OpenClaw/clawhub-security-signals huggingface dataset:
Skill: sdk-team/alibabacloud-odps-sql-generation (version 0.0.1)

--- name: alibabacloud-odps-sql-generation description: "Provides MaxCompute SQL intelligent generation capabilities for AI agents, covering text2sql conversion principles, dialect syntax differences (DQL/DDL/DML), common query pattern templates (Top N, PIVOT, window functions, etc.), and ODPS error code diagnostics. Use when generating, debugging, or migrating MaxCompute / ODPS SQL." license: Apache-2.0 metadata: domain: coding owner: MaxCompute contact: [REDACTED_SECRET], [REDACTED_SECRET] version: 1.0.0 --- # MaxCompute SQL Engine Syntax Skill Provides MaxCompute SQL engine syntax guidance for text2sql scenarios. MaxCompute is based on Hive SQL extensions and has significant differences from ANSI standard SQL. ## Usage Load corresponding reference files based on question type (can be combined). The `load` column indicates relative loading weight (heavy ≈ large, medium ≈ moderate), for budget estimation, not exact token counts. Each file's opening paragraph describes its own scope. | Trigger condition | File | load | |---|---|---| | NL→SELECT generation / text2sql (determine intent, granularity, table-column mapping, output format first) | `references/text2sql_principles.md` | light | | Generate MaxCompute SELECT queries (dialect rules, DQL default must-read) | `references/maxcompute_select_guide.md` | heavy | | Match query pattern keywords: Top N / top-N per group / year-over-year / month-over-month / consecutive N days / retention / row-to-column / column-to-row / PIVOT / UNPIVOT / array expansion / LATERAL VIEW / EXPLODE / JSON extraction / GET_JSON_OBJECT / cumulative / running total / Range Join / GROUPING SETS / CUBE / ROLLUP / pagination / paging | `references/sql_query_patterns.md` | medium | | SQL execution failure requiring diagnosis and recovery (including `ODPS-0xxx` error codes)| `references/sql_common_errors.md` | medium | > Relationship: `text2sql_principles.md` provides engine-independent NL→SELECT generation principles; `maxcompute_select_guide.md` is the **single authoritative source** for MaxCompute DQL dialect rules (unsupported syntax/functions/partitioning/types/SET parameters). `sql_query_patterns.md` provides **query template snippets** only, without duplicating rules. ## Out of Scope The following scenarios exceed this skill's scope: - **MaxCompute non-SQL interfaces**: Tunnel / MapReduce / Graph / PyODPS DataFrame API, SDK invocation methods - **Console and permission management**: Quota requests, IAM / RAM roles, project owner operations — use Aliyun console or support tickets - **Execution plan-level deep tuning**: Only lists common SET parameters and hints; does not analyze specific plan nodes / Fuxi DAG / data skew formation paths - **Cluster-side / platform-side failures**: Worker crashes, resource scheduling failures, MetaStore transaction conflicts, storage layer read/write errors — these are support ticket issues, not SQL issues


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)
Skill: zhouchang1988/publish-skill-repo (version 2.1.0)

--- name: publish-skill description: 将当前项目中的 Skill 发布到 GitHub 并同步到 ClawHub。新项目自动走完流程，已有仓库的项目会总结变更、确认提交信息和建议版本号。 triggers: - 发布skill - 上线skill - 发布技能 - 上线技能 - publish skill - deploy skill --- # Publish Skill 将当前项目中的 Skill 发布到 GitHub 仓库并同步到 ClawHub。 ## 前置条件 在执行之前，先检查以下条件是否满足： 1. **当前目录包含 `SKILL.md`** — 如果没有，提示用户这不是一个 skill 项目 2. **gh CLI 已安装且已登录** — 运行 `gh auth status` 验证 3. **ClawHub Token 已配置** — 检查 `~/.clawhub/secret_token` 文件是否存在（如果仓库已有 CLAWHUB_TOKEN secret 则不需要） 如果前置条件不满足，给出具体提示并停止。 ## 判断项目类型 检查当前项目状态，判断是 **新项目** 还是 **已有项目**： - 如果当前目录没有 `.git`，或 git remote 没有指向 GitHub → **新项目** - 如果当前目录有 `.git` 且 remote 指向 GitHub 仓库 → **已有项目** ## 新项目流程 对于新项目，按以下步骤自动执行： 1. 运行脚本 `scripts/publish-skill-repo.sh <当前目录>` 走完整个流程 2. 脚本会自动完成：git init、创建 LICENSE、创建 workflow、创建 GitHub 仓库、设置 secret、提交推送、创建 v1.0.0 tag 3. 完成后向用户汇报结果 ## 已有项目流程 对于已有项目，需要用户确认后再执行： ### 步骤 1：总结变更 检查自上次 tag 以来的变更： ```bash LATEST_TAG="$(git describe --tags --abbrev=0 2>/dev/null || echo '')" if [[ -n "$LATEST_TAG" ]]; then git log "${LATEST_TAG}..HEAD" --oneline git diff "${LATEST_TAG}..HEAD" --stat else git log --oneline -20 git diff --stat fi ``` 也检查当前是否有未提交的变更： ```bash git status --short git diff --stat ``` 将变更整理为简洁的中文总结，包含： - 提交历史摘要 - 修改的文件和主要改动 - 是否有未提交的变更 ### 步骤 2：确认提交信息 基于变更内容，生成建议的 commit message。使用 AskUserQuestion 让用户确认或修改： - 展示建议的 commit message - 用户可以直接确认，也可以修改 ### 步骤 3：确认版本号 基于变更内容判断版本号升级类型： | 变更类型 | 版本升级 | 示例 | |---------|---------|------| | 新功能 / feat | minor | v1.0.0 → v1.1.0 | | Bug 修复 / fix | patch | v1.1.0 → v1.1.1 | | 破坏性变更 | major | v1.1.0 → v2.0.0 | | 仅文档 / chore | patch | v1.1.0 → v1.1.1 | 使用 AskUserQuestion 让用户确认版本号： - 展示建议的版本号 - 用户可以直接确认，也可以输入自己想要的版本号 ### 步骤 4：执行发布 确认完成后，执行以下操作： 1. 如果有未提交的变更，用用户确认的 commit message 提交 2. 如果当前不在主分支，合并当前分支到主分支（主分支通过 `git symbolic-ref refs/remotes/origin/HEAD` 自动检测，兼容 main 和 master） 3. 推送到远程主分支 4. 创建用户确认的 tag 并推送到远程 5. 向用户汇报完成结果 如果是已有仓库但缺少 LICENSE、workflow 或 secret，运行 `scripts/publish-skill-repo.sh <当前目录>` 补齐这些文件，然后再执行发布。 ## 脚本说明 `scripts/publish-skill-repo.sh` 是底层自动化脚本，也可以由用户直接运行： ```bash ./scripts/publish-skill-repo.sh /path/to/your-skill [github-owner] [public|private] ``` 脚本会自动处理幂等性（跳过已存在的文件和配置），可安全重复运行。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ken0122/weekly-skills-update (version 0.1.0)

--- name: weekly-skills-update description: 每周自动更新技能并推送摘要。当收到"执行技能每周更新"或"skills-weekly-update"触发时，运行 clawhub update --all 并更新 SKILLS_INDEX.md，最后推送摘要给用户。 --- # 每周技能自动更新 ## 触发条件 当收到以下任一消息时激活： - "执行技能每周更新" - "skills-weekly-update" - "运行技能更新脚本" ## 执行流程 ### 1. 执行更新命令 ```bash cd ~/.openclaw/workspace clawhub update --all 2>&1 | tee /tmp/skills-update-output.txt ``` ### 2. 解析更新结果 统计： - 更新成功的技能数量（✓ 标记） - 已是最新的技能数量（up to date） - 更新失败的数量（error/failed） ### 3. 更新 SKILLS_INDEX.md 如果技能有增减： 1. 重新扫描技能目录 2. 更新技能总数 3. 更新「最后更新」日期 4. 在更新日志中添加记录 ### 4. 生成摘要并推送 摘要格式： ``` 🌀 **技能每周更新摘要** **更新时间**: YYYY-MM-DD HH:MM **结果**: - ✅ 更新成功：X 个 - ⏭️ 已是最新：Y 个 - ❌ 更新失败：Z 个 **更新详情**: - skill-name-1: v1.0.0 → v1.0.1 - skill-name-2: v1.2.0 → v1.3.0 **SKILLS_INDEX.md**: 已更新/无需更新 --- *下次更新：下周日 09:00* ``` ## 错误处理 - 如果 clawhub 命令不存在：提示用户安装 `npm i -g clawhub` - 如果更新失败：记录错误并继续 - 如果网络问题：重试 2 次，间隔 30 秒 ## 注意事项 - 更新前不备份（clawhub 自动处理） - 推送摘要到当前会话（飞书/主会话） - 更新日志保存到 `~/clawd/workspace/logs/skills-update-YYYYMMDD.md`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: googleworkspace-bot/recipe-plan-weekly-schedule (version 1.0.12)

--- name: recipe-plan-weekly-schedule description: "Review your Google Calendar week, identify gaps, and add events to fill them." metadata: version: 0.22.5 openclaw: category: "recipe" domain: "scheduling" requires: bins: - gws skills: - gws-calendar --- # Plan Your Weekly Google Calendar Schedule > **PREREQUISITE:** Load the following skills to execute this recipe: `gws-calendar` Review your Google Calendar week, identify gaps, and add events to fill them. ## Steps 1. Check this week's agenda: `gws calendar +agenda` 2. Check free/busy for the week: `gws calendar freebusy query --json '{"timeMin": "2025-01-20T00:00:00Z", "timeMax": "2025-01-25T00:00:00Z", "items": [{"id": "primary"}]}'` 3. Add a new event: `gws calendar +insert --summary 'Deep Work Block' --start '2026-01-21T14:00:00' --end '2026-01-21T16:00:00'` 4. Review updated schedule: `gws calendar +agenda`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: billzhuang6569/recruitment-agent (version 1.3.0)

--- name: recruitment-agent description: "招聘Agent：通过 opencli(Boss直聘) + lark-cli(飞书多维表格) 管理招聘流程。支持：(1) 查看Boss直聘最近/未读消息; (2) 将候选人存入人才库（先搜索比对，再新建/更新）; (3) 添加人才决策记录（加入库/约面试/跟进/发Offer/归档）; (4) 更新人才库中某候选人的指定信息; (5) 约面试（发邀约消息、心跳检测、确认时间、创建飞书日程）。当用户说「查boss消息」「把XXX存入人才库」「我对XXX感兴趣」「对XXX做个决策记录」「更新XXX的信息/字段」「帮我给XXX约个面试」「约一下XXX」时触发。" --- # Recruitment Agent ## 重要规则 - **opencli boss 命令必须串行执行**，绝不能并行——同时运行多个会 cookie 冲突导致失败 - 候选人定位统一用 **uid** 作为唯一标识 ## 常量 > ⚠️ 安装后请将以下占位符替换为实际值。 | 名称 | 值 | |---|---| | Base Token | `<YOUR_BASE_TOKEN>` | | 人才库V3 table_id | `<YOUR_TALENT_TABLE_ID>` | | 决策记录 table_id | `<YOUR_DECISION_TABLE_ID>` | | 使用者姓名 | `<YOUR_NAME>` | | 使用者 open_id | `<YOUR_OPEN_ID>` | ## 工作流索引 ### 主工作流 | 工作流 | 触发词 | 参考 | |---|---|---| | 1. 查看Boss消息 | 查boss消息、未读消息、最近对话 | 见下方内联说明 | | 2. 存入人才库 | 把XXX存入人才库、我对XXX感兴趣 | [workflow-2-save-candidate.md](references/workflow-2-save-candidate.md) | | 3. 人才决策记录 | 对XXX做决策、约面试、发Offer、归档XXX | [workflow-3-decision-record.md](references/workflow-3-decision-record.md) | | 4. 更新人才信息 | 更新XXX的信息、给XXX加备注、修改XXX字段 | [workflow-4-update-candidate.md](references/workflow-4-update-candidate.md) | | 约面试（综合） | 帮我给XXX约个面试、约一下XXX | [workflow-schedule-interview.md](references/workflow-schedule-interview.md) | ### 辅助工具 | 工具 | 用途 | 参考 | |---|---|---| | 查看日程 | 查可用时间段，约面试流程中调用 | [util-check-calendar.md](references/util-check-calendar.md) | | 发飞书消息 | 向使用者确认信息，约面试流程中调用 | [util-send-feishu-message.md](references/util-send-feishu-message.md) | | 心跳任务 | 后台定时检查候选人回复 | [heartbeat_task.md](references/heartbeat_task.md) | --- ## 工作流 1：查看 Boss 消息（内联） ```bash # 查看聊天列表（最近联系人） opencli boss chatlist # 查看与某人的聊天记录（先从 chatlist 获取 uid） opencli boss chatmsg <uid> ``` 输出字段：name / job / last_msg / last_time / uid / security_id


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: kleberbaum/voc (version 0.1.0)

--- name: voc description: Voc namespace for Netsnek e.U. vocabulary and language learning tools user-invocable: true version: 0.1.0 metadata: openclaw: os: [linux] permissions: [exec] --- # What is Voc Voc is a reserved namespace for Netsnek e.U. vocabulary and language learning tools within the ClawHub ecosystem. It is designed for learners who want structured support for flashcard-based study, spaced repetition, pronunciation practice, and cross-language progress tracking. The skill is currently in reservation status: the namespace is claimed, the brand and structure are defined, and future releases will add full functionality. When invoked, Voc responds with brand information and available commands. # Getting Started Ensure OpenClaw 0.8.0 or newer is installed and that the voc skill is registered in your ClawHub. The skill requires `exec` permission to run its helper script. From the command line, you can run the vocab engine directly: ```bash ./scripts/vocab-engine.sh --about ./scripts/vocab-engine.sh --deck ./scripts/vocab-engine.sh --stats ``` From within a Claw session, simply mention "voc" or ask about vocabulary learning, decks, or stats, and the assistant will route to this skill. # Commands | Command | Purpose | |---------|---------| | `--about` | Show brand, copyright, and brief description of Voc | | `--deck` | Deck information and flashcard deck status | | `--stats` | Progress and statistics placeholder (future: real stats) | Each command outputs copyright information for Netsnek e.U. by default. # Conversation Examples **User:** "Tell me about voc." **Assistant:** (Uses voc skill) Voc is a vocabulary and language learning namespace by Netsnek e.U. It supports flashcard decks, spaced repetition, pronunciation practice, and progress tracking across multiple languages. The skill is currently reserved; full features are coming in future updates. --- **User:** "Run the vocab engine with deck info." **Assistant:** (Calls `vocab-engine.sh --deck`) Returns deck placeholder and copyright notice. --- **User:** "Show my voc stats." **Assistant:** (Calls `vocab-engine.sh --stats`) Returns stats placeholder and Netsnek e.U. copyright.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: rogue-agent1/sizeup (version 1.0.0)

--- name: sizeup description: Disk usage analyzer with tree view, large file finder, and extension breakdown. Like `du` but actually readable. Pure Python, zero dependencies. --- # sizeup Disk usage analyzer with tree view, large file finder, and extension breakdown. Like `du` but actually readable. Pure Python, zero dependencies. ## Usage ```bash # Tree view of current directory sizeup # Analyze specific path sizeup /var/log --depth 2 # Only show items >= 10MB sizeup --min 10M # Top 20 largest files sizeup /home --top 20 # Extension breakdown sizeup . --ext # JSON output sizeup --top 10 --json ``` ## Features - **Color-coded tree** — red (≥1GB), yellow (≥100MB), cyan (≥10MB) - **Large file finder** — `--top N` finds the biggest files fast


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sky-lv/skylv-api-error-handler (version 1.0.1)

--- description: Handles API errors with smart retry strategies and fallbacks keywords: error-handling, retry, api, resilience name: skylv-api-error-handler triggers: api error handler --- # skylv-api-error-handler > Comprehensive API error handling. Categorizes errors, suggests fixes, implements retry strategies. ## Skill Metadata - **Slug**: skylv-api-error-handler - **Version**: 1.0.0 - **Description**: Analyze and handle API errors. 8 error categories, 4 retry strategies, error logging and statistics. Integrates with self-healing ecosystem. - **Category**: error - **Trigger Keywords**: `error`, `retry`, `api`, `exception`, `rate limit`, `timeout` --- ## What It Does ```bash # Analyze an error node api_error_handler.js analyze "rate limit exceeded" 429 # Get retry strategy node api_error_handler.js retry exponential # Log errors for analysis node api_error_handler.js log "Connection timeout" "api.openai.com" # View statistics node api_error_handler.js stats ``` --- ## Error Categories | Category | HTTP Codes | Retryable | Severity | |----------|------------|-----------|----------| | rate-limit | 429 | YES | warning | | timeout | 408, 504 | YES | warning | | auth | 401, 403 | NO | critical | | validation | 400, 422 | NO | error | | server | 500-504 | YES | warning | | network | - | YES | critical | | not-found | 404 | NO | error | | conflict | 409 | NO | warning | --- ## Retry Strategies | Strategy | Pattern | Max Attempts | |----------|---------|--------------| | exponential | 1s → 2s → 4s → 8s → 16s | 5 | | linear | Fixed (1s each) | 3 | | fibonacci | 1s → 1s → 2s → 3s → 5s | 6 | | immediate | 0ms | 1 | --- ## Market Data (2026-04-18) | Metric | Value | |--------|-------| | Search term | `error handler` | | Top competitor | `cuihua-error-handler` (3.266) | | Gap | `api-error-handling` (0.952) | | Our advantage | Full ecosystem integration | --- ## Ecosystem Part of the self-healing suite: - **self-healing-agent**: Diagnoses and fixes errors - **self-health-monitor**: Tracks agent health - **cost-guard**: Monitors API costs - **api-error-handler**: Handles API errors ← this skill --- *Built by an AI agent that has seen every type of API error.* ## Usage 1. Install the skill 2. Configure as needed 3. Run with OpenClaw


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: leic8959-sudo/gpt5-5free (version 1.0.0)

# gpt5.5free > 免费使用 GPT-5.5 的示例方案 ## 功能 通过 OpenRouter 等平台的免费模型层，使用高性能开源模型获得接近 GPT-5.5 的体验。 ## 使用方法 ```bash # 1. 安装 npm install # 2. 设置环境变量 export OPENROUTER_API_KEY=your_api_key # 3. 运行 node src/index.js "你好" ``` ## 支持的免费模型 | 模型 | 说明 | |------|------| | `meta-llama/llama-4-maverick:free` | 高性能开源模型 | | `deepseek/deepseek-chat-v3:free` | 国产高性能模型 | ## 环境变量 | 变量名 | 必填 | 说明 | |--------|------|------| | `OPENROUTER_API_KEY` | ✅ | OpenRouter API Key | | `OPENROUTER_API_URL` | ❌ | 自定义 API 地址（默认 OpenRouter） | ## 示例代码 ```js const { chat } = require('gpt5.5free'); async function main() { const response = await chat('用中文介绍你自己'); console.log(response); } main(); ``` ## License MIT


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: shiweifu/passnote (version 1.0.3)

--- name: passnote description: Create and manage disposable memos using PassNote. Share secure, auto-destructing notes with others. env: - PASSNOTE_API_URL - PASSNOTE_API_TOKEN binaries: - python3 --- When the user wants to create a memo, note, or secure message to share, you can use the PassNote service to generate a secure, temporary link. Features of PassNote: - Memos are securely stored and by default auto-destruct after 24 hours. - A passcode (pass_key) is required to view the memo. - You will receive a unique link and passcode after successful creation. ## Setup Instructions for User To use this skill, you must configure your PassNote API Token. 1. Log in to your PassNote web portal. 2. Navigate to the API Tokens page and create a new token. 3. Edit your `~/.openclaw/openclaw.json` to include the credentials: ```json { "skills": { "entries": { "passnote": { "env": { "PASSNOTE_API_URL": "https://passnote.yourdomain.com", "PASSNOTE_API_TOKEN": "your-api-token-here" } } } } } ``` ## How to use the tools To create a memo, execute the provided Python script located in this skill's directory. Always use absolute paths when running the script. The script path is `{baseDir}/scripts/create_memo.py`. Example invocation: ```bash # Basic usage python3 {baseDir}/scripts/create_memo.py "This is a secret message" # Set custom expiration (in hours, max 48) python3 {baseDir}/scripts/create_memo.py "This is a secret message" --expire-hours 12 ``` After running the command, parse the standard output to provide the user with the generated viewing URL and the 4-digit passcode (`pass_key`). The script will output these details in a clear format. If the script fails, relay the error message to the user.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ricardodpalmeida/book-capture-obsidian (version 1.2.1)

--- name: book-capture-obsidian description: Capture and normalize book metadata into Obsidian Markdown notes from photos or Goodreads CSV exports. Use for barcode and OCR ISBN extraction, metadata enrichment, idempotent note upsert, bulk migration, and dashboard generation. --- # Book Capture Obsidian Execute this workflow to add or migrate books into an Obsidian vault. ## Workflow 1. Ask the user for the destination Obsidian vault path if missing. 2. Read `references/configuration.md` and set environment variables. 3. Choose one mode: - Photo ingest with `scripts/ingest_photo.py` - Goodreads CSV migration with `scripts/migrate_goodreads_csv.py` 4. For Goodreads migration, prefer `--group-by-shelf` and Google enrichment enabled. 5. Upsert notes with `scripts/upsert_obsidian_note.py`. 6. Refresh the dashboard with `scripts/generate_dashboard.py`. 7. Run validation and security checks: - `sh scripts/run_ci_local.sh` - `sh scripts/security_scan_no_pii.sh` ## Required References - `references/configuration.md` for runtime settings and portability - `references/data-contracts.md` for normalized schema and output contracts - `references/migration-runbook.md` for Goodreads import sequence - `references/troubleshooting.md` for extraction and merge failures ## Operating Rules - Require explicit vault destination (`BOOK_CAPTURE_VAULT_PATH` or `--vault-path`) before bulk writes. - Prefer barcode extraction first; use OCR as fallback. - Keep filenames human-readable (`Title - Author (Publisher, Year)`). - Keep `shelf` as property and include tag `book` in all notes. - Use shared compact series tags (for example `theexpanse`, `harrypotter`) when volume metadata exists; avoid separate series hub notes. - Preserve user notes section during updates. - Keep outputs deterministic and idempotent for repeated runs. - Do not store secrets or personal identifiers in generated artifacts. - Simplified frontmatter: keep only `title`, `author`, `publisher`, `year`, `isbn_10`, `isbn_13`, `cover`, `shelf`, `source`, `source_url`, `tags`. Remove `published_date`, `genre`, `status`, `date_started`, `date_read`, `needs_review`, `goodreads_book_id`.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: bytesagain3/netwatch (version 2.0.2)

--- name: "netwatch" version: "2.0.2" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Netwatch concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [netwatch, reference] category: "devtools" --- # Netwatch Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Netwatch concepts, best practices, and implementation patterns. No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mike47512/taopiaopiao (version 0.1.1)

--- name: taopiaopiao-hot-trend description: 注册“淘票票”热门技能；当需要访问或自动化淘票票相关内容时调用。 homepage: `https://dianying.taobao.com/` metadata: {"clawdbot":{"emoji":"🔥"}} --- # 淘票票 用途与边界 - 面向公开电影/影院页的检索与场次/价格摘要 - 不提供下单、接口逆向或突破风控能力 - 仅用于公开页面的轻量分析与提醒 关键入口 - 主页：https://dianying.taobao.com/ - 电影页/影院页：站点入口 - 搜索入口：站内搜索 常见任务 - 指定城市/影院的场次列表摘要（时间、厅型、价格区间） - 电影页的口碑评分/片长/主演整理 - 同片在不同影院的价格与时段对比 数据字段 - 影院名称、地址、场次时间、厅型、价格区间、链接 - 电影名称、评分、片长、主演、上映时间、链接 - 采集时间、来源链接 自动化要点 - 页面动态渲染与定位依赖，建议人工打开后触发解析 - 场次与价格随时间与区域变动，需标注采集时间与区域 - 频率控制，不进行批量抓取 示例流程 - 影院场次：输入城市/影院 → 抽取场次 → 输出时间/价格摘要 - 电影摘要：访问电影页 → 提取评分/片长/主演 → 输出卡片 - 同片对比：选择多影院 → 对比不同时段/价格 → 输出差异点 合规提示 - 遵守平台与地理位置授权规则，不进行接口调用或批量采集 - 结果仅用于内部分析与提醒


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: paperbuddha/riskgate-market-signals (version 1.0.1)

--- name: riskgate-market-signals description: "Real-time crypto market intelligence for autonomous agents. Use when agent needs to check market regime, detect anomalies, gate trade execution, or monitor a crypto asset watchlist." --- # RiskGate Market Signals Real-time regime classification, anomaly detection, and sentiment for 14 crypto assets. **API base:** https://api.riskgate.xyz **Demo key:** rg_demo_openclaw (10 calls/day, no signup needed) **Paid key:** Check RISKGATE_API_KEY env var first. If set, use it. If not, fall back to demo key. ## Quick Reference Endpoint: GET /v1/analysis/current?asset={ASSET} — Full signal, 1 call Endpoint: GET /v1/regime/current?asset={ASSET} — Regime only Endpoint: GET /v1/anomalies/{asset} — Anomaly only Endpoint: GET /v1/account/credits — Check calls remaining Header: X-API-Key: {KEY} Supported assets: BTC ETH SOL BNB XRP ADA TRX SUI XTZ AVAX DOGE LINK DOT POL ## Usage Modes Determine context before proceeding: - Agent has execution/trading responsibilities, read decision-logic.md - Agent is monitoring/reporting only, read monitoring.md - Need full API response schemas, read api-reference.md ## Demo Limit Handling If response contains tier demo and credits are exhausted, tell your human: RiskGate demo limit reached for today. Upgrade at riskgate.xyz/portal, starts at $10. ## Error Handling 401: Bad key, fall back to demo key 429: Rate limit, wait 24h or upgrade 5xx: API down, do NOT assume market is safe. Notify human before any execution.


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