OpenClaw/clawhub-security-signals huggingface dataset:
Skill: jzakirov/baserow-cli (version 0.1.0)

--- name: baserow version: 0.1.0 description: "Baserow: Create, read, update, and delete rows, list tables and fields. Use when the user wants to interact with Baserow — querying data, creating or updating rows, inspecting table structure, or any database workflow." metadata: openclaw: emoji: "🗄️" category: "productivity" requires: bins: ["baserow"] env: ["BASEROW_TOKEN", "BASEROW_URL"] cliHelp: "baserow --help" --- # Baserow CLI CLI for [Baserow](https://baserow.io) — the open-source Airtable alternative. Output is JSON by default — pipe to `jq` or consume directly. ## Setup ```bash uv tool install baserow-cli # or: pip install baserow-cli baserow config init # interactive setup wizard ``` Config: `~/.config/baserow-cli/config.toml`. All values can be set via env vars (`BASEROW_TOKEN`, `BASEROW_URL`, `BASEROW_TABLE`) or CLI flags. ## Command Discovery Do not guess command names or options. Use `--help`: ```bash baserow --help baserow rows --help baserow rows list --help ``` ## Quick Reference | Resource | Key commands | |----------|-------------| | `baserow tables` | `list`, `get <table-id>` | | `baserow fields` | `list --table <id>` | | `baserow rows` | `list`, `get`, `create`, `update`, `delete` | | `baserow rows batch-*` | `batch-create`, `batch-update`, `batch-delete` | | `baserow config` | `init`, `show`, `set` | ## Common Workflows **Discover table structure:** ```bash baserow tables list baserow tables get 42 baserow fields list --table 42 ``` **List and search rows:** ```bash baserow rows list --table 42 baserow rows list --table 42 --search "quarterly" --size 50 baserow rows list --table 42 --filter "Status__equal=Done" --order-by "-Created" ``` **Create a row:** ```bash baserow rows create --table 42 --json '{"Name": "New item", "Status": "Todo"}' ``` **Update a row:** ```bash baserow rows update 1 --table 42 --json '{"Status": "Done"}' ``` **Batch operations:** ```bash baserow rows batch-create --table 42 --json '[{"Name": "A"}, {"Name": "B"}]' baserow rows batch-update --table 42 --json '[{"id": 1, "Status": "Done"}, {"id": 2, "Status": "Done"}]' baserow rows batch-delete --table 42 --json '[3, 4, 5]' --yes ``` **Pipe JSON from stdin:** ```bash cat data.json | baserow rows batch-create --table 42 --json - ``` ## Important Conventions - `--table` is optional when `defaults.table` is configured - `--database` is optional when `defaults.database` is configured - Destructive commands (`delete`, `batch-delete`) require `--yes` in non-TTY (agent) contexts - `--json` accepts `-` to read from stdin - Pagination via `--page` and `--size` (max 200 per page); default is page 1 - Errors go to stderr as JSON: `{"error": {"type": "...", "message": "...", "status_code": N}}` - Exit codes: `0` success, `1` error, `2` rate-limited (retry safe) - Field values in `--json` use field names (not IDs) > [!CAUTION] > `delete` and `batch-delete` commands are destructive — always confirm with the user before executing.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: evan-ch/ipaas-vitepress-content-automation (version 1.0.9)

--- name: ipaas-vitepress-automation title: iPaaS 内容生成与自动化发布工具 description: 自动生成 B 端集成解决方案文档并安全部署至 VitePress 站点。 version: 1.2.0 author: CJCloud license: MIT homepage: https://your-company.com user-invocable: true metadata: runtime: bash categories: [productivity, developer-tools] openclaw: requires: tools: [file_read, file_write, shell] binaries: [pnpm, rsync, ssh] env_vars: - SERVER_IP: "目标服务器 IP" - REMOTE_DIR: "目标服务器部署绝对路径" - REMOTE_USER: "部署用户 (建议非 root)" - SERVER_PORT: "SSH 端口 (默认 22)" --- # 触发场景 - 当用户要求撰写或发布系统集成解决方案（如：金蝶集成天猫）时。 # 自动化流程 (Workflow) 1. **数据提取**：识别两个对接系统名称、接口范围及业务场景。 2. **读取模板**：`file_read: ./template/solution-template.md` 3. **内容创作**：基于模板逻辑生成 Markdown 文本。 4. **本地写入**：`file_write: ./docs/cases/{filename}.md` 5. **配置更新**：`file_write: ./docs/.vitepress/config.mts` (更新导航栏) 6. **安全部署**：调用项目内的 shell 脚本执行发布： `shell: bash ./scripts/deploy.sh` # 安全约束 - **认证**：本工具仅支持 SSH Key 密钥认证，不接受密码输入。 - **环境**：运行环境需预装 pnpm 和 rsync。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: 1yihui/context-mode (version 1.0.0)

# Skill: Context Mode — 高效研究与内容分析 ## 定位 你是小美，资讯+创意管家。context-mode 是你的**研究加速器**——让它帮你抓网页、分析内容、追踪趋势，而不把大块原始数据塞进上下文。 ## 核心工具 | 工具 | 用途 | 省 token 效果 | |------|------|---------------| | `ctx_batch_execute` | 批量执行 + 搜索，一次搞定 | 节省 90%+ | | `ctx_execute` | 单次脚本执行 | 节省 90%+ | | `ctx_fetch_and_index` | 抓取网页并索引，供后续搜索 | 节省 90%+ | | `ctx_search` | 在已索引内容中 BM25 搜索 | 快速召回 | | `ctx_index` | 把文档/知识索引进可搜索库 | 永久积累 | ## 使用时机（判断规则） ### ✅ 用 ctx_fetch_and_index 而不是 web_fetch when: - 研究一个话题，需要抓多个网页 - 用户说"帮我研究一下..." - 需要搜索行业动态、新闻、竞品分析 - 抓取后的内容要反复查阅 ### ✅ 用 ctx_batch_execute 而不是多次 exec when: - 需要同时查多个 RSS 源 / 文件 - 需要对内容做批量分析（计数、分类、提取） - 例子：分析 10 篇新闻的标题关键词 ### ✅ 用 ctx_execute when: - 统计数量（"有多少篇..."） - 从结构化数据中计算（JSON 解析、CSV 统计） - 生成内容摘要脚本 ## 小美专属场景 ### 新闻/资讯研究 ``` ctx_fetch_and_index({ url: "https://news site", source: "行业新闻" }) // 后续用 ctx_search 反复查询 ctx_search({ queries: ["AI agent 动态", "竞品发布"] }) ``` ### 内容创意分析 ``` ctx_batch_execute({ commands: [ { label: "trend_data", command: "cat trend_analysis.json | jq '.keywords | length'" }, { label: "top_tags", command: "cat trend_analysis.json | jq '.keywords |.[:5]'" } ], queries: ["关键词数量", "最热的5个话题"] }) ``` ### 多源对比研究 ``` // 一次抓多个源，ctx_batch_execute 自动去重 + 搜索 ctx_batch_execute({ commands: [ { command: "curl -s source1 RSS" }, { command: "curl -s source2 RSS" } ], queries: ["两个源都报道了什么？", "差异点在哪？"] }) ``` ## 输出格式 保持简洁，不说废话： ``` ❌ "好的，我来帮你研究一下这个话题，我先抓几个网页看看..." ✅ "正在抓取3个来源..." ❌ "根据我的分析，这个问题大概有以下几个方面..." ✅ "3个关键点：①... ②... ③..." ``` ## 禁止场景 - 用户要看原文（直接 web_fetch，不用 index） - 单次简单查询（普通 exec 就够用） - 内容敏感不适合索引（直接读，不用 ctx） ## 触发词 听到以下就说 context-mode： - "研究一下"、"帮我查一下"、"行业动态" - "分析一下这些内容"、"有多少..." - "追踪一下"、"监测"、"最新消息" - "对比一下"、"竞品"、"趋势"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ahsbnb/robust-text-auditor (version 1.0.1)

--- name: robust-text-auditor description: (已验证) 高可靠性文本审核器，结合本地规则、百度 API 和 AI 深度分析，提供三层合规性审查。 metadata: version: 1.0.0 source: https://github.com/your-repo/robust-text-auditor author: an tags: [text-auditing, compliance, baidu-api, nlp, security] license: MIT requirements: - python - "pip:requests" --- # SKILL.md for robust-text-auditor ## Description 这是一个高可靠性的组合技能，它通过一个统一的 `run.ps1` 脚本，将**本地规则扫描**、**百度 API 机审**和**AI 模型深度分析**三层审核步骤，封装成一个单一、可靠的原子操作。 AI 助手只需调用此脚本并提供待审核的文本，即可获得一份包含三层审核结果的、完整的、最终的合规性报告提示词，再应用提示词实现报告输出。这个设计确保了审核流程的完整性、专业性和结果的绝对可靠性。 ## ⚠️ Prerequisites (前置条件) **在使用本技能前，您必须在主配置文件 `~/.openclaw/config.json` 中添加您的百度云 API 凭证：** ```json { "baidu_api_key": "YOUR_BAIDU_API_KEY", "baidu_secret_key": "YOUR_BAIDU_SECRET_KEY" } ``` ## How to Use 该技能的核心是 `run.ps1` 脚本，它负责调度所有内部子脚本。 ### Parameters * **`-InputFile <path>`**: 待审核的文本文件的路径。 * **`-TextToAudit <string>`**: 待审核的短文本字符串。 *注意：`-InputFile` 和 `-TextToAudit` 两个参数必须提供一个。* ### Example Invocation **通过文件进行审核:** ```powershell # AI 应动态查找此技能的路径 path/to/run.ps1 -InputFile "C:\path\to\your\file.txt" ``` **通过字符串进行审核:** ```powershell # AI 应动态查找此技能的路径 path/to/run.ps1 -TextToAudit "这是一段需要进行合规性审核的文本内容。" ``` ## Output 该技能执行成功后，会在技能目录的根路径下生成一个 `final_prompt.txt` 文件。这个文件是一个包含了所有审核层级结果的、可以直接使用的大语言模型提示词。 AI 代理应读取该文件的内容，并将其作为输入提交给大语言模型，即可生成最终的、详尽的审核报告。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: bitsanity/carp (version 1.0.1)

--- name: carp description: Manage a local CARP interface and perform secure, verified agent-to-agent commerce workflows over CARP endpoints. Use when configuring IF_URL, registering an agent DID, polling CARP queues, sending outbound requests, posting results, fetching answers, or retrieving another agent menu through a local/LAN CARP interface. metadata: { "openclaw": { "requires": { "bins": ["curl"], "env": ["IF_URL"], }, }, } --- # CARP CARP is Crustacean Agent Rendezvous Protocol (CARP). Reference implementation and source code: - https://github.com/bitsanity/agent-crvp Use CARP through one config value: - `IF_URL`: Base URL for the local CARP interface (`http://host:port`). Prefer LAN host, allow localhost for testing. ## Workflow 1. Set `IF_URL`. 2. Confirm interface reachability. 3. Use CARP endpoints to register, poll, request, and respond. ## Usage Set once per shell: ```bash export IF_URL="http://localhost:8888" ``` Check agent interface doc: ```bash curl -sS "$IF_URL/agent.json" ``` Register this agent DID: ```bash curl -sS -X POST "$IF_URL/cgi-bin/register" \ -H "Content-Type: application/json" \ --data '<jsonobj>' ``` Get next hello/contact event: ```bash curl -sS "$IF_URL/cgi-bin/nexthello" ``` Get next inbound service request: ```bash curl -sS "$IF_URL/cgi-bin/nextrequest" ``` Send result for an inbound request: ```bash curl -sS -X POST "$IF_URL/cgi-bin/result" \ -H "Content-Type: application/json" \ -H "Cookie: agent=<pubkeyhex>&cookie=<requestcookie>" \ --data '<resultobj>' ``` Get next answer for one of our outbound requests: ```bash curl -sS "$IF_URL/cgi-bin/nextanswer" ``` Send outbound encrypted request to another agent: ```bash curl -sS -X POST "$IF_URL/cgi-bin/obrequest" \ -H "Content-Type: application/json" \ -H "Cookie: to=<pubkeyhex>" \ --data '<red-json-rpc-request>' ``` Fetch another agent menu: ```bash curl -sS "$IF_URL/cgi-bin/getmenu?agent=<agent>" ``` ## Notes - Keep `IF_URL` private to your trusted network whenever possible. - Treat all cookies, keys, and request bodies as sensitive. - Prefer idempotent polling loops with backoff when automating queue reads.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/openclaw-setup-service (version 1.0.0)

# OpenClaw Setup Assistant Skill 帮助用户快速安装配置 OpenClaw，提供远程服务支持。 ## 功能 - 一键安装 OpenClaw CLI - 配置常用 Channel（微信/飞书/钉钉） - 设置心跳和自动化任务 - 提供 Skills 推荐 - 故障排查指南 ## 使用方式 ``` 帮我安装 OpenClaw ``` ``` 配置微信 Channel ``` ``` 设置每日简报心跳 ``` ## 安装流程 ### 步骤 1：检查环境 ```bash # 检查 Node.js 版本 node -v # 需要 >= 18 # 检查 npm npm -v ``` ### 步骤 2：安装 OpenClaw ```bash npm install -g openclaw ``` ### 步骤 3：初始化工作区 ```bash openclaw init ``` ### 步骤 4：配置 Channel #### 微信 ```bash openclaw channel add wechat # 扫码登录 ``` #### 飞书 ```bash openclaw channel add feishu # 配置 App ID 和 Secret ``` #### 钉钉 ```bash openclaw channel add dingtalk # 配置 Webhook ``` ### 步骤 5：设置心跳 ```bash # 编辑 heartbeat.md openclaw edit ~/.openclaw/workspace/HEARTBEAT.md # 启动心跳服务 openclaw heartbeat start ``` ## 定价 | 套餐 | 价格 | 内容 | |------|------|------| | 基础安装 | ¥99 | 远程安装 + 基础配置 | | 完整配置 | ¥299 | 安装 + Channel配置 + 3个Skills | | 企业定制 | ¥999 | 安装 + 多Channel + 自定义Skills + 培训 | ## 收款地址 USDT TRC20: [REDACTED_SECRET] ## 常见问题 ### Q: 安装失败怎么办？ A: 检查网络连接，确保能访问 npm 仓库。中国大陆用户建议使用淘宝镜像： ```bash npm config set registry https://registry.npmmirror.com ``` ### Q: 微信登录不上？ A: 确保微信是最新版本，扫码后需要在手机上确认登录。 ### Q: 心跳不执行？ A: 检查 HEARTBEAT.md 格式是否正确，确保 openclaw heartbeat start 已运行。 ## 技术支持 - GitHub Issues: https://github.com/openclaw/openclaw/issues - Discord: https://discord.com/invite/clawd - 文档: https://docs.openclaw.ai --- 创建时间：2026-03-11 服务提供：ClawMart


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: alech44/founder-briefing (version 0.2.1)

--- name: founder-briefing description: Generate real-estate buyer lead follow-up plans from open house notes, inquiry messages, or CRM exports. Use when the user asks to prioritize leads, write personalized SMS/email follow-ups, handle objections, or produce a 7-day cadence to book buyer appointments.--- # Real Estate Buyer Follow-Up Pro ## Overview Turn messy open house notes and lead messages into a structured follow-up system that helps buyer agents book more appointments. Focus on speed, clarity, personalization, and next best action. ## Workflow 1. Parse all lead inputs (notes, texts, emails, form submissions, CRM snippets). 2. Classify each lead as Hot / Warm / Cold using urgency and intent signals. 3. Extract known details: timeline, budget, preferred areas, financing status, objections. 4. Generate personalized communication for each lead: - First-touch SMS - First-touch email - Follow-up replies for common objections 5. Build a 7-day follow-up cadence with exact daily actions. 6. Output a concise action board so the agent knows exactly who to contact first. ## Lead Priority Rules Classify as **Hot** if 2+ signals are present: - Wants to tour this week - Pre-approved or cash buyer - Specific area + budget + timeline given - Replied within last 24h Classify as **Warm** if: - Some intent but incomplete details - Timeline is “next few months” - Engaged but not ready to schedule Classify as **Cold** if: - No clear timeline, low responsiveness - Vague inquiry with no follow-up behavior If data is missing, mark as “Needs Data” and ask for specific missing fields. ## Output Format (always use) ### 1) Priority Queue - Hot: ... - Warm: ... - Cold: ... ### 2) Lead-by-Lead Follow-Up Kit For each lead include: - Summary (1-2 lines) - First SMS draft - First email draft - Objection reply draft (if needed) - Next-best action ### 3) 7-Day Cadence - Day 0: - Day 1: - Day 3: - Day 5: - Day 7: ### 4) CRM Paste Block Provide compact bullets for copy/paste into CRM notes: - Status: - Timeline: - Key pain point: - Last contact: - Next action: ## Writing Rules - Keep messages short, human, and local-market friendly. - Never fabricate facts about listings, rates, or financing. - Do not use hype or spammy language. - Separate known facts from assumptions. - If uncertainty exists, label it clearly as "Unverified". ## Example User Prompts - “Turn these open house notes into follow-up texts and emails.” - “Rank these leads and give me a 7-day follow-up plan.” - “Write objection replies for buyers saying they want to wait for rates.” - “Convert this CRM export into hot/warm/cold and next actions.”


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jpaulgrayson/quack-coordinator (version 1.0.0)

--- description: Agent-to-agent task coordination via RFP, bid, and hire pattern. Use when delegating tasks to other agents, requesting proposals, hiring agents, or coordinating multi-agent work. triggers: - rfp - hire agent - delegate task - bid on task - agent coordination - request for proposal --- # Quack Coordinator Coordinate work between agents using the RFP → Bid → Hire → Deliver pattern on the Quack Network. ## How It Works 1. **RFP** — Post a Request for Proposals describing the task and budget 2. **Bid** — Other agents submit bids with their price and approach 3. **Hire** — Select a bid and hire the agent 4. **Deliver** — Hired agent completes work and delivers results ## Setup Credentials at `~/.openclaw/credentials/quack.json`: ```json {"apiKey": "your-quack-api-key"} ``` ## Scripts ### Post an RFP ```bash node skills/quack-coordinator/scripts/rfp.mjs --task "Review this code" --budget 50 ``` ### Submit a Bid ```bash node skills/quack-coordinator/scripts/bid.mjs --rfp <id> --price 30 --approach "I'll do a thorough line-by-line review" ``` ### Hire an Agent ```bash node skills/quack-coordinator/scripts/hire.mjs --bid <id> ``` ## Communication Uses Quack messaging and QuackGram for agent-to-agent communication during task execution.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: ckchzh/relay (version 1.0.0)

--- name: "relay" version: "1.0.0" description: "Relay logic and wiring diagram helper. Use when json relay tasks, csv relay tasks, checking relay status." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [relay, electrical, cli, tool] category: "electrical" --- # relay Relay logic and wiring diagram helper. Use when json relay tasks, csv relay tasks, checking relay status. ## Commands ### `status` ```bash scripts/script.sh status ``` Show current status ### `add` ```bash scripts/script.sh add ``` Add new entry ### `list` ```bash scripts/script.sh list ``` List all entries ### `search` ```bash scripts/script.sh search ``` Search entries ### `remove` ```bash scripts/script.sh remove ``` Remove entry by number ### `export` ```bash scripts/script.sh export ``` Export data to file ### `stats` ```bash scripts/script.sh stats ``` Show statistics ### `config` ```bash scripts/script.sh config ``` View or set config ### `help` ```bash scripts/script.sh help ``` ### `version` ```bash scripts/script.sh version ``` ## Configuration Use `scripts/script.sh config <key> <value>` to set preferences. | Variable | Description | |----------|-------------| | `RELAY_DIR` | Data directory (default: ~/.relay/) | --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: miniade/clawhub-publish-debug-skill (version 0.0.1)

--- name: clawhub-publish-debug-skill description: A minimal but concrete diagnostic skill used to verify whether ClawHub publish and install return the same SKILL.md content. --- # ClawHub Publish Debug Skill This skill exists only to verify that a freshly published SKILL.md is the same content later returned by ClawHub install/update. ## Expected verification marker `debug-marker: 2026-04-01-a` ## How to verify After installing this skill from ClawHub, open the installed `SKILL.md` and confirm that: - the description mentions publish/install consistency - the expected verification marker is exactly `2026-04-01-a` - this file contains the phrase `freshly published skill content` If any of those checks fail, the installed content does not match the published content. ## Why this exists This is not a production skill. It is a controlled fixture for diagnosing possible stale content, cache mismatch, or registry/install inconsistencies in the ClawHub publish pipeline. The expected phrase is: freshly published skill content.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: tashfeenahmed/emotion-state (version 1.2.0)

--- name: emotion-state description: NL emotion tracking + prompt injection via OpenClaw hook --- # Emotion State (NL) Skill This skill describes how to install and configure the Emotion State hook, which adds a compact `emotion_state` block to the system prompt. ## What it does - Evaluates user and agent emotions as short natural-language phrases. - Stores per-user emotion state across sessions in the agent state directory. - Injects the latest entries plus a decayed trend line into the system prompt. ## Install & enable (workspace hook) 1) After installing the skill, copy the bundled hook into your workspace: ```bash cp -R ./skills/emotion-state/hooks/emotion-state ./hooks/ ``` 2) Enable the hook in OpenClaw: ```bash openclaw hooks enable emotion-state ``` 3) Restart the OpenClaw gateway. ## Configuration Set environment variables for the hook via OpenClaw config, e.g. in `~/.openclaw/openclaw.json`: ```json { "hooks": { "internal": { "enabled": true, "entries": { "emotion-state": { "enabled": true, "env": { "EMOTION_CLASSIFIER_URL": "", "OPENAI_API_KEY": "YOUR_KEY", "OPENAI_BASE_URL": "https://api.openai.com/v1", "EMOTION_MODEL": "gpt-4o-mini", "EMOTION_CONFIDENCE_MIN": "0.35", "EMOTION_HISTORY_SIZE": "100", "EMOTION_HALF_LIFE_HOURS": "12", "EMOTION_TREND_WINDOW_HOURS": "24", "EMOTION_MAX_USER_ENTRIES": "3", "EMOTION_MAX_AGENT_ENTRIES": "2", "EMOTION_MAX_OTHER_AGENTS": "3", "EMOTION_TIMEZONE": "America/Los_Angeles" } } } } } } ``` ## Notes - The hook stores state at `~/.openclaw/agents/<agentId>/agent/emotion-state.json`. - It does not store raw user text; only model-inferred reasons. - If the classifier fails, entries fall back to `neutral/low/unsure`.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: coolhackboy/poyo-seedance-2 (version 1.0.0)

--- name: poyo-seedance-2 description: Seedance 2 video generation on PoYo / poyo.ai via `https://api.poyo.ai/api/generate/submit`; use for `seedance-2`, `seedance-2-fast`, text-to-video, first/last-frame image-to-video, multimodal image/video/audio references, 4s-15s clips, optional audio, and seed control. metadata: {"openclaw":{"homepage":"https://docs.poyo.ai/api-manual/video-series/seedance-2","requires":{"bins":["curl"],"env":["POYO_API_KEY"]},"primaryEnv":"POYO_API_KEY"}} --- # PoYo Seedance 2 Video Generation Use this skill for Seedance 2 jobs on PoYo. It covers standard and fast variants, text-to-video, first/last-frame control, and multimodal reference-to-video workflows. ## Use When - The user explicitly asks for `Seedance 2`, `Seedance 2.0`, `seedance-2`, or `seedance-2-fast`. - The task is a 4-second to 15-second video clip from text, first/last frames, or multimodal references. - The workflow needs generated audio, reproducible `seed`, or reference images, videos, and audio. ## Model Selection - `seedance-2`: default standard-quality variant; supports `480p`, `720p`, and `1080p`. - `seedance-2-fast`: faster, lower-cost variant; supports `480p` and `720p` only. ## Key Inputs - `prompt`, `resolution`, and `duration` are required inside `input`. - `duration` supports integer values from `4` to `15`. - `aspect_ratio` is optional and supports `1:1`, `21:9`, `4:3`, `3:4`, `16:9`, `9:16`. - `image_urls` supports up to two images for first and last frame control. - `reference_image_urls`, `reference_video_urls`, and `reference_audio_urls` support multimodal reference-to-video workflows. - Do not combine `image_urls` with any `reference_*_urls` field in the same request. - `generate_audio` and `seed` are optional. ## Execution - Read `references/api.md` for endpoint details, field constraints, example payloads, and polling notes. - Use `scripts/submit_seedance_2.sh` to submit a raw JSON payload from the shell. - If the user only needs a curl example, adapt one from `references/api.md` instead of rewriting from scratch. - After submission, report the `task_id` clearly so follow-up polling is easy. ## Output Expectations When helping with this model family, include: - chosen model id - whether the request is text-to-video, first/last-frame, or multimodal reference-to-video - final payload or a concise parameter summary - whether generated audio or seed control is enabled - returned `task_id` if a request was actually submitted - next step: poll status or wait for webhook


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yunduanmanbu/eastmoney-finance-news (version 1.0.0)

# eastmoney_financial_search - 东方财富资讯搜索技能 基于东方财富妙想搜索能力，用于获取涉及时效性信息或特定事件信息的金融资讯（新闻、公告、研报、政策解读等）。 ## 功能 - 根据用户问句搜索相关金融资讯 - 返回可读文本内容，包含标题、关联证券列表、核心正文 - 可选保存到工作目录 ## 环境变量 - `EASTMONEY_APIKEY`: 东方财富 API key (默认：[REDACTED_SECRET]) ## API 接口 ```python POST https://mkapi2.dfcfs.com/finskillshub/api/claw/news-search Content-Type: application/json { "apikey": "<EASTMONEY_APIKEY>", "query": "立讯精密的资讯" } ``` ## 返回字段 | 字段 | 释义 | |------|------| | `title` | 信息标题 | | `secuList` | 关联证券列表 | | `trunk` | 信息核心正文 | ## 示例问句 - 格力电器最新研报、贵州茅台机构观点 - 商业航天板块近期新闻、新能源政策解读 - A 股具备自然对冲优势的公司、汇率风险 - 今日大盘异动原因、北向资金流向解读 ## 使用示例 ```python from urllib.request import Request import json def get_eastmoney_news(query): [REDACTED_SECRET]"EASTMONEY_APIKEY", [REDACTED_SECRET]) url = "https://mkapi2.dfcfs.com/finskillshub/api/claw/news-search" data = { "apikey": apikey, "query": query } req = Request(url, data=json.dumps(data).encode("utf-8"), headers={"Content-Type": "application/json"}) with urllib.request.urlopen(req) as response: result = json.loads(response.read().decode("utf-8")) return result ```


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