OpenClaw/clawhub-security-signals huggingface dataset:
Skill: peetzweg/wheel-of-fortune (version 1.0.0)

# Glucksrad — Decision Wheel Use this skill when a user is stuck choosing between multiple options and wants a fun, random way to decide. Generate a URL that opens an interactive 3D spinning wheel. ## When to use - The user can't decide between a few options (e.g. "where should we eat?", "which movie should we watch?") - The user explicitly asks you to pick one at random or spin a wheel - There are 2–20 concrete options to choose from Do NOT use this when the user needs a reasoned recommendation — only when randomness is welcome. ## URL format ``` https://makedecisionforme.netlify.app/?items=Option1:Weight,Option2:Weight,Option3:Weight ``` ### Rules - **Base URL**: `https://makedecisionforme.netlify.app/` - **Query parameter**: `items` — comma-separated list of entries - **Entry format**: `Name:Weight` - `Name` — the option label. URL-encode special characters (spaces → `%20`, `&` → `%26`, etc.) - `Weight` — optional integer (defaults to `1`). Higher weight = larger slice on the wheel. Use weights when the user indicates a preference or when options aren't equally likely. - Items are separated by commas (`,`). Do not add spaces between items. ### Examples Equal chances: ``` ?items=Pizza,Sushi,Tacos,Burgers ``` Weighted (Pizza is 3x more likely than Sushi): ``` ?items=Pizza:3,Sushi:1,Tacos:2 ``` Names with spaces: ``` ?items=Thai%20Food,Fish%20and%20Chips,Mac%20%26%20Cheese ``` ## How to respond 1. Collect the options from the user's message. 2. Build the URL with the `items` query parameter. 3. Present the link to the user so they can click it and spin the wheel.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/ai-intelligent-demand-forecasting (version 1.0.0)

--- name: ai-intelligent-demand-forecasting version: 1.0.0 description: 需求预测，销售预测 + 补货计划。 metadata: openclaw: emoji: "🤖" requires: bins: [] --- # AI 智能需求预测系统 ## 描述 需求预测，销售预测 + 补货计划。 ## 功能 - 销售预测（AI预测） - 季节分析（季节因子） - 促销影响（促销调整） - 补货计划（自动补货） - 预测准确率（误差分析） ## 定价 - 基础版：¥299/月（100 SKU） - 专业版：¥1499/月（1000 SKU） - 企业版：¥4999/月（无限 SKU） ## 适用场景 - 销售预测 - 库存计划 - 供应链 - 零售预测 ## 技术栈 - Python + FastAPI - 时序预测 - 机器学习 - 误差分析 ## 安装 ```bash git clone https://github.com/openclaw-skills/ai-intelligent-demand-forecasting cd ai-intelligent-demand-forecasting pip install -r requirements.txt python app.py ``` --- 创建：2026-03-13 作者：OpenClaw Skills Team


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: flath12/entropyfa (version 0.1.2)

--- name: entropyFA Financial Planning description: Verified financial planning data and blazing-fast, deterministic calculators for Monte Carlo projection, goal solving, Roth conversions, RMDs, income tax, estate tax, and pension analysis. metadata: {"openclaw":{"homepage":"https://github.com/Entropy-Financial-Technologies/entropyfa-cli/tree/main/integrations/openclaw/entropyfa","requires":{"bins":["entropyfa"]}}} --- # entropyFA Financial Planning Use this skill when the user needs source-backed financial planning reference data or deterministic calculations from the local `entropyfa` CLI. ## Workflow 1. If the user needs to discover available reference data, run `entropyfa data coverage`. 2. If the user needs an official value, trust status, or source URL, run `entropyfa data lookup ...`. 3. If the user needs a compute command and the required inputs are unclear, run `entropyfa compute <command> --schema` first. 4. When the inputs are known, run `entropyfa compute <command> --json '<JSON>'`. 5. Use `entropyfa compute projection --visual --json ...` only when the user explicitly wants terminal visuals. Keep projection JSON-only otherwise. ## Behavior - Prefer `data lookup` over recollection when a shipped dataset exists. - Surface `verification_status`, `pipeline_reviewed`, and `sources` when provenance matters. - Do not invent annual tax or planning thresholds when a lookup key exists. - Keep machine-readable JSON on `stdout` and treat visuals as human-facing `stderr` output. For fuller workflow guidance, read [references/workflows.md](references/workflows.md). For exact commands and prompt-style examples, read [references/examples.md](references/examples.md).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: linuszz/sensitivity-charts (version 1.0.0)

--- name: sensitivity-charts description: "Visualize how changes in variables affect outcomes. Use for risk analysis and decision support." --- # Sensitivity Charts ## Metadata - **Name**: sensitivity-charts - **Description**: Sensitivity analysis and tornado charts - **Triggers**: sensitivity, tornado chart, what-if, variable impact ## Instructions Perform sensitivity analysis for $ARGUMENTS. ## Framework ### Tornado Chart ``` Base Case: $100M │ ┌─────────┼─────────┐ │ +$20M│ │-$10M Price ├─────────┼─────────┤ │ +$15M │ │-$8M Volume ├─────────┼─────────┤ │ +$8M │ │-$12M Cost ├─────────┼─────────┤ │ +$5M │ │-$3M Mix └─────────┼─────────┘ │ ▼ ``` ### Variables to Test | Variable | Range | Impact | |----------|-------|--------| | Price | ±10% | High | | Volume | ±20% | High | | Cost | ±15% | Medium | | Mix | ±5% | Low | ## Output ``` ## Sensitivity Analysis: [Decision/Project] ### Variable Impact Ranking | Variable | Base | Low Case | High Case | Impact | |----------|------|----------|-----------|--------| | Price | $100 | $85 (-15%) | $115 (+15%) | $30M | | Volume | 1M | 800K | 1.2M | $25M | | Cost | $50 | $45 | $55 | $20M | | Mix | 50/50 | 40/60 | 60/40 | $10M | ### Scenario Analysis | Scenario | NPV | Probability | Expected Value | |----------|-----|-------------|----------------| | Base Case | $100M | 50% | $50M | | Optimistic | $150M | 20% | $30M | | Pessimistic | $60M | 30% | $18M | | **Expected** | | | **$98M** | ### Key Findings 1. **Most sensitive**: Price (±$30M impact) 2. **Least sensitive**: Mix (±$10M impact) 3. **Breakeven**: [Analysis] ### Risk Mitigation 1. [Mitigation for most sensitive variable] 2. [Mitigation for second most sensitive] ``` ## Tips - Focus on top 5-7 variables - Use realistic ranges - Show both upside and downside


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: teoslayer/pilot-swarm-join (version 1.0.0)

--- name: pilot-swarm-join description: > Join or create agent swarms with auto-discovery and peer mesh formation. Use this skill when: 1. An agent needs to join an existing swarm or create a new one 2. You need to discover and connect to peers in a named swarm 3. You want to establish bidirectional trust with swarm members Do NOT use this skill when: - You only need point-to-point communication (use pilot-p2p-connect) - You need structured topologies like star or tree (use pilot-formation) tags: - pilot-protocol - swarm - coordination - discovery license: AGPL-3.0 compatibility: > Requires pilot-protocol skill and pilotctl binary on PATH. The daemon must be running (pilotctl daemon start). metadata: author: vulture-labs version: "1.0" openclaw: requires: bins: - pilotctl homepage: https://pilotprotocol.network allowed-tools: - Bash --- # pilot-swarm-join Join or create named agent swarms with automatic peer discovery and mesh trust establishment. ## Commands ### Join Swarm ```bash pilotctl --json publish "registry-hostname" "swarm:$SWARM_NAME" --data "{\"agent\":\"$AGENT_ID\",\"role\":\"worker\"}" ``` ### Subscribe to Swarm ```bash pilotctl --json subscribe "registry-hostname" "swarm:$SWARM_NAME" ``` ### Discover Peers ```bash PEERS=$(pilotctl --json inbox | jq -r '.messages[] | select(.topic == "swarm:'$SWARM_NAME'") | .sender') ``` ### Handshake with Peers ```bash for peer in $PEERS; do pilotctl --json handshake "$peer" "Joining swarm $SWARM_NAME" done ``` ### Approve Peers ```bash pilotctl --json pending | jq -r '.[].node_id' | xargs -I {} pilotctl --json approve {} ``` ### Leave Swarm ```bash pilotctl --json publish "registry-hostname" "swarm:$SWARM_NAME" --data "{\"agent\":\"$AGENT_ID\",\"action\":\"leave\"}" ``` ## Workflow Example ```bash #!/bin/bash # Join compute swarm SWARM_NAME="compute-cluster-01" AGENT_ID=$(pilotctl --json info | jq -r '.node_id') REGISTRY_HOST="registry.example.com" # Announce presence pilotctl --json publish "$REGISTRY_HOST" "swarm:$SWARM_NAME" \ --data "{\"agent\":\"$AGENT_ID\",\"role\":\"worker\",\"joined_at\":\"$(date -u +%Y-%m-%dT%H:%M:%SZ)\"}" # Subscribe to swarm updates pilotctl --json subscribe "$REGISTRY_HOST" "swarm:$SWARM_NAME" sleep 2 # Discover peers PEERS=$(pilotctl --json inbox | jq -r '.messages[] | select(.topic == "swarm:'$SWARM_NAME'") | .payload.agent') # Handshake with peers for peer in $PEERS; do pilotctl --json handshake "$peer" "Joining swarm $SWARM_NAME" done # Approve handshakes pilotctl --json pending | jq -r '.[].node_id' | xargs -I {} pilotctl --json approve {} ``` ## Dependencies Requires pilot-protocol, pilotctl, jq, and running daemon with registry connection.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: 52yuanchangxing/meeting-to-kanban (version 1.0.0)

--- name: meeting-to-kanban description: Convert meeting notes or transcripts into a clean Kanban board with owners, due dates, blockers, and next actions. version: 1.1.0 metadata: openclaw: requires: bins: - python3 emoji: 🧰 --- # Meeting to Kanban ## Purpose Convert meeting notes or transcripts into a clean Kanban board with owners, due dates, blockers, and next actions. ## Trigger phrases - 会议纪要转任务 - meeting notes to kanban - 把会议变成看板 - action items from transcript - 给我做项目任务板 ## Ask for these inputs - meeting notes/transcript - board columns - project context - time horizon - participants list if available ## Workflow 1. Extract decisions, action items, risks, and follow-ups. 2. Map actions into board columns such as Backlog, Next, Doing, Waiting, Done. 3. Assign owners and due dates when explicit; otherwise mark as unresolved. 4. Generate a CSV or Markdown board using the bundled columns schema. 5. Return a short manager summary and unresolved questions. ## Output contract - kanban CSV - manager summary - owners and due dates table - open questions list ## Files in this skill - Script: `{baseDir}/scripts/tasks_to_kanban.py` - Resource: `{baseDir}/resources/board-columns.yaml` ## Operating rules - Be concrete and action-oriented. - Prefer preview / draft / simulation mode before destructive changes. - If information is missing, ask only for the minimum needed to proceed. - Never fabricate metrics, legal certainty, receipts, credentials, or evidence. - Keep assumptions explicit. ## Suggested prompts - 会议纪要转任务 - meeting notes to kanban - 把会议变成看板 ## Use of script and resources Use the bundled script when it helps the user produce a structured file, manifest, CSV, or first-pass draft. Use the resource file as the default schema, checklist, or preset when the user does not provide one. ## Boundaries - This skill supports planning, structuring, and first-pass artifacts. - It should not claim that files were modified, messages were sent, or legal/financial decisions were finalized unless the user actually performed those actions. ## Compatibility notes - Directory-based AgentSkills/OpenClaw skill. - Runtime dependency declared through `metadata.openclaw.requires`. - Helper script is local and auditable: `scripts/tasks_to_kanban.py`. - Bundled resource is local and referenced by the instructions: `resources/board-columns.yaml`.

path: CHANGELOG.md
content: # Changelog

## 1.0.0
- Initial release.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ironiclawdoctor-design/93pct (version 1.0.0)

--- name: 93pct description: Autocomplete for agency next steps. Like Google autocomplete but for what to do next. Given any context, returns the top concrete viable actions ranked by ROI/effort ratio. Always outputs exactly one job ID to approve. Named after the 93% standard — every suggestion must clear that bar. version: 1.0.0 author: Fiesta tags: [planning, autocomplete, next-steps, agency, productivity] --- # 93% — Agency Autocomplete ## Doctrine > "Plan Shannon → Penn Station" Shannon is the currency of information (Claude Shannon, entropy theory). Penn Station is the commuter hub — where everything converges. The plan: route all decisions through Shannon (information value) to reach the station (execution). Low Shannon = entropy = noise = waste. High Shannon = signal = viable next step. ## Usage ``` python3 /root/.openclaw/workspace/skills/93pct/suggest.py # top 5 next steps now python3 /root/.openclaw/workspace/skills/93pct/suggest.py --stack # show approval stack python3 /root/.openclaw/workspace/skills/93pct/suggest.py --done <id> # mark completed ``` ## Output format Each suggestion includes: - Shannon score (0-10, information density) - Effort (minutes) - Blocker (what stops it) - Job ID to approve (if exec needed) - Exact command or action ## The 93% Bar A suggestion clears 93% if: - It is concrete (not "consider doing X") - It is viable right now (blocker is known and solvable) - It produces measurable output - It costs less than it returns


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: stale | SkillSpector: suspicious (CRITICAL)
Skill: jasonzhangshuo/openclaw-safety-guard (version 0.1.1)

--- name: openclaw-safety-guard description: "OpenClaw 安全卫士（发布 slug：openclaw-safety-guard，内部代号：openclaw-watchdog）：默认工作日 10:00 自动扫描 7 个维度（安全、记忆、心跳、定时任务、共享文件、通讯、代码规范），生成健康评分和可视化 Dashboard，通过飞书私信发送报告；没有 Cursor 也可直接看 dashboard 和下载 JSON。用户说「体检」「生成健康大盘」「跑安全检查」时手动触发。" metadata: clawdbot: emoji: "🛡️" requires: bins: [python3, node, npm] env: [FEISHU_APP_ID, FEISHU_APP_SECRET] primaryEnv: FEISHU_APP_ID --- # OpenClaw 安全卫士（openclaw-safety-guard） OpenClaw 安全卫士的 Clawhub 发布 slug 为 `openclaw-safety-guard`，内部技术代号仍为 `openclaw-watchdog`。它是 OpenClaw 项目全链路健康监控工具，默认工作日自动扫描 7 个维度，生成评分和可视化 Dashboard，通过飞书发送报告。没有 Cursor 也可以直接查看 dashboard 和下载 JSON 日志。 ## 安装（clawhub） ```bash clawhub install openclaw-safety-guard ``` 安装完成后，Agent 立即执行 post-install 初始化： ```bash python3 {baseDir}/scripts/setup.py --receive_id <当前对话 sender 的 Feishu open_id> ``` `--receive_id` 由 Agent 从当前会话 context 中自动获取（即安装者本人的 open_id），无需手动填写。 setup.py 自动完成： 1. 写入 `{baseDir}/config.json`（绑定通知接收人） 2. 在 `.openclaw/state/cron/jobs.json` 注册每日扫描任务 3. **立即执行一次完整扫描**，安装者 30 秒内收到第一份飞书健康报告 > ⚠️ 安装完成后重启 Gateway 使 cron 生效：`openclaw gateway restart` ## 前置条件 - `FEISHU_APP_ID` 和 `FEISHU_APP_SECRET` 已配置在 Gateway plist 的 `EnvironmentVariables` 中 - 本机已安装 `node`（用于首次 build frontend） - OpenClaw Gateway 已运行 ## 触发方式 1. **每日自动**：安装时注册的 cron job，默认工作日 10:00（Asia/Shanghai）触发 2. **手动触发**：在飞书群对 Agent 说 `体检`、`生成健康大盘`、`跑一遍安全检查` ## 执行流程 ``` 触发 ↓ Step 1: 执行 7 个探针扫描 python3 {baseDir}/scripts/run_pipeline.py 内部依次调用： scan_heartbeat.py → 心跳监控维度 scan_standards.py → 代码规范维度 scan_memory.py → 记忆健康维度 scan_cron.py → 定时任务维度 scan_shared.py → 共享文件维度 scan_comm.py → 通讯配置维度 scan_security.py → 安全维度 ↓ Step 2: 聚合评分（含与上次对比的 score_delta） aggregate_watchdog.py ↓ Step 3: 生成可视化 Dashboard HTML（含飞书 bot 头像） generate_dashboard.py ↓ Step 4: 按次归档到 data/logs/YYYY-MM-DD_HH-MM/ ↓ Step 5: 飞书私信通知（含健康分、问题列表、Dashboard 本地路径） notify_feishu.py ↓ Step 6: 自动执行 GREEN 安全修复（仅 chmod 类低风险操作） fix_green.py ``` ## 手动重发通知 ```bash python3 {baseDir}/scripts/notify_feishu.py ``` ## 配置文件 `config.json` 由 setup.py 自动生成，参考 `config.example.json` 了解全部可配项。 关键字段： | 字段 | 说明 | |---|---| | `notify.receive_id` | 接收飞书通知的 open_id（setup.py 自动填入） | | `notify.score_recovery_threshold` | 分数提升超过此值时用「恭喜」语气（默认 3） | | `memory.checks[cross_workspace_conflict].tracked_concepts` | 自定义需要跨 workspace 监控一致性的概念 | | `security.checks[knowledge_freshness].target` | 知识库目录路径（可选，不填则跳过此检查） | ## 关键文件 | 文件 | 说明 | |---|---| | `data/latest_status.json` | 本次扫描完整结果（含 score_delta） | | `data/dashboard.html` | 最新 Dashboard，浏览器本地打开 | | `data/history.json` | 问题生命周期历史（first_seen / resolved_at） | | `data/logs/YYYY-MM-DD_HH-MM/` | 按次归档的原始扫描日志（最多保留 30 次） | | `config.json` | 运行时配置（由 setup.py 生成，不提交到 git） | | `config.example.json` | 配置模板（可提交到 git） | ## 错误处理 | 失败步骤 | 降级策略 | |---|---| | 探针执行报错 | 该维度显示 N/A，不影响其他维度 | | Dashboard 生成失败 | 通知仍发出，不含 Dashboard 路径 | | 飞书通知失败 | 打印 ERROR 日志，pipeline 不中断 | | Bot info 获取失败 | Dashboard 显示默认缩写，不影响报告发送 |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: anmu14641/anime-update (version 1.0.0)

--- name: anime-update description: 查询国内动漫动画的最新更新信息。包括：(1) 查询特定动漫的最新集数和更新时间；(2) 查询本周/本月新番更新时间表；(3) 动漫推荐。适用于用户问"XX动漫更新到第几集了"、"这周有什么新番"、"最近有什么好看的动漫"等场景。 --- # 动漫更新时间查询 本 Skill 帮助你查询国内动漫的最新更新信息。 ## 数据来源 通过 Web 搜索获取最新的动漫更新数据。常用来源： - 哔哩哔哩 (bilibili.com) - 豆瓣动漫 - 百度动漫吧 - 新浪动漫 ## 使用方式 ### 1. 查询特定动漫最新集数 用户可能问： - "《咒术回战》更新到第几集了？" - "《进击的巨人结了吗？" 》最终季完- "鬼灭之刃锻刀村篇最新一集" 执行流程： 1. 提取动漫名称 2. 搜索 "[动漫名] 更新到第几集" 或 "[动漫名] 最新集数" 3. 提取最新集数、更新日期、平台信息 4. 返回结构化结果 ### 2. 查询本周新番更新时间 用户可能问： - "这周有什么新番要播？" - "本周动漫更新表" - "2024年冬季新番列表" 执行流程： 1. 确定当前日期和季节（冬/春/夏/秋） 2. 搜索 "[季节] 新番更新时间表" 或 "本周动漫更新列表" 3. 整理成表格返回 ### 3. 动漫推荐 用户可能问： - "最近有什么好看的动漫推荐？" - "2024必看动漫" - "热血动漫推荐" 执行流程： 1. 搜索近期热门动漫推荐 2. 筛选评分较高、评价好的作品 3. 整理成推荐列表（包含简介、类型、评分） ## 输出格式 ### 单个动漫查询 ``` 📺 [动漫名] - 最新集数：第 X 集 - 更新日期：XXXX-XX-XX - 播放平台：哔哩哔哩 / 优酷 / etc. - 状态：更新中 / 已完结 ``` ### 新番列表 ``` 📅 本周更新 (2024-XX-XX ~ XX) | 动漫名 | 类型 | 更新日期 | 集数 | |--------|------|----------|------| | ... | ... | ... | ... | ``` ## 注意事项 - 国内平台通常比日本晚 1-2 周更新 - 剧场版和OVA通常单独计算 - 完结时间可能因番剧而异 - 部分动漫可能有多季，注意区分


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: duoduoks/seo-article-generator (version 1.0.0)

# SEO Article Generator Automatically generate SEO-optimized articles for your website to drive organic traffic. ## Features - 🚀 Generate 500+ word SEO articles using DeepSeek AI - 🎯 40+ topic pool covering AI tools, side hustles, digital marketing - 📝 Auto-creates HTML files with proper heading structure - 🔗 Built-in affiliate links to your services - 🌐 Auto-updates sitemap.xml for search engines ## Installation ```bash openclaw skills install seo-article-generator ``` ## Usage The skill automatically generates one SEO article per hour. To trigger manually: ``` Generate a new SEO article about [your topic] ``` ## Example Output The skill creates HTML articles in `./articles/` directory with: - SEO-optimized title and headings (H1, H2, H3) - 500-800 words of original content - Internal links to your services/products - Auto-registration in sitemap.xml ## Configuration No configuration needed. The skill uses your default OpenAI-compatible API provider.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: kellyclaudeai/openclaw-deck (version 0.1.0)

--- name: openclaw-deck description: Multi-column chat deck UI for OpenClaw agents. Launch a local web interface to manage and chat with multiple agents side-by-side. user-invocable: true metadata: {"openclaw":{"emoji":"🦞","requires":{"bins":["node","npm"]},"os":["darwin","linux","win32"]}} --- # OpenClaw Deck Launch the OpenClaw Deck — a multi-column web UI for chatting with OpenClaw agents side-by-side. ## What this skill does When invoked, install dependencies (if needed) and start the Vite dev server for the deck UI. The deck connects to the local OpenClaw Gateway via WebSocket proxy. ## Instructions 1. Check if `node_modules` exists in the skill's base directory. If not, run `npm install` in `{baseDir}`. 2. Start the dev server by running `npm run dev` in `{baseDir}`. 3. Tell the user the deck is available at **http://localhost:5173**. 4. Mention available keyboard shortcuts: - **Tab** / **Shift+Tab** — move focus between agent column inputs - **Cmd+1–9** — jump to a specific column by number - **Cmd+K** — open the Add Agent modal ## Requirements - The OpenClaw Gateway must be running on `ws://127.0.0.1:18789` (default), or the user must set `VITE_GATEWAY_URL` in `.env`. - Node.js and npm must be installed.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: melvynx/turso (version 1.0.0)

--- name: turso description: "Manage Turso SQLite databases via CLI - databases, groups, tokens, replicas. Use when user mentions 'turso', 'libsql', 'sqlite edge', or wants to manage Turso databases." category: devtools install_command: "brew install tursodatabase/tap/turso" --- # turso ## Setup macOS: ```bash brew install tursodatabase/tap/turso ``` Linux/WSL: ```bash curl -sSfL https://get.tur.so/install.sh | bash ``` Verify installation: ```bash turso --version ``` Always use `--output json` flag when calling commands programmatically (where supported). ## Authentication ```bash turso auth login ``` Get auth token: ```bash turso auth token ``` ## Resources ### Databases | Command | Description | |---------|-------------| | `turso db list` | List all databases | | `turso db create <name>` | Create a new database | | `turso db create <name> --group <group>` | Create database in a specific group | | `turso db destroy <name>` | Destroy a database | | `turso db show <name>` | Show database details and URL | | `turso db shell <name>` | Open interactive SQL shell | | `turso db shell <name> "SELECT * FROM users"` | Run a SQL query directly | | `turso db inspect <name>` | Inspect database size and usage | | `turso db tokens create <name>` | Create an auth token for a database | | `turso db tokens create <name> --expiration none` | Create a non-expiring token | ### Groups | Command | Description | |---------|-------------| | `turso group list` | List all groups | | `turso group create <name>` | Create a new group | | `turso group create <name> --location <location>` | Create group in specific location | | `turso group add-location <name> <location>` | Add a replica location to a group | | `turso group remove-location <name> <location>` | Remove a replica location | | `turso group destroy <name>` | Destroy a group | ### Organizations | Command | Description | |---------|-------------| | `turso org list` | List organizations | | `turso org switch <name>` | Switch to a different organization | ### Plan | Command | Description | |---------|-------------| | `turso plan show` | Show current plan details | | `turso plan upgrade` | Upgrade your plan | ## Global Flags | Flag | Description | |------|-------------| | `--output json` | Output result as JSON | | `--no-color` | Disable colored output |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: timo2026/travel-master-mini (version 1.0.0)

--- name: travel-master-mini version: 1.0.0 description: 旅游大师极简版 - 数学收敛+关键词匹配，无外部依赖 author: Timo2026 license: MIT priority: P0 keywords: - 旅游规划 - 数学收敛 - 极简版 triggers: - 旅游规划 config: GAODE_API_KEY: optional output: type: text delivery: text security: no_external_llm: true no_async: true no_flask: true no_aiohttp: true no_exec_eval: true no_subprocess: true no_hardcoded_paths: true no_daemon_scripts: true no_watchdog: true user_managed_service: true verified: ClawHub-Mini-V1 --- # 旅游大师极简版 🦞 > **数学收敛守卫 - 关键词匹配 - 无外部依赖** --- ## 核心功能 | 功能 | 说明 | |------|------| | **数学收敛** | 7字段收敛度计算 | | **关键词匹配** | 本地正则提取 | | **无外部依赖** | 完全本地实现 | --- ## 使用方法 ```python from core.mini_engine import MiniEngine engine = MiniEngine() result = engine.process("五一去敦煌玩5天") print(result) ``` --- ## 收敛度公式 ```python convergence_rate = confirmed_fields / 7 ``` --- ## 安全合规 ✅ 无外部依赖 ✅ 无硬编码路径 ✅ 无守护脚本 ✅ 完全本地实现 --- 🦫 海狸 | 靠得住、能干事、在状态


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