OpenClaw/clawhub-security-signals huggingface dataset:
Skill: rishabhdugar/lock-pdf (version 1.0.0)

--- name: lock-pdf description: "Add password protection and encryption to a PDF. Supports AES-256, AES-128, RC4-128 encryption and granular permissions." --- # Lock PDF ## What It Does Encrypts a PDF with password protection. Supports AES-256 (default), AES-128, and RC4-128 encryption. Allows setting separate user and owner passwords with granular permission controls. ## When to Use - Protect a PDF with a password before sharing - Set permissions (allow print but deny copy/modify) - Re-encrypt an already encrypted PDF with a new password ## Required Inputs - `password` — user password (required to open the PDF) - Plus one of: `url`, `base64_pdf`, or multipart `file` upload ## Authentication Send your API key in the `CLIENT-API-KEY` header. Get your **free API key** at [https://pdfapihub.com](https://pdfapihub.com). Full API documentation is available at [https://pdfapihub.com/docs](https://pdfapihub.com/docs). ## Use Cases - **Confidential Document Protection** — Encrypt contracts, NDAs, or financial reports before sharing - **Read-Only Distribution** — Allow viewing and printing but prevent copying or editing - **Client Deliverables** — Password-protect reports or proposals sent to clients - **Compliance** — Encrypt sensitive PDFs (HIPAA, GDPR) before storage or transmission - **Exam Papers** — Lock exam PDFs with a password released only at exam time - **IP Protection** — Restrict copying from proprietary documents while allowing viewing ## Permissions | Permission | Description | |-----------|-------------| | `print` | Allow printing | | `print_highres` | Allow high-res printing | | `copy` | Allow copying text | | `modify` | Allow modifying content | | `annotate` | Allow adding annotations | | `fill_forms` | Allow filling form fields | | `extract` | Allow extracting content | | `assemble` | Allow assembling pages | ## Example Usage ```bash curl -X POST https://pdfapihub.com/api/v1/lockPdf \ -H "CLIENT-[REDACTED_SECRET]" \ -H "Content-Type: application/json" \ -d '{ "url": "https://pdfapihub.com/sample.pdf", "password": "viewerPass", "owner_password": "adminPass", "permissions": { "print": true, "copy": false, "modify": false }, "output": "url" }' ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: erongcao/ai-model-team (version 2.9.2)

--- name: ai-model-team category: trading model: prediction description: AI Model Team - Multi-model prediction system (Kronos + Chronos-2 + TimesFM + VADER) for OKX crypto and US stocks. Features error handling, retry logic, HTTP caching, unit tests, and correct data source routing (OKX for crypto, Yahoo Finance for stocks). version: 2.9.0 --- # AI Model Team Multi-model collaborative prediction system: Kronos + Chronos-2 + TimesFM + VADER FinBERT ## Models - **Kronos-base**: K-line specialist (NeoQuasar) - **Chronos-2**: Macro cycle prediction (Amazon) - **TimesFM-2.5-200M**: General time series (Google) - **VADER FinBERT**: Financial sentiment (<1s load) ## Supported Assets | Type | Examples | Data Source | |------|----------|-------------| | Crypto | BTC, ETH, SOL | OKX API | | US Stocks | NVDA, AAPL, MSFT | Yahoo Finance | ## Usage ```bash # 4-model analysis python scripts/model_team.py BTC-USDT-SWAP --models kronos,chronos-2,timesfm,finbert # Run tests python -m pytest tests/ -v ``` ## Robustness - Full error handling (Kronos, Chronos, TimesFM) - Reddit API retry (3 retries, 429 handling) - HTTP caching (lru_cache, 60s TTL) - Yahoo Finance for US stocks - Unit tests (pytest, 9 tests)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: adarshvmore/telegram-marketing-audit-adarsh (version 1.0.0)

# Telegram Marketing Audit Command Handler Skill ## Purpose Handles the Telegram `/marketing_audit` command by triggering the Marketing Orchestrator skill with given input and replying with the final report. ## Telegram Command - Command: `/marketing_audit` - Args: `instagramHandle` (optional), `websiteDomain` (optional) ## Implementation ```javascript module.exports = async function marketingAuditHandler(context) { const { instagramHandle, websiteDomain } = context.args; if (!instagramHandle && !websiteDomain) { await context.reply("Please provide an Instagram handle or website domain (or both)."); return; } await context.reply("Starting marketing audit. This may take a few minutes..."); try { const result = await context.callSkill("marketing-orchestrator", { instagramHandle, websiteDomain, }); if (result && result.reportMarkdown) { await context.reply(result.reportMarkdown); } else { await context.reply("Audit completed but no report was generated."); } } catch (err) { await context.reply("Error during marketing audit: " + err.message); } }; ``` ## Notes - Add this skill folder to OpenClaw skills directory. - Register a Telegram slash command `/marketing_audit` that uses this skill as the handler via OpenClaw config or ClawHub. - Ensure environment variables for collectors (API keys) are set.


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

--- name: rxtool description: Test, extract, replace, split, and explain regular expressions. Use when asked to test a regex pattern, extract matches from text, do regex replacements, split text by pattern, or understand what a regex does. Supports named groups, case-insensitive/multiline/dotall flags, and JSON output. Works with stdin pipes or inline text. Zero dependencies. --- # rxtool 🔤 Regex Swiss army knife — test, extract, replace, split, explain. ## Commands ```bash # Test a pattern against text python3 scripts/rxtool.py test '(\d+)-(\d+)' "order-123-456" # Extract all matches python3 scripts/rxtool.py extract '\b\w+@\w+\.\w+\b' "email me at [REDACTED_SECRET]" # Extract with JSON output echo "text" | python3 scripts/rxtool.py extract '\w+' --json # Replace matches python3 scripts/rxtool.py replace '\d+' 'NUM' "got 42 items and 7 boxes" # Split by pattern python3 scripts/rxtool.py split '[,;\s]+' "a, b; c d" # Explain a regex python3 scripts/rxtool.py explain '(?P<year>\d{4})-(?P<month>\d{2})' ``` ## Features - Group capture (numbered and named) - Case-insensitive (-i), multiline (-m), dotall (-s) flags - Stdin pipe support - JSON output for extraction - Regex explanation with component breakdown


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 1999azzar/stability-ai (version 1.0.0)

--- name: stability-ai description: Generate high-quality images via Stability AI API (SDXL, SD3, Stable Image Core). Use when user asks to "generate image", "make a picture", or "draw this". --- # Stability AI Skill ## Setup 1. Copy `.env.example` to `.env`. 2. Set `STABILITY_API_KEY` in `.env`. 3. Optional: Set `API_HOST` if using a custom endpoint. ## Usage - **Role**: Digital Artist. - **Trigger**: "Draw a cat", "Generate cyberpunk city", "Create an image". - **Output**: Local file path to the generated image and JSON metadata. ## Commands (The script automatically handles dependencies on first run) ```bash # Basic generation scripts/generate "A futuristic city with neon lights" # Aspect ratio (1:1, 16:9, 9:16, 21:9, 4:3, 3:4) scripts/generate "Landscape painting" --ratio 16:9 # Style preset scripts/generate "Portrait of a warrior" --style photographic # Seed for reproducibility scripts/generate "Abstract art" --seed 42 # Negative prompt scripts/generate "Beautiful sunset" --negative "blurry, low quality" # Output format (png, jpg, webp) scripts/generate "Nature scene" --format webp # Advanced: Custom model, steps, CFG scale scripts/generate "Fantasy landscape" \ --model stable-diffusion-3-medium \ --steps 50 \ --cfg 7.0 \ --ratio 21:9 # V2 API (experimental) scripts/generate "Modern architecture" --v2 # Combined options scripts/generate "Cyberpunk street at night" \ --ratio 16:9 \ --style neon-punk \ --seed 123 \ --format jpg \ --steps 45 \ --cfg 6.5 ``` ## Features ### Style Presets Available styles: enhance, anime, photographic, digital-art, comic-book, fantasy-art, line-art, analog-film, neon-punk, isometric, low-poly, origami, modeling-compound, cinematic, 3d-model, pixel-art, tile-texture ### Aspect Ratios Supported: 1:1 (default), 16:9, 9:16, 21:9, 4:3, 3:4 ### Output Formats - PNG (default, lossless) - JPEG/JPG (lossy, smaller size) - WebP (modern, efficient) ### Metadata Each generated image includes a JSON metadata file with: - Prompt and negative prompt - Model, parameters, and settings - Generation timestamp - API version used ## Models - Default: SDXL 1.0 (`stable-diffusion-xl-1024-v1-0`) - See `references/models.md` for complete model list, API versions, and credit costs. ## Auto-Cleanup Automatically keeps the last 20 generated images. Older files and their metadata are removed to save disk space.

path: requirements.txt
content: requests
python-dotenv
pillow


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ipythoning/sdr-supermemory (version 1.2.0)

# supermemory — AI Memory Engine Semantic memory layer powered by vector search. Store, recall, and connect conversation insights across all customer interactions. ## Architecture ``` Conversation → Extract Insights → Embed → Store (Vector DB) ↓ Query → Semantic Search → Relevant Memories → Inject into Context ``` ## Memory Types | Type | TTL | Example | |------|-----|---------| | Customer Fact | Permanent | "Ahmed from Dubai, buys 50 units/quarter" | | Conversation Insight | 90 days | "Interested in bulk pricing for Model X" | | Market Signal | 30 days | "East Africa demand spike for product Y" | | Effective Script | Permanent | "Opening with local market data → 3x reply rate" | ## Commands - `memory:add <text>` — Manually add a memory - `memory:search <query>` — Semantic search across all memories - `memory:list [type]` — List recent memories by type - `memory:forget <id>` — Delete a specific memory - `memory:stats` — Memory usage statistics ## Auto-Capture When enabled, the engine automatically extracts and stores: 1. Customer preferences and requirements 2. Price sensitivity signals 3. Competitive mentions 4. Purchase timeline indicators 5. Relationship context (referrals, prior interactions) ## Configuration ```json { "provider": "lancedb", "embedding_model": "{{embedding_model}}", "auto_capture": true, "capture_strategy": "last_turn", "recall_top_k": 5, "ttl_days": { "customer_fact": null, "conversation_insight": 90, "market_signal": 30, "effective_script": null } } ``` ## Integration Works with: - **LanceDB** (local, no external dependency) - **Supermemory Cloud** (hosted, API key required) - **Memos** (self-hosted note-taking)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: benmillerat/supermarket-deals (version 1.1.0)

--- name: supermarket-deals description: Search German supermarket flyers (Aldi, Lidl, REWE, EDEKA, Kaufland) for product deals via Marktguru. Results ranked by best price per litre (EUR/L). No API key needed. --- # supermarket-deals Search German supermarket flyers for product deals via the Marktguru API. Results are ranked by best price per litre. ## What this skill does - Fetches Marktguru API keys automatically from the homepage (no registration needed, keys are cached 6h) - Searches current Prospekte (flyers) by product query + ZIP code - Supports multiple search terms in one call (merged + deduplicated) - Filters by store, ranks by EUR/L - Returns a direct Marktguru link for each deal - The skill is intentionally "dumb" — it fetches and formats data. Your agent applies smart filtering and formatting for notifications. ## Setup ```bash cd path/to/supermarket-deals npm install npm run build ``` Optionally set your defaults: ```bash node dist/index.js config set zip 85540 node dist/index.js config set stores "Lidl,REWE,EDEKA,ALDI SÜD,Kaufland" ``` ## Usage ```bash # Single search term node dist/index.js search "Cola Zero" --zip 85540 # Multiple terms (merged + deduped, useful for product aliases) node dist/index.js search "Cola Zero" "Coke Zero" --zip 85540 # Broad search — let your agent do the filtering node dist/index.js search "Cola" --zip 85540 # Filter by specific stores node dist/index.js search "Monster Energy" --zip 80331 --stores "Lidl,ALDI SÜD" # JSON output for agent/cron use node dist/index.js search "Cola" --zip 85540 --json # Show config node dist/index.js config ``` ## Agent pattern (recommended) Use a broad search term and let your agent filter intelligently: ``` node dist/index.js search "Cola" --zip 85540 --json ``` Then instruct your agent to: - Include deals where description says "versch. Sorten" (these bundle all variants incl. Zero) - Include deals that explicitly mention Coca-Cola, Coke Zero, etc. - Exclude deals that only mention Powerade, Fuze Tea, Sprite-only, etc. - Rank by EUR/L and highlight the best deal This approach catches deals that Marktguru lists as generic "Cola category" without naming every variant. ## Output columns | Column | Description | |--------|-------------| | Description | Product description from flyer | | Store | Retailer name | | Size | Volume × quantity (e.g. `6×0.33l`, `1.5l`) | | Price | Total price | | EUR/L | Price per litre (calculated or from API reference price) | | Valid | Deal validity dates | | URL | Direct link to Marktguru offer page | ## Notes - Prospekte refresh on Mondays and Thursdays - Results are cached by Marktguru for ~15 minutes - Some regional store branches may not submit flyers to Marktguru — broad queries catch more - API keys rotate and are fetched fresh at runtime (cached 6h in `~/.supermarket-deals/keys.json`)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: nexscope/price-optimization-tool (version 1.0.0)

# Price Optimization Tool Optimize product pricing using data-driven methods. Elasticity analysis, competitor benchmarking, margin modeling, and A/B price testing to maximize revenue and profit across e-commerce platforms. ## Capabilities - Price elasticity estimation by category - Competitor price benchmarking and gap analysis - Margin optimization modeling with platform fees - A/B price testing framework and statistical significance - Bundle and tiered pricing optimization - Promotional price planning (coupons, lightning deals) - Cross-marketplace price harmonization - Price monitoring alert setup ## Install ```bash npx skills add nexscope/price-optimization-tool ``` ## Usage Ask your AI agent: - "What is the optimal price for my product given these costs and competitor prices?" - "Set up a price testing plan for my top 5 SKUs." - "Calculate the ideal bundle price for maximum margin." ## Output Structured recommendations with actionable steps, benchmarks, and platform-specific guidance. --- *Built by [Nexscope](https://www.nexscope.ai/) — AI-powered e-commerce tools for sellers worldwide.*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: liuxindavid/cliby-tavily-search (version 1.0.1)

--- name: tavily description: use this when the user asks to search the web, look up recent information, check current events, gather online sources, or research a topic using tavily search. --- # Tavily Search Use this skill for web search and lightweight research through the Tavily Search API. ## Requirements A valid Tavily API key must be available through one of these methods: 1. `--api-key` 2. `TAVILY_API_KEY` 3. `{baseDir}/.secrets/tavily.key` If no key is available, explain that Tavily search is not configured in this environment. ## Command Run: ```bash python3 {baseDir}/scripts/tavily_search.py --query "<user query>"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: rookiedeng33/jma-voice-clone-free (version 1.0.2)

--- name: jiuma-free-voice-clone description: 该skill已废弃，请移步https://clawhub.ai/dddcn1/jiuma-free-voice-clone ---


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhangxiaopeng-zgci/zhongguancun-deal-closer (version 1.0.0)

--- name: deal-closer description: 产学研合作推进与落地决策引擎。当用户提交会议纪要、合作进展、对接记录，要求分析下一步、推进合作、判断成单概率、写跟进话术时触发。核心不是提醒，而是决策：基于会议内容推断对方真实需求、判断合作阶段与概率、给出最优策略动作和可直接使用的跟进话术。适用于钉钉听记/飞书妙记等会议纪要的结构化分析。配合 industry-signal-monitor（发现线索）和 industry-research（深度调研）形成完整链路。 --- # 合作推进决策引擎 把会议纪要变成推进策略。不是 ToDo 列表，而是"下一步该干嘛，怎么更容易成"。 ## 核心产出 1. 会议关键结论提取 2. 合作推进判断 3. 下一步最优动作 4. 跟进话术（可直接用） 5. 风险提示 ## 输入 主要输入：会议纪要（钉钉听记/飞书妙记/手动整理均可） 可选补充： - 当前合作状态（如有历史上下文） - 之前会议的推进记录 ## 工作流程 ### 第一步：关键结论提取 从会议纪要中提取： **1. 对方真实需求** - 显性需求：对方直接说的（要技术、要人、要数据） - 隐性需求：没说但推断的（要背书、要政绩、要品牌、要压价筹码） - 判断方法：看对方问了什么、反复强调什么、对什么点了头 **2. 关键人识别** - 谁拍板（决策者） - 谁推动（内部champion） - 谁观望（可能反对者） - 格式：姓名 | 角色 | 立场倾向 **3. 预算/项目判断** - 是否有明确预算 → YES/NO/待确认 - 是否有具体项目 → YES/NO/待确认 - 时间节点 → 如有 ### 第二步：合作推进判断 **阶段判定**（详见 [references/stages.md](references/stages.md)）： | 阶段 | 特征 | 典型信号 | |------|------|----------| | 了解 | 对方在收集信息 | 问很多问题，不谈具体合作 | | 探索 | 双方在找交叉点 | 提到"可以试试""有没有可能" | | 方案 | 在讨论具体怎么做 | 提到需求、资源、时间表 | | 签约 | 在谈条款 | 提到合同、预算、分工 | **成单概率**： | 判定 | 条件 | |------|------| | 高 | 有明确需求+有预算+有拍板人参与+我方有对口能力 | | 中 | 有需求但预算不清/拍板人未参与/我方匹配度一般 | | 低 | 对方只是了解/无预算/我方无对口能力 | ### 第三步：下一步最优动作（核心） 不是泛泛的"跟进"，而是具体的策略动作： | 动作 | 适用场景 | 说明 | |------|---------|------| | 发方案 | 探索→方案阶段，对方有具体需求 | 针对性方案，不是全量介绍 | | 安排老师对接 | 对方对某方向有深度兴趣 | 让研究者直接聊，比中间人传话有效10倍 | | 推学生/实习生 | 对方缺人手 | 低门槛合作，先有人再谈项目 | | 约第二次会 | 第一次信息不够 | 缩小范围，只叫关键人 | | 送资料/论文 | 对方还在了解阶段 | 不用开会，先让对方消化 | | 暂停观察 | 对方无明确需求或时机不对 | 别追，追了反而掉价 | | 找内部人引荐 | 对方层级不对等 | 通过我方高层推进 | 每个动作必须附带： - 具体做什么（不是"发方案"，而是"发一份AI+教育诊断技术的合作方案，重点回应他们提到的XX问题"） - 谁来做 - 什么时候做（建议时间） ### 第四步：跟进话术 提供两档： **1. 微信/钉钉消息（1-3句话）** - 自然、不硬、有推进感 - 示例："X总，上次聊的XX方向，我们X老师很感兴趣，要不要安排一次小范围沟通？" **2. 邮件（完整版）** - 正式、有结构、有附件 - 详见 [references/email-templates.md](references/email-templates.md)) ### 第五步：风险提示 识别常见合作陷阱： | 风险类型 | 信号 | 建议 | |----------|------|------| | 只了解一下 | 对方不问细节、不提预算、不约下次 | 降级为"保持联系"，别投入资源 | | 资源错配 | 对方要的我方没有，我方给的对对方不痛 | 重新找交叉点或放弃 | | 已有竞品合作 | 对方提到其他合作伙伴 | 差异化切入，不做替代品 | | 内部博弈 | 对方不同人说法不一 | 找到champion，跟对人 | | 拖延症 | 总说"下次再聊"但没下次 | 设deadline，推具体事项 | ## 与其他 skill 联动 ``` 🔍 industry-signal-monitor → 发现线索 🧪 industry-research → 深度调研+议程 🎯 deal-closer → 会议后推进决策 ``` 完整链路：发现 → 调研 → 接触 → **推进落地**


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: newageinvestments25-byte/filament-vault (version 1.0.0)

--- name: filament-vault description: "Track 3D printing filament inventory locally. Add spools, log usage, check stock levels, and generate spending reports. Use when: filament, spool, 3d print inventory, filament stock, what filament do I have, printing materials, filament vault, filament inventory, low stock filament." --- # filament-vault Local filament inventory tracker. All data stored in JSON at `~/.openclaw/workspace/filament-vault/inventory.json`. ## Scripts All scripts live in `scripts/`. Run with `python3 scripts/<script>.py [args]`. ## Add a Spool ``` python3 scripts/add_spool.py --brand "Bambu" --material PLA --color "Matte Black" --weight 1000 --cost 19.99 --location "Shelf A" ``` Options: `--brand`, `--material` (PLA/PETG/ABS/TPU/Nylon/ASA/other), `--color`, `--weight` (grams, default 1000), `--cost` (USD), `--location`, `--notes` ## Update / Use a Spool ``` python3 scripts/update_spool.py --search "matte black" --used 150 python3 scripts/update_spool.py --id <uuid> --finished --notes "Ran out mid-print" ``` Options: `--id` (UUID), `--search` (partial match), `--used` (grams consumed), `--finished` (mark empty), `--notes` ## List Inventory ``` python3 scripts/inventory.py python3 scripts/inventory.py --material PLA --low-stock python3 scripts/inventory.py --json ``` Filters: `--material`, `--color`, `--brand`, `--low-stock` (under threshold, default 100g), `--threshold <g>`, `--json` ## Generate Report ``` python3 scripts/report.py python3 scripts/report.py --threshold 200 ``` Shows: total spools, total weight, total value, by-material breakdown, low-stock alerts, monthly spending. ## Search ``` python3 scripts/search.py "bambu" python3 scripts/search.py "black petg" --json ``` Fuzzy search across all fields (brand, color, material, location, notes). ## References See `references/materials.md` for filament material properties and guidance.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mr-j-j/siliconflow-api (version 1.0.0)

--- name: siliconflow-api description: SiliconFlow（硅基流动）AI 生成能力集成。支持文生图、图生图、文生视频、图生视频、文字转语音。当用户需要生成图片、视频、语音时使用此技能。首次使用需运行 setup 配置自己的 API Key（在 siliconflow.cn 注册获取）。国内支付方式（微信/支付宝），无需海外银行卡。 --- # SiliconFlow API 硅基流动 AI 生成平台，支持多种生成模型，国内直接使用。 ## 配置（首次使用必须配置） 1. 注册 [siliconflow.cn](https://siliconflow.cn) 获取 API Key 2. 运行配置命令输入 Key： ```bash bash scripts/sf_api.sh setup ``` ⚠️ API Key 保存在本地 `.sf-config.json`，不会泄露到技能包中。 ## 可用命令 | 功能 | 命令 | 说明 | |------|------|------| | 文生图 | `t2i <提示词> [尺寸] [模型] [输出路径]` | 默认1024x1024，模型Kwai-Kolors/Kolors | | 图生图 | `i2i <图片路径> <提示词> [输出路径]` | 编辑已有图片 | | 文生视频 | `t2v <提示词> [时长] [输出路径]` | 默认5秒，Wan2.2模型 | | 图生视频 | `i2v <图片路径> <提示词> [时长] [输出路径]` | 上传图片生成视频 | | 查询视频 | `vs <任务ID> [输出路径]` | 异步任务状态查询 | | 文字转语音 | `tts <文本> [音色] [输出路径]` | CosyVoice2 | | 列出模型 | `models` | 查看可用模型列表 | ## 快速开始 脚本在 `scripts/sf_api.sh`，所有命令通过它执行。 ```bash cd ~/.openclaw/workspace/skills/siliconflow-api # 文生图 bash scripts/sf_api.sh t2i "一个穿深蓝色西装的老板坐在办公室，自信微笑，4K写实" # 图生图（用老板照片生成成熟版） bash scripts/sf_api.sh i2i /root/.openclaw/workspace/assets/laoban-photo.jpg "成熟稳重企业家风格，深蓝色西装，办公室背景" # 文生视频 bash scripts/sf_api.sh t2v "一个穿西装的商人在办公室微笑" 5 # 图生视频（用老板照片生成视频） bash scripts/sf_api.sh i2v /root/.openclaw/workspace/assets/laoban-photo.jpg "微笑看向镜头，轻轻点头" 5 # 语音生成 bash scripts/sf_api.sh tts "你好，我是做软件开发的，这是我们公司的产品" ``` ## 图片生成 ### 推荐模型 - `Kwai-Kolors/Kolors`（默认）— 中文理解好，写实风格强 - `Qwen/Qwen-Image` — 通义千问，质量高 ### 常用尺寸 - `1024x1024`（默认方图） - `1216x832`（横图，适合16:9） - `832x1216`（竖图，适合9:16手机） ### 提示词技巧 - 中文提示词效果更好 - 加 `4K`, `写实`, `电影级` 等词提升画质 - 加 `浅景深`, `柔和自然光` 等摄影术语 ## 视频生成 ### 模型 - `Wan-AI/Wan2.2-T2V-A14B` — 文生视频 - `Wan-AI/Wan2.2-I2V-A14B` — 图生视频（上传参考图） ### 注意 - 视频生成是异步任务，提交后需轮询等待 - 生成时间约2-10分钟（排队+推理） - 视频最长15秒 - 输出格式为 MP4 ## 语音合成 ### 模型 - `FunAudioLLM/CosyVoice2-0.5B` — 中文语音合成 ### 音色选项 - 中文男声 - 中文女声 ## 注意事项 - 所有生成都会消耗账户余额 - 图片生成约0.1-0.3元/次 - 视频生成较贵，约0.5-2元/次 - 语音合成约0.01元/次 - 生成结果保存在 `/tmp/` 目录，用完后清理


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