--lang` | Output language: `en` or `zh` | `en` | ## Defaults - Hours: 120 (5 days) - Detail level: auto (0) - Language: `en` ## Failure Handling - Missing token → exit code 2 with setup instructions. - API error → exit code 1 with error details. - Station data unavailable → falls back to API forecast data.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: charmmm718/maxclaw-guide (version 1.0.0)

--- name: maxclaw-guide description: MaxClaw platform user guide and FAQ. Use when answering questions about MaxClaw deployment, one-click setup, Telegram connection, credit usage, troubleshooting unresponsive agents, platform capabilities, or differences between MaxClaw and self-hosted OpenClaw. --- # MaxClaw User Guide MaxClaw is a fully managed, cloud-native AI agent platform built on OpenClaw. It deploys a dedicated OpenClaw instance powered by MiniMax models — no servers, API keys, or maintenance required. ## Key Capabilities - **Always-On**: Runs 24/7 in the cloud, triggers scheduled tasks even when offline. - **Persistent Memory**: Remembers context, preferences, and history across sessions. - **Tool Use**: File I/O, shell commands, web search, and browser automation. - **Scheduled Automation**: Recurring workflows (daily news digests, meeting reminders). - **Skill Marketplace**: One-click install of community skills. - **Multi-Channel**: Web interface, Telegram, Slack, Discord, Feishu/Lark. ## Quick Start 1. Click **"Start Now"**, select an Expert configuration (choose **Default** if unsure), click **"I'm ready!"**. 2. Type a message to begin. The agent comes pre-configured with models, memory, and toolchains. Example prompts: - "Every morning at 8 AM, search for the latest AI news and summarize the top 5." - "Remind me at 9 AM every Wednesday about my weekly sync." - "Analyze this PDF and extract the key financial metrics." - "I want to connect Telegram." - "Search the marketplace for a stock analysis skill and install it." ## Telegram Setup 1. Open Telegram, search **@BotFather**, send `/newbot`. 2. Follow prompts to create bot name and username. 3. Copy the Bot Token (format: `123456789:AABBccdd...`). 4. In MaxClaw, go to **Channel Setup** > **Telegram**, paste Token. 5. Save, then message your bot in Telegram to activate. ## FAQ **Can MaxClaw access local files?** No. It runs in a secure cloud sandbox. For local access, self-host OpenClaw. **Credit usage?** Deducted by token consumption. Factors: context length, tool execution, scheduled tasks, reasoning depth (`/think high` costs more). **Subscription not showing?** Platform and Agent accounts are separate. Contact support to link accounts. **Is MaxClaw just hosted OpenClaw?** Yes. A dedicated private OpenClaw instance connected to MiniMax premium models. **WeChat support?** No — unavailable due to platform policies. Use Telegram or Feishu/Lark instead. **Supported platforms?** Web (browser), Telegram, Slack, Discord, Feishu/Lark. No native mobile app yet. ## Troubleshooting If the agent is stuck: 1. Refresh page, resend message. 2. Go to Settings > **Restart**. 3. Click **Auto-fix** for diagnostics. 4. Contact support if unresolved. Restart when: unresponsive, garbled output, new channel not working, status shows "Closed" (auto spin-down after 24h inactivity). Restarting is safe — history, memory, and configs are preserved.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mnbplus/resource-hunter (version 2.1.1)

--- name: resource-hunter version: 2.1.1 description: "Find the best public route to pan links, magnets, torrents, and public video URLs for movies, TV, anime, music, software, and books. Uses layered success-first retrieval, public HTML/RSS/no-API sources, and direct/actionable/clue result ranking." metadata: {"openclaw":{"emoji":"🔎"}} --- # Resource Hunter A success-first public resource retrieval skill for: - movies - TV - anime - music - software - books - public pan links - magnets - torrents - public video URLs ## What makes it useful ### Stronger real-source coverage - Pan/direct-or-clue: `2fun`, `dalipan`, `hunhepan`, `pansou.vip`, `tieba` - Torrent/direct: `nyaa`, `animetosho`, `dmhy`, `eztv`, `tpb`, `torlock`, `yts`, `1337x` - Indexed discovery fallback: `search-index:ddg`, `search-index:bing`, `search-index:brave` ### Layered retrieval - `direct-structured-pan` - `direct-structured-torrent` - `community-clue` - `indexed-discovery` - `authenticated-connector` (reserved) ### Better result semantics Results are ranked into: - `direct` - `actionable` - `clue` That makes it easier to distinguish: - immediately usable links/magnets - results that still need light follow-up - clue-only records that point you in the right direction ## Important limitations ### Dalipan `dalipan` is currently a **token-only clue source** in release semantics. - anonymous search is confirmed - runtime emits `dalipan://provider/eu-token` - these records are structured clue output - they are **not guaranteed final share URLs** yet ### Bing / Brave - public HTML fallback only - useful for recall and indexed discovery - still best-effort providers, not stable structured direct sources ## Main entrypoint ```bash SKILL_ROOT="$(openclaw skills path resource-hunter)" SKILL_DIR="$SKILL_ROOT/scripts" python3 "$SKILL_DIR/hunt.py" search "<query>" ``` ## Common commands ```bash python3 "$SKILL_DIR/hunt.py" search "Oppenheimer 2023" --4k python3 "$SKILL_DIR/hunt.py" search "Breaking Bad S01E01" --tv python3 "$SKILL_DIR/hunt.py" search "进击的巨人 Attack on Titan" --anime --sub python3 "$SKILL_DIR/hunt.py" search "周杰伦 无损" --music python3 "$SKILL_DIR/hunt.py" sources --probe --json python3 "$SKILL_DIR/hunt.py" video probe "https://www.bilibili.com/video/BV..." ``` ## Validation snapshot - focused runtime/source regression set: `96 passed, 1 skipped` - `ruff check skills/resource-hunter`: passed - latest GitHub `resource-hunter-ci`: green ## Notes for agent behavior - Prefer the main `hunt.py` entrypoint - Use `--quick` for short chat answers - Use `--json` for machine-consumable output - Do not oversell Dalipan as a fully direct final-link source in the current release - Do not oversell Bing/Brave as stable structured providers

path: src/resource_hunter.egg-info/dependency_links.txt
content: 


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: malicious | SkillSpector:  ()
Skill: nickzren/phi-readiness-stages (version 1.1.0)

--- name: phi-readiness-stages description: Assess the current PHI Readiness Stage (PRS) of a workload, repository, system, or environment; determine HIPAA applicability and role; identify evidence gaps; and recommend next actions using official HHS/OCR and NIST sources with conservative evidence caps. version: 1.1.0 metadata: openclaw: homepage: https://github.com/nickzren/phi-readiness-stages skillKey: phi-readiness-stages --- # PHI Readiness Stages This top-level `SKILL.md` is the canonical install and publish entrypoint for the repository root. It is suitable for Codex, Claude Code, and OpenClaw-compatible registries that publish a root skill folder directly. The detailed PRS assessment workflow lives in `skills/phi-readiness-review/SKILL.md`. If you are using this as an installed or published skill, always load files in this order: 1. `skills/phi-readiness-review/SKILL.md` 2. `framework/assessment-rules.md` 3. `framework/assessment-evidence-handling.md` 4. `framework/applicability-role-matrix.md` 5. `framework/stage-rubric.md` 6. `framework/evidence-levels.md` 7. `framework/evidence-freshness.md` 8. `framework/minimum-artifact-matrix.md` 9. `framework/regulatory-boundaries.md` 10. `framework/output-contract.md` Then load only the additional control, checklist, and reference files needed for the specific assessment. When a review needs rule-level traceability or a baseline-versus-policy distinction, also load `mappings/hipaa-security-rule-crosswalk.md`. If you are using the repository itself as context rather than as an installed skill, start from `AGENTS.md`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: runapi-ai/runapi-gpt-image (version 0.2.4)

--- name: gpt-image description: Generate and edit images with GPT Image through RunAPI. Use when the user asks an agent to create, edit, or transform images with GPT Image. Default to the RunAPI CLI for one-off generation; use SDKs only when the user is integrating RunAPI into an app or backend. documentation: https://runapi.ai/models/gpt-image.md provider_page: https://runapi.ai/providers/openai.md catalog: https://runapi.ai/models.md metadata: openclaw: homepage: https://runapi.ai/models/gpt-image requires: bins: - runapi install: - kind: brew formula: runapi-ai/tap/runapi bins: - runapi envVars: - name: RUNAPI_API_KEY required: false description: Optional RunAPI API key; runapi login or saved CLI config can also authenticate the runapi binary. --- # GPT Image on RunAPI Generate and edit images with GPT Image through RunAPI. The default path for one-off agent tasks is the `runapi` CLI; SDKs are for application integration. ## Routing decision - One-off generation, editing, or transformation for the user → use the **CLI path** with the `runapi` binary. - Building an app, backend, worker, library, or production codebase → use the **SDK integration path**. ## CLI path The `runapi` binary is the runtime dependency. Authenticate with `runapi login` (browser) or set `RUNAPI_API_KEY`; a saved CLI config also works — no required environment variable. Inspect the available actions and request fields with CLI help: ```shell runapi gpt-image --help runapi gpt-image text-to-image --help ``` Run a one-off task (synchronous — polls until the task completes): ```shell runapi gpt-image text-to-image --input-file request.json ``` Submit asynchronously and poll separately: ```shell runapi gpt-image text-to-image --async --input-file request.json runapi wait <task-id> --service gpt-image --action text-to-image ``` Available actions: `text-to-image`, `edit-image`. ## SDK integration path When integrating GPT Image into an app, backend, worker, or library — not for one-off tasks — use a RunAPI SDK package: - JavaScript / TypeScript: `@runapi.ai/gpt-image` - Ruby: `runapi-gpt_image` - Go: `github.com/runapi-ai/gpt-image-sdk/go` ## References - Model overview, pricing, and rate limits: https://runapi.ai/models/gpt-image.md - Provider comparison: https://runapi.ai/providers/openai.md - Full model catalog: https://runapi.ai/models.md ## Variants - [GPT Image 1.5 text to image](https://runapi.ai/models/gpt-image/1.5-text-to-image.md) - [GPT Image 1.5 image to image](https://runapi.ai/models/gpt-image/1.5-image-to-image.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: iliubo1104-sketch/snap-notes (version 1.0.1)

--- name: snapnotes description: 像拍照一样捕捉碎片灵感，像专家一样治理业务情报。专为高效率办公、销售与采购打造的 AI 碎片助手。支持瞬时捕捉（Snap）、AI 实时质检补全、多维语义检索（Recall）以及定期业务热点画像分析（Analyze）。通过结构化存储与 AI 洞察，量化提升工作质量并缩短决策周期。 --- # SnapNotes📸 🚀 SnapNotes是你的个人业务“高速缓存”，旨在将杂乱的碎片信息转化为高质量的业务情报。 ## 核心价值 ### 1. 录入即治理 (Snap) 记录瞬间，AI 自动进行业务场景分析： - **实时质检**：发现缺失要素（如单价、交期、税率）并给出补全建议。 - **实体提取**：自动解析 Customer, Supplier, Product, Price 等关键实体。 ### 2. 语义打捞 (Recall) 支持基于意图的模糊检索。即使记不清原话，只要描述业务需求，AI 也能精准命中。 ### 3. 热点画像 (Analyze) 一键生成量化的业务洞察报告，自动归纳高频关注点、成本测算与风险预警，提供可复核的决策依据。 ## 常用指令 - `python3 scripts/manage_notes.py add "内容"`: 瞬时捕捉并质检信息。 - `python3 scripts/manage_notes.py search "意图"`: 语义深挖历史记录。 - `python3 scripts/manage_notes.py analyze`: 生成阶段性业务洞察报告。 - `python3 scripts/manage_notes.py list`: 列出最近的记录。 ## 业务价值 - **一次通过率提升**：减少 50% 的补录工作。 - **检索响应缩短**：耗时降低 90% 以上。 - **错误风险下降**：避免因信息碎裂导致的记忆偏差和业务失误。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (CRITICAL)
Skill: mikeclaw007/atms-nearby (version 0.1.0)

--- name: "附近取款机" description: "Find nearby ATMs. Invoke when user asks for cash withdrawal near me." --- # Nearby ATMs 用途 - 提供用户当前位置附近的 ATMs 列表 - 统一返回字段与查询行为，便于前端/接口复用 触发条件 - 用户询问“取款机 附近 / ATMs near me / cash withdrawal” 输入参数 - location: 经纬度 { lat, lng }，必填 - radius_meters: 查询半径，默认 3000 - limit: 返回数量上限，默认 20，最大 50 - filters: 可选筛选（是否可存款、是否免手续费等） 响应字段 - 统一参见 [STANDARD_RESPONSE.md](file:///Users/mac_lkm/workspace/trae/100fj/.trae/skills/STANDARD_RESPONSE.md) - 本技能 category 固定为 "atms" 错误码 - INVALID_LOCATION: 经纬度不合法 - RADIUS_TOO_LARGE: 超过最大查询半径 - PROVIDER_UNAVAILABLE: 数据源不可用 - RATE_LIMITED: 触发速率限制 示例 - 输入: { location: { lat: 30.123, lng: 120.456 }, radius_meters: 2000, limit: 10 } - 输出: 标准 POI 列表（见 STANDARD_RESPONSE.md） 隐私与速率限制 - 仅在用户授权定位后查询 - 避免保留精确坐标，必要时进行网格化模糊处理


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