OpenClaw/clawhub-security-signals huggingface dataset:
Skill: jikeapi-cn/jike-zodiac-pairing-query (version 1.0.0)

--- name: jike-zodiac-pairing-query description: 星座配对。输入男生星座和女生星座，查询配对指数、比例、同情指数、天长地久指数、结果评述和恋爱建议。 适用场景：用户询问相关传统文化、生活常识或配对资料时使用。数据由即刻数据（jikeapi.cn）开放接口提供。 homepage: https://www.jikeapi.cn/ metadata: {"openclaw":{"emoji":"💫","requires":{"bins":["python3"],"env":["JIKE_ZODIAC_PAIRING_QUERY_KEY"]},"primaryEnv":"JIKE_ZODIAC_PAIRING_QUERY_KEY"}} --- # 星座配对 - 即刻数据 > 数据由 **[即刻数据](https://www.jikeapi.cn/)** 提供，帮助 AI 客户端快速接入可靠数据服务。 ## 前置配置 ```bash export JIKE_ZODIAC_PAIRING_QUERY_KEY=你的AppKey # 或使用通用 Key export JIKE_APPKEY=你的AppKey ``` ## 使用方法 ```bash python3 scripts/zodiac_pairing_query.py --boy-zodiac 双鱼座 --girl-zodiac 天蝎座 python3 scripts/zodiac_pairing_query.py --boy-zodiac 双鱼座 --girl-zodiac 天蝎座 --json ``` 直接调用 API： ```text GET https://api.jikeapi.cn/v1/zodiac_pairing?boy_zodiac=双鱼座&girl_zodiac=天蝎座&appkey=YOUR_APPKEY ``` ## AI 使用步骤 1. 从用户消息中提取查询参数。 2. 执行 `python3 scripts/zodiac_pairing_query.py` 并传入对应参数。 3. 默认返回中文文本；需要结构化处理时追加 `--json`。 4. 如果用户没有提供可选参数，可不传该参数，由接口按默认逻辑处理。 ## 脚本位置 `scripts/zodiac_pairing_query.py`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: xabo1986/auto-updater-gateway (version 1.0.1)

--- name: auto-updater-gateway description: Schedule and run safe daily/weekly updates for Clawdbot + installed skills using the Gateway cron scheduler. Use when setting up "run updates at 04:00" jobs, rotating update reports, running `clawdhub update --all`, and (optionally) applying Clawdbot updates + restart + doctor. metadata: {"version":"1.0.1","clawdbot":{"emoji":"🔄","category":"automation","os":["windows","darwin","linux"],"requires":{"anyBins":["clawdbot","clawdhub"]}}} --- # Auto-Updater (Gateway) Create a reliable daily auto-update routine using **Clawdbot Cron** (Gateway scheduler). This is a "skill" in the sense of a repeatable workflow + correct config shapes (not a plugin). ## Quick setup checklist 1) Ensure ClawHub CLI is logged in (for skill updates): ```bash /home/xabo/.nvm/versions/node/v22.22.0/bin/clawdhub login --workdir /home/xabo/clawd --dir skills /home/xabo/.nvm/versions/node/v22.22.0/bin/clawdhub whoami --workdir /home/xabo/clawd --dir skills ``` 2) Decide: - When to run (cron + timezone) - Whether the job should only **report**, or **update + restart** ## Recommended cron job (isolated, deliver output) Use an **isolated** cron job so it doesn’t spam the main session context. Example CLI (04:00 Europe/Stockholm): ```bash /home/xabo/.nvm/versions/node/v22.22.0/bin/clawdbot cron add \ --name "Daily auto-update (Clawdbot + skills)" \ --cron "0 4 * * *" \ --tz "Europe/Stockholm" \ --session isolated \ --wake now \ --deliver \ --channel telegram \ --to "2095290688" \ --message "Run daily auto-update: update skills via clawdhub update --all; if Clawdbot has an update available, apply it and restart; then run clawdbot doctor --non-interactive; report what changed." ``` ## What the job should do (workflow) Within the cron run: 1) Capture “before” state - `clawdbot --version` - `clawdhub list` (skills + versions) 2) Update skills - `clawdhub update --all` 3) (Optional) Update Clawdbot - Only if the owner explicitly wants self-updates. - After updating, run `clawdbot doctor --non-interactive`. - Restart gateway if required. 4) Send a concise summary - Clawdbot version before/after - Skills updated (old → new) - Any errors ## Notes / gotchas - **Timezone field:** in Gateway job objects this is `schedule.tz` (IANA tz like `Europe/Stockholm`). - **Delivery:** Prefer explicit `channel` + `to` so the job always reaches you. - **Clawdbot self-update:** can be disruptive (restarts). Run at a quiet time. ## Troubleshooting - `clawdhub update` says “Not logged in” → run `clawdhub login` again. - Job doesn’t run → confirm Gateway is always-on and cron is enabled. - Nothing updates → that can be normal; still send a “no changes” report.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dnaxxx-hub/docx-editor (version 1.0.0)

--- name: DOCX description: Read and generate Word documents with correct structure, styles, and cross-platform compatibility. metadata: {"clawdbot":{"emoji":"📘","os":["linux","darwin","win32"]}} --- ## Structure - DOCX is a ZIP containing XML files—`word/document.xml` has main content, `word/styles.xml` has styles - Text splits into runs (`<w:r>`)—each run has uniform formatting; one word may span multiple runs - Paragraphs (`<w:p>`) contain runs—never assume one paragraph = one text block - Sections control page layout—headers/footers, margins, orientation are per-section ## Styles vs Direct Formatting - Styles (Heading 1, Normal) are named and reusable—direct formatting is inline and overrides style - Removing direct formatting reveals underlying style—useful for cleanup - Character styles apply to runs, paragraph styles to paragraphs—they layer together - Linked styles can be both—applying to paragraph or selected text behaves differently ## Lists & Numbering - Numbering is complex: `abstractNum` defines pattern, `num` references it, paragraphs reference `numId` - Restart numbering not automatic—need explicit `<w:numPr>` with restart flag - Bullets and numbers share the numbering system—both use `numId` - Indentation controlled separately from numbering—list can exist without visual indent ## Headers, Footers, Sections - Each section can have different headers/footers—first page, odd, even pages - Section breaks: next page, continuous, even/odd page—affects pagination - Headers/footers stored in separate XML files—referenced by section properties - Page numbers are fields, not static text—update on open or print ## Track Changes & Comments - Track changes stores original and revised in same document—accept/reject to finalize - Deleted text still present with `<w:del>` wrapper—don't assume visible = all content - Comments reference ranges via bookmark IDs—`<w:commentRangeStart>` to `<w:commentRangeEnd>` - Revision IDs track who changed what—metadata persists even after accepting ## Fields & Dynamic Content - Fields have code and cached result—`{ DATE \@ "yyyy-MM-dd" }` vs displayed date - TOC, page numbers, cross-references are fields—update fields to refresh - Hyperlinks can be fields or direct `<w:hyperlink>`—both valid - MERGEFIELD for mail merge—placeholder until merge executes ## Compatibility - Compatibility mode limits features to earlier Word version—check `w:compat` settings - LibreOffice/Google Docs: complex formatting may shift—test roundtrip - Embedded fonts may not transfer—fallback fonts substitute - DOCM contains macros (security risk); DOC is legacy binary format ## Common Pitfalls - Empty paragraphs for spacing—prefer space before/after in paragraph style - Manual page breaks inside paragraphs—use section breaks for layout control - Images in headers: relationship IDs are per-part—same image needs separate relationship in header - Copy-paste brings source styles—can pollute style gallery with duplicates


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: brucegutman/pipeworx-census (version 1.0.0)

# Census Census MCP — U.S. Census Bureau housing-relevant APIs. ## census_acs Search American Community Survey data by geography and variable code (e.g., B25077_001E for median h ## census_building_permits Check monthly building permits for new residential construction by geography. Returns count of autho ## census_housing_starts Get residential construction pipeline by geography: new starts, units under construction, and comple ## census_homeownership Check quarterly homeownership rates by geography. Returns percentage of owner-occupied housing units ## census_available_datasets Discover Census datasets and their variables. Returns dataset names, descriptions, and variable code ```json { "mcpServers": { "census": { "url": "https://gateway.pipeworx.io/census/mcp" } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wangxx07/comfyui-mcp-skill (version 1.0.0)

# ComfyUI MCP Skill AI 视频生成技能，基于 ComfyUI 的 MCP 服务 ## 功能 - 🎬 **视频生成** - 根据提示词生成 AI 视频 - 📖 **分镜生成** - 创建视频分镜脚本 - 🎞️ **视频合成** - 合并多个视频片段 - 📥 **视频下载** - 下载生成的视频 - 📊 **进度查询** - 查看生成任务进度 ## 技术栈 - Python 3.10+ - FastMCP - ComfyUI API - Docker (可选) ## 安装 ### 方式 1：从 GitHub 安装 ```bash cd /root/.nanobot/workspace/skills git clone https://github.com/lemnt-ai/comfyui-mcp-server.git comfyui-mcp-skill cd comfyui-mcp-skill pip install -r requirements.txt ``` ### 方式 2：Docker 部署 ```bash docker-compose up -d ``` ## 配置 ### 环境变量 | 变量 | 说明 | 默认值 | |------|------|--------| | COMFYUI_HOST | ComfyUI 服务器地址 | localhost | | COMFYUI_PORT | ComfyUI 端口 | 8188 | | LOG_LEVEL | 日志级别 | INFO | ### 配置文件 编辑 `config/config.yaml`： ```yaml comfyui: host: localhost port: 8188 timeout: 300 server: transport: http host: 0.0.0.0 port: 18060 path: /mcp ``` ## 使用说明 ### 启动服务 ```bash # HTTP 模式 python server.py --transport http --host 0.0.0.0 --port 18060 # stdio 模式 python server.py --transport stdio ``` ### 工具调用示例 #### 生成视频 ```python generate_video( prompt="一个机器人在跳舞", duration=5, width=512, height=512, fps=12 ) ``` #### 查询进度 ```python check_progress(prompt_id="xxx-xxx-xxx") ``` #### 下载视频 ```python download_video(prompt_id="xxx-xxx-xxx") ``` ## 集成到 nanobot 在 nanobot 配置中添加： ```json { "mcpServers": { "comfyui": { "command": "python", "args": ["/root/.nanobot/workspace/skills/comfyui-mcp-skill/server.py"], "cwd": "/root/.nanobot/workspace/skills/comfyui-mcp-skill", "env": { "COMFYUI_HOST": "localhost", "COMFYUI_PORT": "8188" } } } } ``` ## 依赖服务 ### ComfyUI 安装 ```bash # 克隆 ComfyUI git clone https://github.com/comfyanonymous/ComfyUI.git cd ComfyUI # 安装依赖 pip install -r requirements.txt # 启动 python main.py --listen 0.0.0.0 --port 8188 ``` ### 必要节点 - AnimateDiff - ControlNet - IPAdapter ## 工作流文件 工作流配置文件位于 `workflows/` 目录： | 文件 | 说明 | |------|------| | video_workflow.json | 视频生成工作流 | | storyboard_workflow.json | 分镜生成工作流 | | compose_workflow.json | 视频合成工作流 | ## 常见问题 ### Q: 视频生成失败？ A: 检查 ComfyUI 服务是否正常运行，端口是否正确。 ### Q: 生成速度慢？ A: 视频生成依赖 GPU，确保有可用的 GPU 资源。 ### Q: 如何自定义工作流？ A: 编辑 `workflows/` 目录下的 JSON 文件。 ## 相关链接 - 原项目：https://github.com/lemnt-ai/comfyui-mcp-server - ComfyUI: https://github.com/comfyanonymous/ComfyUI - FastMCP: https://github.com/jlowin/fastmcp ## 版本历史 - v1.0.0 - 初始版本 - 视频生成 - 分镜生成 - 视频合成 - 进度查询 --- *创建时间：2026-03-17* *版本：v1.0.0*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: veeicwgy/word-converter (version 0.2.0)

--- name: word-converter description: > Universal Word document converter powered by MinerU API. Convert .docx and .doc files to Markdown, HTML, LaTeX, DOCX, or JSON using mineru-open-api CLI. Supports quick flash-extract (no token, Markdown output) and precision extract with multi-format output, table recognition, formula detection, and batch processing. Use when asked to 'convert Word document', 'transform docx to markdown', 'Word to PDF', 'Word to LaTeX', 'change Word format', 'batch convert Word files', 'Word文档格式转换', '把Word转成其他格式', 'docx转markdown', 'Word批量转换', 'how do I convert my Word file to another format', 'is there a tool to convert docx'. Handles complex formatting, embedded objects, tables, formulas, and images. Ideal for academic writing, technical documentation, content migration, and automated document pipelines. tags: - word - converter - docx - markdown - html - latex - mineru - batch-processing - format-conversion - document-pipeline tools: - Bash(mineru-open-api:*) model: claude-3-5-haiku-20241022 --- # Word Document Converter with mineru-open-api You are a Word document conversion specialist. Convert Word files to any supported format using mineru-open-api. ## Installation ```bash npm install -g mineru-open-api ``` ## Conversion Workflow 1. Quick Markdown conversion (no token): ```bash mineru-open-api flash-extract document.docx -o ./output/ ``` 2. Multi-format conversion (token required): ```bash mineru-open-api extract document.docx -f md,html,latex -o ./output/ ``` 3. Batch conversion: ```bash mineru-open-api extract *.docx -f html -o ./results/ ``` ## Supported Conversions | Input | flash-extract | extract | |-------|:---:|:---:| | .docx | Markdown | md, html, latex, docx, json | | .doc | Not supported | md, html, latex, docx, json | ## Key Rules - Default to `flash-extract` for simple .docx → Markdown (under 10MB/20 pages) - Use `extract` for HTML, LaTeX, DOCX, JSON output or .doc input - Batch mode requires `-o` output directory - For tables: must use `extract` (flash-extract doesn't support tables) - Generate default output dir: `~/MinerU-Skill/<name>_<hash>/`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: xcchenx345/mimo-asr (version 1.0.0)

--- name: mimo-asr description: 小米 MiMo-V2.5-ASR 语音识别技能。通过 Gradio API 调用，将音频转录为文字，支持中英文自动检测和显式指定。无需本地模型，无需 API Key。当用户请求语音转文字、音频转录、提取对话内容时使用。 --- # MiMo-V2.5-ASR 语音识别 / Speech Recognition (中文/English) > 通过小米官方 Gradio 免费 API 调用语音识别模型，**无需本地模型、无需 API Key**。 > Transcribe audio via Xiaomi's free public Gradio API — **no local model, no API key needed**. ## 为什么选这个？/ Why This One? 比起安装 4GB 本地模型 + `mimo-audio` 包，这个版本： - ✅ 零依赖（只需要 `requests`） - ✅ 零配置（不需要下载任何东西） - ✅ 免费无限调用 - ✅ 支持中文/英文/自动检测 - ❌ 需要网络（音频传云端处理） ## 快速开始 / Quick Start ```bash # 安装唯一依赖 / Install the only dependency pip install requests # 自动语言检测 / Auto language detection python scripts/mimo_asr_api.py recording.wav # 指定中文 / Specify Chinese python scripts/mimo_asr_api.py speech.mp3 --language zh # 指定英文 / Specify English python scripts/mimo_asr_api.py lecture.wav --language en # 输出到文件 / Output to file python scripts/mimo_asr_api.py meeting.wav --output transcript.txt ``` ## 参数 / Parameters | 参数 | 说明 | 默认值 | |---|---|---| | `audio` | 音频文件路径（wav/mp3/m4a 等） | 必填 | | `--language` | 语言：`auto`（自动）、`zh`（中文）、`en`（英文） | `auto` | | `--output` | 输出文本文件路径（可选） | 打印到终端 | ## 示例 / Examples ```bash # 🪟 Windows python scripts/mimo_asr_api.py C:\Users\abc15\Desktop\录音.wav --language zh --output output.txt # 🍎 macOS / 🐧 Linux python scripts/mimo_asr_api.py ~/Desktop/meeting.mp3 --language auto ``` ## API 内部原理 / How It Works 该脚本通过 Gradio API 调用 HuggingFace 上的小米官方空间： The script calls Xiaomi's official HuggingFace space via the Gradio API: ``` https://xiaomimimo-mimo-v2-5-asr.hf.space ``` 步骤 / Steps: 1. **上传音频** → `/gradio_api/upload` 2. **启动推理** → `/gradio_api/call/infer`（传音频 URL + 语言标签） 3. **轮询结果** → 获取转录文本 ## 支持的文件格式 / Supported Formats WAV、MP3、M4A、FLAC、OGG 等主流音频格式。 WAV, MP3, M4A, FLAC, OGG and common audio formats. ## 注意事项 / Notes - 大文件处理时间较长 / Large files take longer - 需网络连接 / Internet connection required - 不需要 MIMO API Key / No MIMO_API_KEY needed - 免费使用 / Free to use


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal:  | SkillSpector: suspicious (MEDIUM)
Skill: anyunzhong/clawec-amazon-asin-comment-query (version 1.0.1)

--- name: amazon-asin-comment-query description: 通过 Clawec API 按 ASIN 或商品链接查询亚马逊商品评论。在用户需要亚马逊评论分析、ASIN 评论抓取、差评调研、Review 分析、买家反馈洞察时使用。 --- # 亚马逊 ASIN 评论查询 ## 关于 ClawEC ClawEC 是一款面向跨境电商场景的 AI 智能体协同平台，以「你的跨境电商 AI 团队」为品牌主张，将选品、调研、运营、上架、营销、客服、采购、合规等环节沉淀为可执行的 SOP（标准作业程序），通过多智能体（「虾员工」）分工协作与 7×24 小时自动化任务，帮助卖家在单人或少人条件下完成跨境业务闭环，降低对专业运营团队与复杂本地部署的依赖。 本技能调用 ClawEC 开放 API，用于按 ASIN 或商品 URL 获取亚马逊商品评论数据。 ## 认证与基址 - **Base URL**: `https://www.clawec.com/api` - **API Key**: 在 https://www.clawec.com/?source=q-clawhub 注册帐号 然后去https://www.clawec.com/api-key?source=q-clawhub 获取key - **请求头**: - `Content-Type: application/json` - `Authorization: Bearer <API_KEY>` 优先从环境变量 `CLAWEC_API_KEY` 读取密钥；未设置时向用户索取，勿硬编码。 ## 接口 `POST /aigc/tool/amazon_asin_comment_query` | 参数 | 位置 | 必填 | 说明 | |------|------|------|------| | url | body | 否* | 商品链接；有 `url` 时可不传 `asin`、`region` | | asin | body | 否* | 商品 ASIN；无 `url` 时与 `region` 一起使用（由 API 拼接链接） | | region | body | 否* | 站点区域代码（2 位大写字母）；有 `url` 时可不传 | \* 必须提供 `url`，或同时提供 `asin` + `region`。 ### region 取值 | 代码 | 站点 | |------|------| | NA | 北美（美国） | | CA | 加拿大 | | BR | 巴西 | | UK | 英国 | | DE | 德国 | | FR | 法国 | | JP | 日本 | | SG | 新加坡 | | AU | 澳大利亚 | 未指定 `region` 且使用 ASIN 查询时默认 `NA`。 ## 调用 **按商品链接：** ```bash curl -s -X POST "https://www.clawec.com/api/aigc/tool/amazon_asin_comment_query" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $CLAWEC_API_KEY" \ -d '{"url":"https://www.amazon.com/dp/B0XXXXXXXX"}' ``` **按 ASIN + 站点：** ```bash curl -s -X POST "https://www.clawec.com/api/aigc/tool/amazon_asin_comment_query" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $CLAWEC_API_KEY" \ -d '{"asin":"B0XXXXXXXX","region":"NA"}' ``` 或使用脚本： ```bash # 商品链接 bash scripts/query.sh "https://www.amazon.com/dp/B0XXXXXXXX" # ASIN + 区域（默认 NA） bash scripts/query.sh B0XXXXXXXX NA ``` ## 响应结构 ```json { "status": 1, "data": { ... } } ``` - `status`: `1` = 成功，`0` = 失败 - `data`: 评论数据对象或列表，字段以实际返回为准 完整说明见 [references/response-schema.md](references/response-schema.md)。 ## 工作流程 1. 确认查询方式：商品 `url`，或 `asin` + `region` 2. 检查 `CLAWEC_API_KEY` 是否可用 3. 执行 API 请求 4. `status !== 1` 或请求失败时，说明错误并提示检查密钥、ASIN/链接与站点 5. 解析 `data`（若为嵌套结构，展开评论列表），按用户需求整理输出 ## 输出建议 默认中文摘要，包含： - 查询目标（ASIN / 链接）与站点 - 评论总量与星级分布（若 `data` 含统计字段） - 评论列表表：星级、标题、正文摘要、日期、是否 Verified Purchase、有帮助数等（以实际字段为准） - 分析场景：归纳高频好评/差评主题各 2–3 条，并标注可改进点 ## 示例 **输入**：北美站 ASIN `B0D1XD1ZV3` 的评论 **输出摘要**： | 星级 | 标题 | 摘要 | 日期 | |------|------|------|------| | 5 | ... | ... | ... | | 1 | ... | ... | ... | （其余字段与统计按 API 返回的 `data` 动态展示。）


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: aricgamma/ancient-term-normalization (version 1.0.4)

--- name: ancient-term-normalization description: | Normalises recognised characters or words from ancient manuscripts and excavated texts by mapping them to standardised forms. Use this skill after OCR or character recognition to expand ambiguous characters into sets of aliases (e.g. 荆 → 楚, 荆楚). The skill reads a JSON file of recognised characters and produces a JSON file of normalised terms. license: MIT compatibility: | Requires Python 3.10+ and PyYAML. Scripts run in a sandbox environment with read/write access to the workspace. No network access is required. allowed-tools: Bash(python:*), Bash(jq:*) metadata: author: ai4s version: "1.0.0" domain: humanities --- # Ancient Term Normalisation Skill ## When to use Use this skill when you have a list of characters or terms recognised from manuscripts and want to generate possible aliases, alternative spellings or historical equivalents. This is useful for search and retrieval because many names and states have variant forms in historical sources. ## How it works 1. **Input** – Provide a path to a JSON file containing recognised characters and their confidence scores. The input must follow the schema defined in `assets/schemas/recognized_chars.schema.json`. 2. **Lookup** – The script loads a YAML file of alias rules (`assets/data/historical_aliases.yaml`) where each key maps to a list of standardised forms. If a character is absent from the mapping, it is preserved as its own normalised form. 3. **Output** – A JSON file is written to the `term_normalisation/` folder in the workspace. Each entry contains the original character, the list of normalised forms, the entity type, notes and the original confidence score. The output conforms to `assets/schemas/normalized_terms.schema.json`. ## Files produced Outputs reside under `term_normalisation/` in the workspace: - `normalized_terms.json` – list of normalised term objects. ## References - See `references/REFERENCE.md` for input and output schema details. - See `assets/data/historical_aliases.yaml` for the mapping of historical names to standardised forms.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ghwyever/01-tomato-ip-parse (version 1.0.5)

# 番茄IP解析技能 ## 功能介绍 专业解析番茄小说IP内容，自动提取核心世界观、人物设定、核心冲突、高光爽点，并完成内容合规初检，为AI短剧改编提供标准化素材。 ## 输入参数 - novel_title：小说标题（必填） - novel_text：小说正文内容（必填） - style_tag：风格标签，可选：爽文/甜宠/逆袭/玄幻/现言，默认：爽文 ## 输出结果 - ip_info：结构化IP信息 - compliance_check：内容合规检查结果 ## 使用场景 - 番茄小说IP改编短剧第一步 - 批量提取小说核心信息 - 内容合规风险初筛 ## 技术说明 支持通用大模型接入，需配置 API_KEY、API_BASE、MODEL_NAME。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: brs999/kalshi-api (version 1.0.0)

--- name: kalshi-api description: Read-only Kalshi API skill for market discovery, liquidity checks, and market validation. Use for scanning and ranking Kalshi opportunities. Pair with a separate paper-trading skill if you want open/close execution. homepage: https://docs.kalshi.com user-invocable: true disable-model-invocation: true metadata: { "openclaw": { "emoji": "📊", "requires": { "bins": ["node"] } } } --- # Kalshi API (Read-Only) Use this skill for Kalshi scouting and market validation only. ## Rules - Use OpenAPI read endpoints for market discovery and validation. - This skill does not place, amend, or cancel orders. - This skill does not include paper ledger execution scripts. ## Primary Commands Exchange status: ```bash node {baseDir}/scripts/kalshi-api.mjs status --pretty ``` Broad market scan: ```bash node {baseDir}/scripts/kalshi-api.mjs markets --status open --limit 1000 --pretty node {baseDir}/scripts/kalshi-api.mjs events --limit 100 --pretty node {baseDir}/scripts/kalshi-api.mjs series --limit 400 --pretty ``` Focused validation: ```bash node {baseDir}/scripts/kalshi-api.mjs market --ticker <TICKER> --pretty node {baseDir}/scripts/kalshi-api.mjs trades --ticker <TICKER> --limit 200 --pretty node {baseDir}/scripts/kalshi-api.mjs orderbook --ticker <TICKER> --pretty ``` ## Optional Integration: Paper Ledger Skill Required If you want paper open/close workflows, install and use a separate paper-trading skill that provides the execution script. This Kalshi skill can supply candidate/market data to that separate skill. ```bash node --experimental-strip-types skills/paper-trading/scripts/paper_trading.ts status --account kalshi --format json --pretty ``` ## Environment Optional override (defaults to Kalshi production API): ```bash export KALSHI_BASE_URL="https://api.elections.kalshi.com/trade-api/v2" ``` ## Tests Run the Kalshi reader smoke tests: ```bash node --test {baseDir}/tests/kalshi-api.test.mjs ```


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