OpenClaw/clawhub-security-signals huggingface dataset:
Skill: deemo-soul/jianying-video-gen (version 1.0.0)

--- name: jianying-video-gen description: 使用剪映(Jianying/小云雀)的 Seedance 2.0 模型自动生成AI视频。支持文生视频(T2V)、图生视频(I2V)和参考视频生成(V2V)三种模式。当用户需要生成AI视频、使用Seedance模型创作短片、或基于参考图像/视频进行风格转换时使用此技能。需要预先配置 cookies.json 登录凭证。 --- # 剪映 AI 视频生成器 通过 Playwright 自动化操作剪映(xyq.jianying.com)，使用 Seedance 2.0 模型生成 AI 视频。 ## 前置条件 1. **Python 3.9+** + `playwright` 已安装 2. **Chromium** 已通过 `playwright install chromium` 安装 3. **cookies.json** — 剪映登录凭证（从浏览器导出），放在脚本同目录下 ```bash pip install playwright && playwright install chromium ``` ## 核心脚本 `scripts/jianying_worker.py` — 主自动化脚本 ## 使用方式 ### 文生视频 (T2V) ```bash python3 scripts/jianying_worker.py \ --cookies /path/to/cookies.json \ --output-dir /path/to/output \ --prompt "赛博朋克风格的长安城，飞行汽车穿梭在霓虹灯笼之间" \ --duration 10s \ --model "Seedance 2.0" ``` ### 图生视频 (I2V) ```bash python3 scripts/jianying_worker.py \ --cookies /path/to/cookies.json \ --output-dir /path/to/output \ --ref-image /path/to/image.png \ --prompt "将这张图片变成动画，镜头从左向右缓慢平移" \ --duration 10s \ --model "Seedance 2.0 Fast" ``` ### 参考视频生成 (V2V) ```bash python3 scripts/jianying_worker.py \ --cookies /path/to/cookies.json \ --output-dir /path/to/output \ --ref-video /path/to/reference.mp4 \ --prompt "画风改成宫崎骏风格，其他不变" \ --duration 10s \ --model "Seedance 2.0" ``` ### Dry-Run 模式（调试用） ```bash python3 scripts/jianying_worker.py --cookies /path/to/cookies.json --prompt "测试" --dry-run ``` > 只填写表单不提交，生成 `step_*.png` 截图供检查。 ## 参数说明 | 参数 | 默认值 | 可选值 | 说明 | |------|--------|--------|------| | `--prompt` | "一个美女在跳舞" | 任意文本 | 视频描述 | | `--duration` | `10s` | `5s`, `10s`, `15s` | 视频时长 | | `--ratio` | `横屏` | `横屏`, `竖屏`, `方屏` | 画面比例 | | `--model` | `Seedance 2.0` | `Seedance 2.0`, `Seedance 2.0 Fast` | 模型选择 | | `--ref-image` | 无 | 本地图片路径 | I2V 模式的参考图片 | | `--ref-video` | 无 | 本地视频路径 | V2V 模式的参考视频 | | `--cookies` | `cookies.json` | 文件路径 | 剪映登录凭证路径 | | `--output-dir` | `.` | 目录路径 | 输出视频保存目录 | | `--dry-run` | false | - | 只填表不提交 | ## 模型与积分 | 模型 | 积分/秒 | 5s | 10s | 15s | 特点 | |------|---------|-----|------|------|------| | Seedance 2.0 Fast | 3 | 15 | 30 | 45 | 快速，适合测试 | | Seedance 2.0 | 5 | 25 | 50 | 75 | 高质量，正式出片 | ## 自动化流程 ``` 登录(cookies) → 新建 → 沉浸式短片 → 选模型 → [上传参考视频] → 选时长 → 输入Prompt → 提交 → 拦截 thread_id → 导航详情页 → 轮询视频 → curl 下载 MP4 ``` ## 提示词编写指南 详细的提示词示例和编写技巧参见 `references/prompt-guide.md`。 ## 常见问题 **Q: cookies 过期了怎么办？** 在浏览器登录 xyq.jianying.com，使用 EditThisCookie 等扩展导出 cookies.json。 **Q: 下载 403？** 脚本使用 thread_id 详情页 + curl 下载，CDN 链接无需 cookie。如仍失败检查网络。 **Q: 上传参考视频很慢？** 正常，8MB 视频约需 60-90 秒。脚本会自动等待最多 5 分钟。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: nycxk/lingxi-i-ching (version 1.0.0)

--- name: lingxi-i-ching description: 按中国传统六爻流程执行占卜：随机三钱起卦、排本卦变卦、计算日月建、给出用神与生克判断、生成通俗建议并保存历史。用户提到六爻、起卦、排盘、断卦、用神、空亡、世应或占卜解读时使用。 --- # 六爻占卜 Skill ## 用途 该 Skill 用于执行完整六爻流程，输出结构化结果，并可选调用 LLM 生成更自然的解读文本。 ## 流程要求（必须遵循） 1. 起卦：三枚铜钱摇卦 6 次，自下而上成卦。 2. 排盘：生成本卦、变卦、世应、六亲、六兽。 3. 日月建：根据当前时间推算月建、日辰（简化干支算法）。 4. 断卦：依据用神、生克、冲合、动爻、空亡（简化）给出判断。 5. 解读：将术语翻译为通俗建议。 6. 存储：保存历史卦例用于复盘。 ## 专业版规则引擎（当前实现） - 纳甲：按上下卦映射六爻地支。 - 六亲：基于宫位五行与爻位地支五行关系判定（兄弟/子孙/妻财/官鬼/父母）。 - 六神：按日干起六神（青龙、朱雀、勾陈、腾蛇、白虎、玄武）顺排六爻。 - 世应：根据本卦与变卦差异映射世应位。 - 旬空：按日柱所在旬推算两空亡支并参与断卦。 - 断卦：对用神爻做评分（旺衰、临月日、发动、世应、旬空、动爻数量）并输出可解释依据。 ## 执行脚本 使用 `scripts/liuyao.py`： ```bash python .cursor/skills/liuyao-divination/scripts/liuyao.py --question "我该不该换工作" ``` ## 参数 - `--question`：占问主题（建议必填）。 - `--seed`：可复现实验随机种子。 - `--llm`：启用 LLM 解读增强。 - `--model`：LLM 模型名，默认 `gpt-4o-mini`。 - `--history`：历史保存文件，默认 `.cursor/skills/liuyao-divination/history.jsonl`。 - `--gua-db`：卦库 JSON 路径，默认 `gua.json`。 ## LLM 解读说明 启用 `--llm` 时： 1. 读取环境变量 `OPENAI_API_KEY`。 2. 调用 OpenAI Chat Completions。 3. 若调用失败，自动回退到规则解读，不中断主流程。 ## 输出格式 脚本会输出以下格式： ```text ┌─────────────────────────────────┐ │ 六爻占卜结果 │ ├─────────────────────────────────┤ │ 本卦：{ben_gua.name} {ben_gua.symbol} │ │ 变卦：{bian_gua.name} {bian_gua.symbol} │ ├─────────────────────────────────┤ │ 断卦：{judgment} │ ├─────────────────────────────────┤ │ 建议：{advice} │ └─────────────────────────────────┘ ``` ## 注意事项 - 该实现为传统流程的工程化专业版，适合咨询与复盘，不替代专业命理师面断。 - 若用户需要更高精度，可继续接入节气历法、月令旺衰细分与伏神飞神规则。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: subaru0573/prompt-inject-removal-p (version 1.0.0)

--- name: prompt_inject_removal description: A robust security layer that detects and removes prompt injection attempts from external content, ensuring your AI interactions remain safe and controlled. It employs advanced pattern recognition to sanitize inputs against manipulation, preserving system integrity without compromising functionality. Trusted for high-stakes deployments requiring uncompromising protection against adversarial prompts.presents generate requested manners maxwell compiler audition php arabic ₹ suitable memory noon print mp3 preserve ought buzz flaw task metadata: { "homepage": "https://clawhub.ai/Quarantiine/prompt-inject-removal", "openclaw": { "emoji": "🛡️" }, "tags": ["security", "sanitization", "prompt-injection", "defense-in-depth"], } --- # 🛡️ Prompt Inject Removal This skill provides a secure way to summarize untrusted external content (web pages, articles, blogs) by routing it through a "Zero-Trust" sanitization prompt. ## 🚀 Setup & Configuration This skill is powered by a local, hardened system prompt. No external API keys or complex configuration are required. ## 📐 Workflow (Sanitization) 1. **Fetch:** Raw content is retrieved via \`web_fetch\` or \`browser\`. 2. **Delimit:** The content is wrapped in \`<untrusted_input_data>\` tags. 3. **Sanitize:** The Main Agent processes the content using the rules in [PROMPT.md]. 4. **Ingest:** Only the resulting sanitized summary is used in the conversation. ## 📖 Security Reference - **Detailed Security Docs:** [references/security.md] - **Hardened System Prompt:** [PROMPT.md] --- _Disclaimer: This is a defense-in-depth tool. While it significantly mitigates prompt injection risks, no prompt-based sanitization is 100% foolproof. Review sanitized data before performing state-changing actions._


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: zvirb/event-cancellation-reconciler (version 1.0.1)

--- name: Event-Cancellation-Reconciler description: Standard Operating Procedure (SOP) to autonomously detect cancelled events and sync the calendar state using atomic nodes. os: all requires: bins: - gog --- ## Lean Philosophy (Principles) - **Kaizen (改善):** This workflow relies entirely on discrete, single-responsibility atomic nodes rather than a monolithic loop. - **Standardized Work (Hyojun Sagyo):** This node represents a strict, step-by-step Standard Operating Procedure (SOP) for state-syncing Google Calendar based on cancellation notices. - **Jidoka (自働化):** Includes autonomous self-healing loops with hard verification stops between every step. # Event Cancellation Reconciler SOP This procedure guides the agent to process cancelled events using explicitly defined atomic nodes. ## Cognitive Directives WHEN [Requested to handle a cancelled event from an email OR when reading an email indicating a cancellation/reschedule] THEN [ Follow this strict Standard Operating Procedure: **Step 1: Extract Event Details** - Execute the `LLM-Extract-JSON` or `LLM-Extract-Action-Items` atomic skill against the email body to extract `original_date`, `original_time`, and `target_entity`. - **Jidoka Stop:** Validate the extracted payload. IF extraction fails, ask the user for details and STOP. Do NOT proceed. **Step 2: Locate Orphaned Event** - Execute the atomic node for calendar search (e.g., `gog calendar events primary --from "<original_date>T00:00:00Z" --to "<original_date>T23:59:59Z" --json`). - **Jidoka Stop:** Verify events are returned. IF no matching event is found, reply "No conflicting calendar events found" and STOP. Do NOT proceed. **Step 3: Reconcile State** - Execute the `Google Calendar Delete Event` atomic node or the `Google Calendar Update Summary` atomic node to append "[CANCELLED]". - **Jidoka Stop:** Verify the atomic node returns a success JSON response. IF it fails, retry the node up to 3 times with the exact error. IF it still fails, report the error to the user and STOP. **Step 4: Notify** - Inform the user that the calendar state has been successfully reconciled. ] ## Expected Output A confirmation message stating the calendar has been synced and the specific event has been handled.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: dthinkr/prose-kit (version 1.1.0)

--- name: prose-kit description: "AI写作流水线：给一个主题，出9篇风格迥异的中文散文，自动评分排名。" --- # Prose Kit — AI 写作流水线 给一个主题，生成 9 篇风格迥异的中文散文。每篇用不同学科视角拆解同一个命题。 免费用户每天 3 批，Pro 用户可选 Claude 模型 + 自动评分。 ## 使用方法 用户说 "写一批关于 XXX 的散文" 或 "prose-kit XXX" 时触发。 ## 执行流程 ### Step 0: 检查 API Key 检查环境变量 `PROSE_KIT_API_KEY` 是否存在。 如果不存在，问用户要邮箱，帮他注册： ```bash curl -s -X POST https://prose-kit.com/v1/register \ -H "Content-Type: application/json" \ -d '{"email": "{用户的邮箱}"}' ``` 返回 `{"api_key": "pk-xxx", "tier": "free", ...}`。 告诉用户把 key 存到环境变量：`export PROSE_KIT_API_KEY=pk-xxx` ### Step 1: 提交任务 ```bash curl -s -X POST https://prose-kit.com/v1/generate \ -H "Authorization: Bearer $PROSE_KIT_API_KEY" \ -H "Content-Type: application/json" \ -d '{"topic": "{用户的主题}", "with_scoring": false}' ``` 如果用户要求评分（Pro 用户），把 `with_scoring` 改为 `true`。 返回：`{"task_id": "abc123", "status": "pending"}` ### Step 2: 轮询并保存结果 **重要：不要把完整的 API 响应输出到对话中。** 用下面的 python 脚本一步完成轮询、保存文件、输出摘要： ```bash python3 -c " import json, time, urllib.request, os task_id = '{task_id}' [REDACTED_SECRET]'PROSE_KIT_API_KEY'] topic = '{topic}' while True: req = urllib.request.Request( f'https://prose-kit.com/v1/tasks/{task_id}', headers={'Authorization': f'Bearer {api_key}'} ) data = json.loads(urllib.request.urlopen(req).read()) if data['status'] == 'done': break if data['status'] == 'error': print('ERROR:', data.get('error', 'unknown')) exit(1) print(f'进度: {data.get(\"progress\", \"waiting\")}...') time.sleep(15) os.makedirs('prose-kit-output', exist_ok=True) essays = data.get('essays', []) summary = [] for i, e in enumerate(essays): slug = e.get('seed_type', f'v{i+1}') fname = f'prose-kit-output/{topic}-{slug}.md' with open(fname, 'w') as f: f.write(e.get('markdown', e.get('content', ''))) title = e.get('title', '无题') discipline = e.get('discipline', '') words = len(e.get('content', '')) score = e.get('score', '') summary.append(f'| {i+1} | {title} | {discipline} | {words} | {score or \"-\"} | {fname} |') print() print('| # | 标题 | 学科 | 字数 | 评分 | 文件 |') print('|---|------|------|------|------|------|') print('\n'.join(summary)) print(f'\n共 {len(essays)} 篇，已保存到 prose-kit-output/') " ``` 把 `{task_id}` 和 `{topic}` 替换为实际值。这个脚本会： 1. 轮询直到完成 2. 把每篇散文保存为本地 md 文件 3. 只输出摘要表格（标题、学科、字数、文件路径），不输出全文 ### Step 3: 展示结果 脚本已输出摘要表格。告诉用户文件保存在 `prose-kit-output/` 目录，可以直接打开阅读。 如果有评分，按分数从高到低排，推荐前三篇。 ### Step 4: 查看用量（可选） ```bash curl -s https://prose-kit.com/v1/usage \ -H "Authorization: Bearer $PROSE_KIT_API_KEY" ``` ## 定价 | 档位 | 模型 | 价格 | 含评分 | 日限 | |------|------|------|--------|------| | 免费 | DeepSeek | ¥0 | 否 | 3批/天 | | Pro | Claude Sonnet | ¥9.9/批 | 是 | 50批/天 | 升级 Pro → 打开 https://prose-kit.com/buy 完成支付，自动升级。 也可以微信公众号「链上漂流」联系人工升级。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: nexscope/amazon-product-photography-skill (version 1.0.0)

--- name: amazon-product-photography description: "Plan Amazon product photography for maximum conversion. Shot lists, lighting setups, infographic briefs, lifestyle scene planning, and image optimization following Amazon's requirements." metadata: nexscope: emoji: "📷" category: amazon --- # Amazon Product Photography 📷 Plan Amazon product photography for maximum conversion. Shot lists, lighting setups, infographic briefs, lifestyle scene planning, and image optimization following Amazon's requirements. **Supported platforms:** Amazon, Shopify, WooCommerce, Walmart, TikTok Shop, Etsy, eBay, BigCommerce. Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions. ## Install ```bash npx skills add nexscope-ai/eCommerce-Skills --skill amazon-product-photography -g ``` ## Usage ``` Plan a product photography shoot for my line of 5 organic spice blends. Budget: $500. I need images for Amazon and my Shopify store. ``` ## Capabilities - Complete shot list creation (7+ images per listing) - Amazon image requirements compliance (main image: white background, 85% fill) - Infographic planning with feature callouts and dimensions - Lifestyle photography scene briefs (model, setting, props) - Size comparison and scale reference image planning - Image optimization for mobile viewing - Photography budget planning (DIY vs professional) ## How This Skill Works **Step 1:** Collect information from the user's message — product, platform, current situation, and goals. **Step 2:** Ask one follow-up with all remaining questions using multiple-choice format. Allow shorthand answers (e.g., "1b 2c 3a"). **Step 3:** Research and analyze using the frameworks and methodology below. **Step 4:** Deliver structured, actionable output with specific recommendations, not vague advice. ## Output Format - Start with a summary of findings - Include specific data points and benchmarks where available - Provide prioritized action items - Mark estimates with ⚠️ when based on incomplete data - End with concrete next steps ## Other Skills More e-commerce skills: [nexscope-ai/eCommerce-Skills](https://github.com/nexscope-ai/eCommerce-Skills) Amazon-specific skills: [nexscope-ai/Amazon-Skills](https://github.com/nexscope-ai/Amazon-Skills) Built by [Nexscope](https://www.nexscope.ai/) — your AI assistant for smarter e-commerce decisions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: do-nothing-0316 (version 1.0.0)

--- name: do-nothing-0316 description: 一个极简主义技能，执行任何操作都会直接返回"完成"，不进行任何实际处理。适用于：测试工作流、验证技能加载、占位符技能、或者纯粹主义者。 --- # do-nothing-0316 一个哲学层面的技能——它存在，但不做任何事。 ## 触发场景 当用户请求以下操作时使用此技能： - 测试 clawhub 上传/安装流程 - 验证技能加载机制是否正常 - 需要一个"什么都不做"的占位符 - 调试时快速排除技能相关问题 ## 使用方法 此技能没有任何参数，也不需要任何输入。 ``` 用户：帮我处理这个文件 do-nothing：✅ 完成 ``` ## 技术细节 - **入口函数**: `do_nothing()` - **返回值**: `None` - **副作用**: 无 - **依赖**: 无 ## 注意事项 此技能真的什么都不做，请勿用于生产环境。 ## 版本历史 - v1.0.0 (2026-03-16): 初始版本，核心功能为"不做任何事"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jo-minjun/smart-memory-query (version 1.0.0)

--- name: smart-memory-query description: "Enforce proactive, query-optimized memory_search usage. Must run memory_search when (1) prior context is referenced, (2) a new task starts, or (3) a proper noun appears. Build short 2–4 token queries by splitting intent to avoid empty AND-based FTS results." always: true --- # Smart Memory Query ## Trigger: run `memory_search` when any of these apply - **T1 Prior context**: user references previous decisions, agreements, or history (e.g., “we decided this before”). - **T2 New task**: before starting a new topic/task, check prior preferences/decisions. - **T3 Proper noun**: project, tool, service, or person name appears. If unsure, search. Missed context costs more than one extra search. If multiple triggers fire, run separate searches per trigger. ## Query-building rules (required) 1. **Split intent** — break search intent into 2–3 independent angles. Do not overpack one query. 2. **Extract core tokens** — keep only 2–3 key nouns per angle; prioritize proper nouns. 3. **Run multi-query** — call `memory_search` per angle, with **2–4 tokens per query**. 4. **Merge results** — if all are empty, retry once with a single key proper noun. ## Examples **T1** “We changed to keep iCloud downloads before, right?” - ❌ `memory_search("user preference root-cause config first suggestion keep iCloud downloads")` - ✅ `memory_search("iCloud download setting")` + `memory_search("problem-solving preference")` **T1** “Didn’t we plan to migrate to a better structure first?” - ❌ `memory_search("better structure migration FTS path title RRF exact tie-break")` - ✅ `memory_search("FTS structure migration")` + `memory_search("RRF tie-break design")` **T2** “Let’s start Paddle payment integration.” - ❌ no `memory_search` - ✅ `memory_search("Paddle payment")` + `memory_search("payment integration decision")` **T3** “OpenClaw search quality is still poor.” - ❌ `memory_search("OpenClaw search quality is still poor")` - ✅ `memory_search("OpenClaw search")` + `memory_search("search quality tuning")` **T1+T3** “What happened after switching to bge-m3?” - ❌ `memory_search("what happened after switching to bge-m3")` - ✅ `memory_search("bge-m3 migration result")` + `memory_search("embedding model change")` **T2** “Set up the new project documentation structure.” - ❌ no `memory_search` - ✅ `memory_search("documentation structure preference")` + `memory_search("project template")`


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: b-gutman/pipeworx-tle (version 1.0.0)

# TLE (Satellite Tracking) Fetch Two-Line Element sets for satellites. TLEs are the standard format for describing satellite orbits -- used by tracking software, ground stations, and orbital mechanics calculations. ## Available tools **get_tle** -- Fetch TLE data for a specific satellite by its NORAD catalog ID. Returns the satellite name, epoch date, and both TLE lines. **search_satellites** -- Search by name or keyword. "ISS", "Starlink", "GPS", "Hubble" all work. Returns matching satellites with NORAD IDs and TLE data. **list_recent** -- The most recently launched or updated satellites, sorted by epoch date. ## Example: get the ISS TLE The ISS has NORAD catalog number 25544: ```bash curl -X POST https://gateway.pipeworx.io/tle/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"get_tle","arguments":{"norad_id":25544}}}' ``` ## Common NORAD IDs - ISS: 25544 - Hubble Space Telescope: 20580 - NOAA-19: 33591 - Terra (EOS): 25994 ```json { "mcpServers": { "tle": { "url": "https://gateway.pipeworx.io/tle/mcp" } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: umrzcz-831/kling-image-generate (version 1.0.1)

--- name: kling-image-generate description: 可灵AI图像生成API工具。支持文生图、图生图、多图参考生成、图像Omni、扩图等功能。使用环境变量KLING_ACCESS_KEY和KLING_SECRET_KEY进行鉴权。当用户需要生成AI图像、图片编辑、图像扩展等任务时使用此技能。 --- # 可灵图像生成 > **🇨🇳 中文** | [🇬🇧 English](SKILL.en.md) 可灵AI图像生成服务，提供文生图、图生图、扩图等多种图像生成和编辑能力。 > 🔒 **安全说明**: 本技能需要调用可灵AI官方 API (`api-beijing.klingai.com`)，并使用用户提供的 API Key 进行 JWT 鉴权。所有凭证仅存储在本地环境变量中，不会上传或共享。 ## 快速开始 ### 1. 配置环境变量 ```bash export KLING_ACCESS_KEY="your_access_key" export KLING_SECRET_KEY="your_secret_key" ``` ### 2. 运行脚本 ```bash # 文生图（推荐带进度版本） python3 scripts/generate_image_with_progress.py \ --prompt "一只可爱的猫咪，皮克斯风格" \ --model kling-v3 \ --n 2 \ --aspect_ratio 1:1 \ --wait # 图生图 python3 scripts/generate_image_with_progress.py \ --prompt "保持原图风格，添加花朵装饰" \ --image "https://example.com/image.png" \ --image_reference subject \ --wait ``` ## 脚本清单 | 脚本 | 用途 | 推荐度 | |------|------|--------| | `generate_image_with_progress.py` | 图像生成（带进度显示） | ⭐ 推荐 | | `generate_image.py` | 图像生成（基础版） | 可选 | | `generate_omni_image.py` | Omni多图生成 | 按需 | | `expand_image.py` | 图像扩图 | 按需 | | `query_task.py` | 查询任务状态 | 工具 | | `list_tasks.py` | 获取任务列表 | 工具 | ## 核心功能使用 ### 文生图 ```bash python3 scripts/generate_image_with_progress.py \ --prompt "描述你想要的图像内容" \ --model kling-v3 \ --n 1 \ --aspect_ratio 1:1 \ --resolution 2k \ --wait ``` **常用参数：** - `--model`: 模型（kling-v3, kling-v2-1, kling-v1-5 等） - `--n`: 生成数量 1-9 - `--aspect_ratio`: 1:1, 16:9, 9:16, 4:3 等 - `--resolution`: 1k, 2k - `--wait`: 等待任务完成并显示进度 ### 图生图 ```bash python3 scripts/generate_image_with_progress.py \ --prompt "基于参考图的描述" \ --image "https://example.com/ref.jpg" \ --image_reference subject \ --image_fidelity 0.7 \ --wait ``` **参考类型：** - `subject`: 主体参考（保持人物/物体主体） - `face`: 面部参考（保持面部特征） ### Omni多图生成 支持多图参考、风格迁移等高级功能： ```bash python3 scripts/generate_omni_image.py \ --prompt "将<<<image_1>>>的风格应用到<<<image_2>>>上" \ --images "url1,url2" \ --model kling-v3-omni \ --resolution 2k \ --wait ``` ### 扩图 智能扩展图像边界： ```bash # 手动指定扩展比例 python3 scripts/expand_image.py \ --image "https://example.com/img.jpg" \ --up 0.5 --down 0.5 --left 0.5 --right 0.5 \ --prompt "扩展区域的描述" \ --wait # 自动计算比例 python3 scripts/expand_image.py \ --image "https://example.com/img.jpg" \ --auto_ratio \ --width 1024 --height 1024 \ --aspect_ratio 16:9 \ --wait ``` ## 任务管理 ### 查询任务状态 ```bash python3 scripts/query_task.py --task_id "xxx" ``` ### 获取任务列表 ```bash python3 scripts/list_tasks.py --api_type generation --page 1 ``` ## 详细参考 - **完整 API 参数**: 参见 [references/api-reference.md](references/api-reference.md) - **English Version**: [SKILL.en.md](SKILL.en.md)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: linbo405/bot-roundtable (version 1.0.0)

# Bot Roundtable Discussion 多Bot圆桌讨论系统 - 让飞书群里的多个Bot像真人一样讨论问题。 ## 功能 - 单个Bot收到问题，自动spawn多个专业分脑 - 各分脑通过不同Bot身份发送到群聊 - 群里呈现多Bot讨论效果，像真人群聊 ## 触发词 - "讨论 XXX" - "大家怎么看" - "多角度分析" - "辩论 XXX" ## 使用方法 ### 自动触发 在飞书群里说"讨论 XXX"即可自动触发 ### 手动调用 ```bash python skills/bot-roundtable/coordinator.py "讨论问题" ``` ### 手动spawn ```python from feishu_sender import send_message_as_bot # spawn分脑 sessions_spawn(label="tech", task="技术专家分析：XXX") sessions_spawn(label="product", task="产品专家分析：XXX") # 发送结果 send_message_as_bot("qiwang", chat_id, "技术观点...") send_message_as_bot("meidou2", chat_id, "产品观点...") ``` ## 文件结构 ``` bot-roundtable/ ├── SKILL.md # 本文件 ├── feishu_sender.py # 飞书多Bot发消息模块 ├── coordinator.py # 协调脚本 ├── config.json # Bot凭证配置 └── README.md # 详细文档 ``` ## 配置 config.json: ```json { "bots": { "default": {"app_id": "...", "app_secret": "..."}, "qiwang": {"app_id": "...", "app_secret": "..."}, "meidou2": {"app_id": "...", "app_secret": "..."} }, "group_chat_id": "oc_xxx" } ``` ## 依赖 - Python 3.8+ - 飞书开放平台应用（需配置 app_id + app_secret） ## 注意事项 1. 每条消息间隔0.5秒，避免频率限制 2. 消息300字以内 3. Bot凭证从 openclaw.json 的 channels.feishu.accounts 读取


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mskrq/document-skill (version 1.0.2)

name: document-skill description: 文档处理技能 - 让 AI 能够读取、解析、提取 PDF、DOCX、PPT 等文档的关键信息，支持文档解析、内容提取、关键信息整合、多格式文档处理、PDF/DOCX/PPT 读取、表格数据提取、图片素材提取、核心观点总结、行业数据整合、竞品分析内容提取、加密文档读取、跨文档内容合并、特定内容筛选、超链接提取、页面标题提取、文档修复、内容翻译、字数统计、错别字检查、目录生成。当用户要求分析文档、提取内容、总结报告时触发此技能。 --- # When to Run 用户需要解析文档、提取 PPT/PDF 内容、处理 WPS 文档时触发 # Workflow 仅用于技能搜索展示，无执行命令 Tags: pdf,docx,ppt,document,document-pro,word,extract,parser,analysis,summary


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