OpenClaw/clawhub-security-signals huggingface dataset:
Skill: shawnshenopeninterx/influencer-report (version 2.0.0)

--- name: influencer-report description: Generate a comprehensive influencer vetting report. Use when someone asks to vet, analyze, or review an influencer/creator. Takes a creator profile URL or video URLs and produces a brand safety and content quality report powered by Memories.ai video intelligence. --- # Influencer Report Skill Vet influencers by analyzing their recent videos with Memories.ai V1 + V2 APIs. ## Setup Required environment variables: - `MEMORIES_V1_API_KEY` — Memories.ai V1 API key (scraper, library, search) - `MEMORIES_API_KEY` — Memories.ai V2 API key (MAI transcript, metadata) ## Quick Start **From a profile URL (auto-scrapes recent videos):** ```bash python3 scripts/influencer_report.py --handle charlidamelio \ --profile-url "https://www.tiktok.com/@charlidamelio" --scrape-count 5 ``` **From direct video URLs:** ```bash python3 scripts/influencer_report.py --handle creator_name --platform tiktok \ --videos https://tiktok.com/@user/video/1 https://tiktok.com/@user/video/2 ``` ## Workflow 1. **Scrape** — V1 `/scraper` ingests creator's recent videos from their profile URL 2. **List & Search** — V1 `/list_videos` and `/search` retrieve the ingested content 3. **Analyze** — V2 MAI Transcript API provides visual + audio analysis per video 4. **Metadata** — V2 Metadata API pulls engagement stats (views, likes, comments) 5. **Score & Report** — Content quality scoring + formatted markdown report ## API Endpoints Used | Step | API | Endpoint | |------|-----|----------| | Scrape profile | V1 | `POST /serve/api/v1/scraper` | | List library | V1 | `POST /serve/api/v1/list_videos` | | Search library | V1 | `POST /serve/api/v1/search` | | MAI transcript | V2 | `POST /serve/api/v2/{platform}/video/mai/transcript` | | Video metadata | V2 | `POST /serve/api/v2/{platform}/video/metadata` | ## Report Format See `references/report-format.md` for the full template and scoring guide.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: rmorse/ghapp (version 0.1.5)

--- name: ghapp description: Give your AI agents and automations their own GitHub (App) identity. Authenticate using GitHub Apps so every commit, PR, and action is attributed to the bot — not your personal account. homepage: https://github.com/operator-kit/ghapp-cli metadata: {"clawdbot":{"emoji":"🔑","requires":{"bins":["ghapp"]},"install":[{"id":"brew","kind":"brew","formula":"operator-kit/tap/ghapp","bins":["ghapp"],"label":"Install ghapp (brew)"}]}} --- # ghapp Use `ghapp` to authenticate as a GitHub App so `git` and `gh` commands use installation tokens. Requires a GitHub App with App ID, Installation ID, and a private key (.pem). Setup - `ghapp setup` — interactive wizard: enter App ID, Installation ID, key path, then configure auth - `ghapp auth configure` — configure git + gh authentication (if skipped during setup) - `ghapp auth status` — show current auth config and diagnostics Commands - `ghapp --help` — list all commands and flags - `ghapp token` — print an installation token (cached; `--no-cache` for fresh) - `ghapp auth configure [--gh-auth shell-function|path-shim|none]` — configure how git/gh authenticate - `ghapp auth status` — check auth health - `ghapp auth reset [--remove-key]` — undo all auth config - `ghapp config set`, `ghapp config get [key]`, `ghapp config path` — manage config - `ghapp update` — self-update to latest release - `ghapp version` — print version gh auth modes (passed to `auth configure`) - `shell-function` — auto-authenticates gh commands via shell integration (recommended) - `path-shim` — wrapper binary for CI/containers - `none` — static token in hosts.yml Notes - After setup, `git clone/push/pull` and `gh` work without manual tokens. - Commits are attributed to the app's bot account (e.g., `myapp[bot]`). - Tokens are cached locally and auto-refreshed. - Config stored at `~/.config/ghapp/config.yaml`.


Security scan verdicts — ClawScan: clean | static analysis:  | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: codenova58/tiktok-minis (version 0.1.0)

--- name: youku-shortdrama-hot-trend description: 注册“优酷短剧”技能；用于短剧频道与剧集页信息整理与表现摘要。 homepage: `https://www.youku.com/` metadata: {"clawdbot":{"emoji":"🎬"}} --- # 优酷短剧 用途与边界 - 面向公开短剧频道与剧集页面的检索与表现数据摘要 - 不提供下载、接口逆向或突破风控能力 - 仅用于公开页面的轻量分析与提醒 关键入口 - 主页：https://www.youku.com/ - 短剧频道/排行榜：站点入口 - 搜索入口：https://so.youku.com/ 常见任务 - 短剧剧集的目录与更新状态、期次简介 - 榜单条目分布与题材统计 - 热度/评论与主演信息摘要 数据字段 - 短剧标题、更新状态、期次目录、简介、主演、评分/评论、链接 - 榜单名称、采集时间、来源链接 自动化要点 - 动态渲染与人机校验，建议人工打开后触发解析 - 请求频率需控制，不进行批量抓取 - 不进行下载或接口调用 示例流程 - 剧集目录：访问短剧页 → 抽取期次 → 输出更新状态与简介 - 榜单统计：访问排行榜 → 抽取条目 → 题材分布统计 - 信息摘要：抽取主演与评论概览 → 输出要点 合规提示 - 遵守平台规则，不进行下载、接口调用或批量采集 - 结果仅用于内部分析与提醒


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jopyin/process-flow-navigator (version 1.0.0)

--- name: process-flow-navigator description: 业务流程图导航助手 - 帮助你在复杂的多分支流程中导航、规划路径、查询技能编码。支持 A→K 主流程及所有子流程分支。 metadata: { "openclaw": { "requires": { "bins": [] }, "install": [], } } instant: true author: Instant company: Instant --- # Process Flow Navigator - 业务流程导航助手 ## 功能 - 🧭 **流程导航** - 告诉你在哪个节点，帮你规划下一步 - 🗺️ **路径规划** - 从任意节点到任意节点的多路径规划 - 🏷️ **技能编码查询** - 快速查找各节点的校验/执行/判定技能编码 - 📊 **流程可视化** - 输出当前分支的结构图 ## 支持的流程节点 | 节点 | 校验技能 | 执行技能 | 判定技能 | |------|---------|---------|---------| | A | PROC-A-CHECK-001 | PROC-A-EXEC-001 | PROC-A-B-JUDGE-001 | | B | PROC-B-CHECK-002 | PROC-B-EXEC-002 | PROC-B-C-JUDGE-002 | | C | PROC-C-CHECK-003 | PROC-C-EXEC-003 | - | | D | PROC-D-CHECK-004 | PROC-D-EXEC-004 | PROC-D-E-JUDGE-004 | | E | PROC-E-CHECK-005 | PROC-E-EXEC-005 | PROC-E-F-JUDGE-005 | | F | PROC-F-CHECK-006 | PROC-F-EXEC-006 | PROC-F-G-JUDGE-006 | | G | PROC-G-CHECK-007 | PROC-G-EXEC-007 | PROC-G-H-JUDGE-007 | | H | PROC-H-CHECK-008 | PROC-H-EXEC-008 | PROC-H-I-JUDGE-008 | | I | PROC-I-CHECK-009 | PROC-I-EXEC-009 | PROC-I-J-JUDGE-009 | | J | PROC-J-CHECK-010 | PROC-J-EXEC-010 | PROC-J-K-JUDGE-010 | | K | PROC-K-CHECK-011 | PROC-K-EXEC-011 | PROC-K-END-001 | ## 使用示例 ### 导航到下一步 ``` 我在流程 B，下一步怎么走？ ``` ### 路径规划 ``` 从流程 B 到结束，有几种路线？ ``` ### 查询技能编码 ``` 流程 C 的技能编码是什么？ ``` ### 查看分支结构 ``` 显示 C 分支的完整流程 ``` ## 流程终点 | 终点 | 到达条件 | |------|---------| | **结束** | 最终判断 [是]、判断 9 [是] | | **售后** | 判断 7 [乱码]、判断 8 [否]、判断 9 [否] | ## 核心规则 - **最终判断 [是]** → 结束 - **最终判断 [否]** → 继续流转 ## 数据来源 本 skill 基于用户提供的业务流程图.pdf 创建，包含完整的节点连接关系和技能编码映射。 ## 更新日志 - **v1.0.0** - 初始版本，包含完整 A-K 流程导航能力 --- ## ©️ 版权 **Instant** © 2026 - 保留所有权利


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: aidfsoft/product-test-skill (version 1.0.0)

# Data Analyzer Skill ## Description A data analysis skill that processes CSV files and generates summary statistics reports. ## Version 1.0.0 ## Input - `file_path` (string, required): Path to the CSV file to analyze - `columns` (list, optional): Specific columns to include in the analysis ## Output - `summary` (object): Statistical summary including mean, median, std, min, max for each numeric column - `row_count` (integer): Total number of rows processed ## Permissions - File read access (local workspace only) - No network access required ## Usage Example ``` Input: {"file_path": "data/sales.csv", "columns": ["revenue", "quantity"]} Output: {"summary": {"revenue": {"mean": 150.5, "median": 120.0}}, "row_count": 1000} ``` ## Dependencies - pandas >= 2.0.0 - numpy >= 1.24.0


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: msarheed/uptime-kuma (version 1.0.0)

--- name: uptime-kuma description: Interact with Uptime Kuma monitoring server. Use for checking monitor status, adding/removing monitors, pausing/resuming checks, viewing heartbeat history. Triggers on mentions of Uptime Kuma, server monitoring, uptime checks, or service health monitoring. --- # Uptime Kuma Skill Manage Uptime Kuma monitors via CLI wrapper around the Socket.IO API. ## Setup Requires `uptime-kuma-api` Python package: ```bash pip install uptime-kuma-api ``` Environment variables (set in shell or Clawdbot config): - `UPTIME_KUMA_URL` - Server URL (e.g., `http://localhost:3001`) - `UPTIME_KUMA_USERNAME` - Login username - `UPTIME_KUMA_PASSWORD` - Login password ## Usage Script location: `scripts/kuma.py` ### Commands ```bash # Overall status summary python scripts/kuma.py status # List all monitors python scripts/kuma.py list python scripts/kuma.py list --json # Get monitor details python scripts/kuma.py get <id> # Add monitors python scripts/kuma.py add --name "My Site" --type http --url https://example.com python scripts/kuma.py add --name "Server Ping" --type ping --hostname 192.168.1.1 python scripts/kuma.py add --name "SSH Port" --type port --hostname server.local --port 22 # Pause/resume monitors python scripts/kuma.py pause <id> python scripts/kuma.py resume <id> # Delete monitor python scripts/kuma.py delete <id> # View heartbeat history python scripts/kuma.py heartbeats <id> --hours 24 # List notification channels python scripts/kuma.py notifications ``` ### Monitor Types - `http` - HTTP/HTTPS endpoint - `ping` - ICMP ping - `port` - TCP port check - `keyword` - HTTP + keyword search - `dns` - DNS resolution - `docker` - Docker container - `push` - Push-based (passive) - `mysql`, `postgres`, `mongodb`, `redis` - Database checks - `mqtt` - MQTT broker - `group` - Monitor group ### Common Workflows **Check what's down:** ```bash python scripts/kuma.py status python scripts/kuma.py list # Look for 🔴 ``` **Add HTTP monitor with 30s interval:** ```bash python scripts/kuma.py add --name "API Health" --type http --url https://api.example.com/health --interval 30 ``` **Maintenance mode (pause all):** ```bash for id in $(python scripts/kuma.py list --json | jq -r '.[].id'); do python scripts/kuma.py pause $id done ```


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: mtsatryan/ah-api-designer (version 1.0.0)

--- name: api-designer description: 'You are an API design specialist with expertise in RESTful services, GraphQL, OpenAPI/Swagger specifications, and API-first development. Use when: restful api design and best practices, graphql schema design and optimization, openapi/swagger specification, api versioning and evolution, authentication and authorization patterns.' --- # Api Designer You are an API design specialist with expertise in RESTful services, GraphQL, OpenAPI/Swagger specifications, and API-first development methodologies. ## Core Expertise - RESTful API design and best practices - GraphQL schema design and optimization - OpenAPI/Swagger specification - API versioning and evolution - Authentication and authorization patterns - Rate limiting and throttling - API documentation and testing - Microservices architecture ## Technical Stack - **Specification**: OpenAPI 3.1, Swagger 2.0, AsyncAPI, GraphQL SDL - **Design Tools**: Stoplight Studio, Postman, Insomnia, SwaggerHub - **Documentation**: Redoc, Swagger UI, GraphQL Playground, Slate - **Testing**: Postman, Newman, Dredd, Pact, REST Assured - **Gateways**: Kong, Apigee, AWS API Gateway, Azure API Management - **Protocols**: REST, GraphQL, gRPC, WebSocket, Server-Sent Events - **Standards**: JSON:API, HAL, JSON-LD, OData ## API Design Framework > 📎 **Code example 1** (typescript) — see [references/examples.md](references/examples.md) ## Best Practices 1. **RESTful Principles**: Follow REST architectural constraints 2. **Consistent Naming**: Use consistent naming conventions 3. **Versioning Strategy**: Plan for API evolution 4. **Error Handling**: Provide clear, actionable error messages 5. **Documentation**: Comprehensive, up-to-date documentation 6. **Security First**: Design with security in mind 7. **Performance**: Consider caching and pagination ## API Design Principles - Resource-based URLs (nouns, not verbs) - Use HTTP methods appropriately - Stateless communication - HATEOAS when applicable - Standard status codes - Content negotiation - Idempotent operations ## Approach - Understand business requirements - Design resource model - Define operations and endpoints - Create data schemas - Design authentication/authorization - Document comprehensively - Generate client SDKs ## Output Format - Provide complete API specifications - Include OpenAPI/Swagger documentation - Generate client SDK code - Add testing strategies - Include security considerations - Provide migration guides --- ## Reference Materials For detailed code examples and implementation patterns, see [references/examples.md](references/examples.md).


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhangifonly/vllm (version 1.0.0)

--- name: "vLLM" version: "1.0.0" description: "vLLM 推理引擎助手，精通高性能 LLM 部署、PagedAttention、OpenAI 兼容 API" tags: ["ai", "inference", "vllm", "deployment"] author: "ClawSkills Team" category: "ai" --- # vLLM 高性能推理引擎助手 你是 vLLM 部署和优化领域的专家，帮助用户高效部署和运行大语言模型。 ## 核心优势 | 特性 | 说明 | |------|------| | PagedAttention | 类似操作系统虚拟内存的 KV Cache 管理，显存利用率提升 2-4 倍 | | 连续批处理 | Continuous Batching，动态合并请求，吞吐量远超静态批处理 | | 高吞吐 | 相比 HuggingFace Transformers 推理速度提升 14-24 倍 | | Prefix Caching | 自动缓存公共前缀，多轮对话和共享系统提示词场景加速明显 | | 投机解码 | Speculative Decoding，用小模型加速大模型生成 | ## 安装部署 ```bash pip install vllm # 需要 CUDA 12.1+ # Docker 部署（推荐生产环境） docker run --runtime nvidia --gpus all \ -v ~/.cache/huggingface:/root/.cache/huggingface \ -p 8000:8000 vllm/vllm-openai:latest \ --model meta-llama/Llama-3.1-8B-Instruct ``` ## OpenAI 兼容 API 服务器 ```bash # 基础启动 vllm serve meta-llama/Llama-3.1-8B-Instruct --port 8000 # 生产环境推荐配置 vllm serve Qwen/Qwen2.5-72B-Instruct \ --tensor-parallel-size 4 \ --max-model-len 32768 \ --gpu-memory-utilization 0.9 \ --enable-prefix-caching \ --max-num-seqs 256 --port 8000 ``` ## 支持的主流模型 | 模型系列 | 代表模型 | 参数量 | |----------|----------|--------| | Llama 3.1 | meta-llama/Llama-3.1-8B-Instruct | 8B/70B/405B | | Qwen 2.5 | Qwen/Qwen2.5-7B-Instruct | 0.5B-72B | | DeepSeek V3 | deepseek-ai/DeepSeek-V3 | 671B (MoE) | | Mistral | mistralai/Mistral-7B-Instruct-v0.3 | 7B | | ChatGLM | THUDM/glm-4-9b-chat | 9B | | Gemma 2 | google/gemma-2-27b-it | 2B/9B/27B | ## 关键参数详解 | 参数 | 默认值 | 说明 | |------|--------|------| | `--tensor-parallel-size` | 1 | 张量并行 GPU 数，多卡必设 | | `--max-model-len` | 模型默认 | 最大上下文长度，降低可省显存 | | `--gpu-memory-utilization` | 0.9 | GPU 显存使用比例，0.0-1.0 | | `--max-num-seqs` | 256 | 最大并发序列数 | | `--dtype` | auto | 数据类型：auto/half/float16/bfloat16 | | `--quantization` | None | 量化方式：awq/gptq/fp8/squeezellm | | `--enable-prefix-caching` | False | 启用前缀缓存，多轮对话推荐开启 | ## 量化支持 | 量化方式 | 精度损失 | 显存节省 | 说明 | |----------|----------|----------|------| | FP16/BF16 | 无 | 基准 | 默认精度 | | AWQ | 极小 | ~50% | 推荐，4bit 量化，需预量化模型 | | GPTQ | 小 | ~50% | 经典方案，社区模型多 | | FP8 | 极小 | ~50% | H100/L40S 原生支持，推荐新硬件 | ```bash vllm serve TheBloke/Llama-2-70B-Chat-AWQ --quantization awq ``` ## 与同类工具对比 | 特性 | vLLM | Ollama | TGI | llama.cpp | |------|------|--------|-----|-----------| | 定位 | 生产级高吞吐推理 | 本地便捷运行 | HuggingFace 官方 | CPU/边缘推理 | | 吞吐量 | 极高 | 中等 | 高 | 低-中 | | 多卡支持 | 原生 TP/PP | 不支持 | 支持 | 有限 | | 量化 | AWQ/GPTQ/FP8 | GGUF | AWQ/GPTQ/BnB | GGUF 专精 | | 适用场景 | 服务端大规模部署 | 个人本地使用 | HF 生态集成 | 低资源设备 | ## 常见问题排查 - OOM 错误：降低 `--max-model-len` 或 `--gpu-memory-utilization` - 模型加载慢：使用 `--load-format safetensors`，确保本地有缓存 - 多卡不均衡：检查 `CUDA_VISIBLE_DEVICES` 和 NVLink 拓扑 - 输出乱码：确认模型和 tokenizer 版本匹配，检查 chat template


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jmagar/prowlarr (version 1.0.0)

--- name: prowlarr version: 1.0.0 description: Search indexers and manage Prowlarr. Use when the user asks to "search for a torrent", "search indexers", "find a release", "check indexer status", "list indexers", "prowlarr search", "sync indexers", or mentions Prowlarr/indexer management. --- # Prowlarr Skill Search across all your indexers and manage Prowlarr via API. ## Setup Config: `~/.clawdbot/credentials/prowlarr/config.json` ```json { "url": "https://prowlarr.example.com", "apiKey": "your-api-key" } ``` Get your API key from: Prowlarr → Settings → General → Security → API Key --- ## Quick Reference ### Search Releases ```bash # Basic search across all indexers ./scripts/prowlarr-api.sh search "ubuntu 22.04" # Search torrents only ./scripts/prowlarr-api.sh search "ubuntu" --torrents # Search usenet only ./scripts/prowlarr-api.sh search "ubuntu" --usenet # Search specific categories (2000=Movies, 5000=TV, 3000=Audio, 7000=Books) ./scripts/prowlarr-api.sh search "inception" --category 2000 # TV search with TVDB ID ./scripts/prowlarr-api.sh tv-search --tvdb 71663 --season 1 --episode 1 # Movie search with IMDB ID ./scripts/prowlarr-api.sh movie-search --imdb tt0111161 ``` ### List Indexers ```bash # All indexers ./scripts/prowlarr-api.sh indexers # With status details ./scripts/prowlarr-api.sh indexers --verbose ``` ### Indexer Health & Stats ```bash # Usage stats per indexer ./scripts/prowlarr-api.sh stats # Test all indexers ./scripts/prowlarr-api.sh test-all # Test specific indexer ./scripts/prowlarr-api.sh test <indexer-id> ``` ### Indexer Management ```bash # Enable/disable an indexer ./scripts/prowlarr-api.sh enable <indexer-id> ./scripts/prowlarr-api.sh disable <indexer-id> # Delete an indexer ./scripts/prowlarr-api.sh delete <indexer-id> ``` ### App Sync ```bash # Sync indexers to Sonarr/Radarr/etc ./scripts/prowlarr-api.sh sync # List connected apps ./scripts/prowlarr-api.sh apps ``` ### System ```bash # System status ./scripts/prowlarr-api.sh status # Health check ./scripts/prowlarr-api.sh health ``` --- ## Search Categories | ID | Category | |----|----------| | 2000 | Movies | | 5000 | TV | | 3000 | Audio | | 7000 | Books | | 1000 | Console | | 4000 | PC | | 6000 | XXX | Sub-categories: 2010 (Movies/Foreign), 2020 (Movies/Other), 2030 (Movies/SD), 2040 (Movies/HD), 2045 (Movies/UHD), 2050 (Movies/BluRay), 2060 (Movies/3D), 5010 (TV/WEB-DL), 5020 (TV/Foreign), 5030 (TV/SD), 5040 (TV/HD), 5045 (TV/UHD), etc. --- ## Common Use Cases **"Search for the latest Ubuntu ISO"** ```bash ./scripts/prowlarr-api.sh search "ubuntu 24.04" ``` **"Find Game of Thrones S01E01"** ```bash ./scripts/prowlarr-api.sh tv-search --tvdb 121361 --season 1 --episode 1 ``` **"Search for Inception in 4K"** ```bash ./scripts/prowlarr-api.sh search "inception 2160p" --category 2045 ``` **"Check if my indexers are healthy"** ```bash ./scripts/prowlarr-api.sh stats ./scripts/prowlarr-api.sh test-all ``` **"Push indexer changes to Sonarr/Radarr"** ```bash ./scripts/prowlarr-api.sh sync ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: 1204tmax/riji (version 1.0.0)

--- name: diary description: 个人日记自动化 skill。用于按天生成日记文本并导出 1080px 图片；支持首次自动初始化、读取 SOUL/MEMORY/每日记忆素材、保持写作风格连续性。适用于用户要求“写日记/生成日记图片/补昨天日记/自动日记归档”等场景。 --- # 日记（Diary）Skill ## 目标 在需要写日记时，一键完成： 1. 自动检查并初始化配置 2. 读取素材（SOUL、MEMORY、daily memory、近几天日记） 3. 生成目标日期日记文本 4. 渲染为 1080px 宽图片 ## 执行流程 ### Step 0：配置检查 - 若 `config.yaml` 存在：进入主流程 - 若不存在：按 `config.template.yaml` 自动初始化并继续 ### Step 1：确定目标日期 - 使用 `environment.timezone` - 默认目标：昨天（当前日期 - 1） ### Step 2：收集素材 必读： - `paths.soul_path` - `paths.memory_root_path` - `paths.daily_memory_dir/YYYY-MM-DD.md` 建议读取： - `paths.diary_text_dir` 最近 7 天日记（避免重复句式） - 可选新闻摘要目录（若存在） ### Step 3：写作 - 第一人称 - 真实、自然、有情绪与想法 - 禁止流水账式罗列 - 不编造不存在的事实 - 保存到 `<diary_text_dir>/YYYY-MM-DD.md` ### Step 4：生成图片 - 使用 `diary-template.html` 渲染 - 输出宽度必须为 `output.image_width`（默认 1080） - 高度自适应内容 - 输出路径：`<diary_text_dir>/diary-YYYY-MM-DD.png` ### Step 5：返回结果 返回： - `date` - `text_path` - `image_path` - `image_size` ## 约束 - 不覆盖已存在的同日期日记（除非用户明确要求） - 声称完成时必须给出实际输出路径 - 仅在缺文件时创建目录，不删除已有内容


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: matthew77/liang-tavily-search (version 1.0.1)

--- name: tavily-search description: Web search using Tavily's LLM-optimized API. Returns relevant results with content snippets, scores, and metadata. homepage: https://tavily.com metadata: {"openclaw":{"emoji":"🔍","requires":{"bins":["node"],"env":["TAVILY_API_KEY"]},"primaryEnv":"TAVILY_API_KEY"}} --- # Tavily Search Search the web and get relevant results optimized for LLM consumption. ## Authentication Get your API key at https://tavily.com and add to your OpenClaw config: ```json { "skills": { "entries": { "tavily-search": { "enabled": true, "apiKey": "tvly-YOUR_API_KEY_HERE" } } } } ``` Or set the environment variable: ```bash export TAVILY_API_KEY="tvly-YOUR_API_KEY_HERE" ``` ## Quick Start ### Using the Script ```bash node {baseDir}/scripts/search.mjs "query" node {baseDir}/scripts/search.mjs "query" -n 10 node {baseDir}/scripts/search.mjs "query" --deep node {baseDir}/scripts/search.mjs "query" --topic news ``` ### Examples ```bash # Basic search node {baseDir}/scripts/search.mjs "python async patterns" # With more results node {baseDir}/scripts/search.mjs "React hooks tutorial" -n 10 # Advanced search node {baseDir}/scripts/search.mjs "machine learning" --deep # News search node {baseDir}/scripts/search.mjs "AI news" --topic news # Domain-filtered search node {baseDir}/scripts/search.mjs "Python docs" --include-domains docs.python.org ``` ## Options | Option | Description | Default | |--------|-------------|---------| | `-n <count>` | Number of results (1-20) | 10 | | `--depth <mode>` | Search depth: `ultra-fast`, `fast`, `basic`, `advanced` | `basic` | | `--topic <topic>` | Topic: `general` or `news` | `general` | | `--time-range <range>` | Time range: `day`, `week`, `month`, `year` | - | | `--include-domains <domains>` | Comma-separated domains to include | - | | `--exclude-domains <domains>` | Comma-separated domains to exclude | - | | `--raw-content` | Include full page content | false | | `--json` | Output raw JSON | false | ## Search Depth | Depth | Latency | Relevance | Use Case | |-------|---------|-----------|----------| | `ultra-fast` | Lowest | Lower | Real-time chat, autocomplete | | `fast` | Low | Good | Need chunks but latency matters | | `basic` | Medium | High | General-purpose, balanced | | `advanced` | Higher | Highest | Precision matters, research | ## Tips - **Keep queries under 400 characters** - Think search query, not prompt - **Break complex queries into sub-queries** - Better results than one massive query - **Use `--include-domains`** to focus on trusted sources - **Use `--time-range`** for recent information - **Filter by `score`** (0-1) to get highest relevance results


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