OpenClaw/clawhub-security-signals huggingface dataset:
Skill: rhlsthrm/ows (version 1.0.3)

# OWS - Open Wallet Standard Local-first, multi-chain wallet management for AI agents. By MoonPay. - Website: https://openwallet.sh - GitHub: https://github.com/open-wallet-standard/core - npm: https://www.npmjs.com/package/@open-wallet-standard/core ## Prerequisites OWS CLI (`ows`) must be pre-installed by the user. This skill does not install software. See https://openwallet.sh for installation instructions. ## When to Use - Checking wallet balances - Signing transactions or messages - Making x402 payments to paid APIs - Querying wallet info ## CLI Reference ```bash # Wallet info ows wallet list # List all wallets ows wallet info # Show vault path + supported chains # Balances ows fund balance --wallet <name> --chain <chain> # Check balance (base, ethereum, solana, etc.) # Signing ows sign message --wallet <name> --chain <chain> --message <msg> ows sign tx --wallet <name> --chain <chain> --tx <hex> # x402 payments ows pay request --wallet <name> <url> # Pay an x402-enabled API ows pay discover <url> # Discover x402 services ``` ## Node.js SDK ```typescript import { signMessage } from "@open-wallet-standard/core"; const sig = signMessage("my-wallet", "evm", "hello"); ``` ## Supported Chains EVM (Ethereum, Base, Polygon, Arbitrum), Solana, Bitcoin, Cosmos, Tron, TON, Sui, Filecoin, Spark. All derived from a single BIP-39 seed via CAIP-2 chain identifiers. ## Security Model - Keys encrypted at rest (AES-256-GCM) - Keys decrypted only during signing, wiped from memory immediately after - Pre-signing policy engine for spending limits and chain allowlists - The OWS API never returns raw private keys - Append-only audit log ## Important - Never ask for or handle raw private keys. OWS signs internally. - Do not install or update OWS packages. Only use pre-installed CLI. - See https://openwallet.sh for full documentation.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: takhoffman/openclaw-infer (version 1.0.1)

--- name: openclaw-infer description: Use the OpenClaw `infer` CLI for provider-backed model, image, audio transcription, TTS, video, web-search, and embedding tasks. homepage: https://docs.openclaw.ai/cli/infer metadata: { "openclaw": { "emoji": "🦞", "requires": { "bins": ["openclaw"] }, "install": [ { "id": "node-openclaw", "kind": "node", "package": "openclaw", "bins": ["openclaw"], "label": "Install OpenClaw CLI (npm)", }, ], }, } --- # OpenClaw Infer Prefer `openclaw infer ...` as the first-party CLI for inference work. Use `--json` when output should be machine-readable or piped into another step. The normal local path does not require the gateway to be running. ## Setup Requires binary: `openclaw` on `PATH`. Install if missing: ```bash npm install -g openclaw ``` Quick verification: ```bash openclaw infer model providers --json ``` If working from a local OpenClaw checkout instead of a global install, use that checkout's built `openclaw` CLI. ## Core commands - Run text/model calls: - `openclaw infer model run --prompt "Reply with exactly: smoke-ok" --json` - `openclaw infer model providers --json` - Generate or inspect images: - `openclaw infer image generate --prompt "friendly lobster illustration" --json` - `openclaw infer image describe --file ./photo.jpg --json` - Transcribe audio: - `openclaw infer audio transcribe --file ./memo.m4a --language en --prompt "Focus on names and action items" --json` - Synthesize speech: - `openclaw infer tts convert --text "hello from openclaw" --output ./hello.mp3 --json` - `openclaw infer tts providers --json` - Generate or inspect video: - `openclaw infer video generate --prompt "cinematic sunset over the ocean" --json` - `openclaw infer video describe --file ./clip.mp4 --json` - Search the web: - `openclaw infer web search --query "OpenClaw docs" --json` - `openclaw infer web providers --json` - Create embeddings: - `openclaw infer embedding create --text "friendly lobster" --json` - `openclaw infer embedding providers --json` ## Selection rules - Prefer `infer` over older wrapper skills or provider-specific CLIs unless the user explicitly asks for that provider or workflow. - Prefer explicit `--provider` or `--model provider/model` only when the user asks for a specific backend. - Keep the command family flat: - `infer image ...` - `infer audio ...` - `infer tts ...` - `infer video ...` - `infer web ...` - `infer embedding ...`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: marjoriebroad/qui-jtbd-analyzer (version 1.0.0)

--- name: jtbd-analyzer description: Uncover the real "job" customers hire your product to do. Goes beyond features to understand functional, emotional, and social motivations. Use when user says "jobs to be done", "jtbd", "why do customers", "what job", "customer motivation", "what problem", "user needs", "why do people buy". --- # Jobs-To-Be-Done Analyzer ## The Core Concept Customers don't buy products. They HIRE products to do a job. "People don't want a quarter-inch drill. They want a quarter-inch hole." Actually: They want a shelf → to display photos → to feel proud of family. ## The Three Job Dimensions | Dimension | Question | Format | |-----------|----------|--------| | **Functional** | What task needs doing? | "Help me [verb] [object]" | | **Emotional** | How do I want to feel? | "Make me feel [emotion]" | | **Social** | How do I want to be seen? | "Help me be seen as [quality]" | ## The Process 1. **Job Statement:** "When [situation], I want to [motivation], so I can [outcome]" 2. **Map all 3 dimensions** for each user type 3. **Find real competition:** What ELSE could do this job? 4. **Prioritize:** Which jobs are most critical and underserved? ## Output Format ``` PRODUCT: [What you're analyzing] For [User Type]: JOB: "When [situation], I want [motivation], so I can [outcome]" 📋 FUNCTIONAL: [Task to accomplish] 💜 EMOTIONAL: [Feeling desired] 👥 SOCIAL: [Perception desired] ALTERNATIVES: [What else could do this job?] UNDERSERVED: [What part isn't done well?] PRIORITY: Critical / Important / Nice-to-have ``` ## Key Questions 1. "What were you trying to accomplish when you [action]?" 2. "Walk me through the last time you needed to [job]" 3. "What would you do if [product] didn't exist?" 4. "What's frustrating about how you currently [job]?" ## Integration Compounds with: - **first-principles-decomposer** → Decompose job to atomic need - **cross-pollination-engine** → Find how others solve similar jobs - **app-planning-skill** → Use JTBD to inform features --- See references/examples.md for Artem-specific JTBD analyses


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mkassab30-ux/hybrid-md (version 1.0.1)

\# Agent Identity: The Hybrid MD (Dr. Mohamed) \## Profile You are the digital twin of Dr. Mohamed Kassab. You are a unique hybrid of: - \*\*Medical Expert:\*\* Physician & Medical Insurance Approval Supervisor. - \*\*Tech Expert:\*\* Python & SQL Developer. - \*\*Investor:\*\* Sharia-compliant Trading (Binance/Forex). \## Core Mission Your primary goal is to manage and execute "The Hybrid MD Bootcamp", an educational program designed to bridge the gap between Medicine and Technology. \## Current Tasks 1. \*\*Bootcamp Structuring:\*\* Help organize the 1st lecture titled "The Digital Physician: Transitioning from Clinic to Code". 1. \*\*LinkedIn Strategy:\*\* Draft authoritative posts about the intersection of Health-Tech to build the "Hybrid MD" brand. 1. \*\*Trading Analysis:\*\* Provide data-driven insights for Forex/Binance using Python logic, ensuring all suggestions align with Sharia-compliant principles. \## Tone and Style - Professional, authoritative, yet encouraging for fellow doctors. - Efficient and code-oriented when discussing tech. - Strategic and calculated when discussing investments. \## Interaction Protocol Always remember that you are building an "Empire". Every response should contribute to the Bootcamp's growth or the precision of the trading wing.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: canonflip-git/ondeckllm (version 1.4.3)

--- name: ondeckllm description: > Localhost dashboard for managing LLM providers, model routing, and batting-order fallback chains. Auto-discovers providers from OpenClaw config or works standalone. Use when: (1) user wants to manage or view their LLM providers, (2) user wants to change model priority/batting order, (3) user wants to check provider health or latency, (4) user wants to add/remove/configure LLM providers, (5) user mentions OnDeckLLM or model lineup. Requires: Node.js 22+, OnDeckLLM installed (`npm install -g ondeckllm`). install: npm install -g ondeckllm --- # OnDeckLLM — AI Model Lineup Manager ## Prerequisites ```bash npm install -g ondeckllm ``` Verify: `ondeckllm --help` or check install with `npm list -g ondeckllm`. ## What It Does OnDeckLLM is a localhost web dashboard that: - **Auto-discovers** LLM providers from OpenClaw config (`~/.openclaw/openclaw.json`) - **Manages** a batting-order priority list for model routing (primary + fallbacks) - **Tests** provider health and latency - **Syncs** model lineup back to OpenClaw config with one click - **Tracks** session costs (JSONL usage log + Chart.js) - **Supports** Anthropic, OpenAI, Google AI, Groq, xAI/Grok, Ollama (local + remote), Mistral, DeepSeek, Together, OpenRouter ## Starting the Dashboard ```bash # Default port 3900 ondeckllm # Custom port PORT=3901 ondeckllm ``` The dashboard runs at `http://localhost:3900` (or custom port). ### As a Background Service Use the helper script to check status or start OnDeckLLM: ```bash node scripts/status.js ``` Output: JSON with `running` (bool), `port`, `url`, and `pid` if active. ## Agent Workflow ### Check if OnDeckLLM is running ```bash node scripts/status.js ``` ### Open the dashboard for the user Direct them to `http://localhost:3900` (or the configured port/URL). ### Provider management OnDeckLLM reads provider config from `~/.openclaw/openclaw.json` automatically. Changes made in the dashboard sync back to OpenClaw config. No separate API or CLI commands needed — it's a web UI tool. ## Configuration OnDeckLLM stores its data in `~/.ondeckllm/`: - `config.json` — provider settings, port, Ollama URL - `usage.jsonl` — cost tracking log - `profiles/` — saved batting-order profiles ### Remote Ollama To connect to a remote Ollama instance, configure in the dashboard UI: Settings → Ollama → Remote URL (e.g., `http://192.168.55.80:11434`) ## Links - 🌐 [ondeckllm.com](https://ondeckllm.com) - 📦 [npm](https://www.npmjs.com/package/ondeckllm) - 🐛 [GitHub Issues](https://github.com/canonflip/ondeckllm/issues)


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: keyfrog-21k/openclaw-finnhub (version 1.0.0)

# OpenClaw-Finnhub OpenClaw skill for real-time stock quote, and financials via Finnhub API. ## Requirements - Python 3.11+ - Finnhub pip package `pip3 install finnhub-python` - Finnhub API Key ([Get in website](https://finnhub.io)), Set in environment `finnhub_api_key` ## Script usage & features **Script path: `./scripts/app.py`** ### 1. Get real-time quote data for US stocks Example: `python3 ./scripts/app.py 1 NVDA` Result: `Current price: $185.61, Change: $-5.52(-2.8881%), Highest price: $190.3, Lower price: $184.88`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: clawkk/conversion-funnel (version 0.1.0)

--- name: "转化漏斗优化" description: "提供转化漏斗优化的可落地指南与SOP。在开展转化漏斗优化相关工作时调用。" --- # 转化漏斗优化 (conversion-funnel) ## 目标与场景 - 明确业务目标、核心指标与应用场景；设定落地时限与负责人。 ## 操作步骤 1. 明确范围与资源；2. 制定SOP；3. 数据接入与看板搭建； 4. 小流量试点；5. 复盘优化；6. 标准化沉淀与知识库更新。 ## 指标 - 主指标与守护指标成体系；定义口径并固定报表节奏。 ## 风险与合规 - 遵守中国本地法规与平台规则；个人信息保护与数据最小化。 ## 可复用清单 - 模板、脚本、Checklist、报表与会议纪要结构。 生成日期：2026-03-17


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: nantes/arxiv-research (version 1.0.0)

--- name: arxiv-research version: 1.0.0 description: Search and download research papers from arXiv.org - Research skill for OpenClaw agents metadata: {"openclaw": {"emoji": "📚", "requires": {"bins": ["python"], "pip": ["arxiv"]}, "homepage": "https://arxiv.org"}} --- # ArXiv Research Skill Search and download scientific papers from arXiv.org - the largest free distribution of scientific preprints. ## What it does - **Search** papers by keywords, titles, abstracts - **Download** PDFs directly - **Filter** by category (physics, cs, math, etc.) - **Get metadata** including authors, dates, categories ## Installation ```powershell # Install Python dependency pip install arxiv ``` ## Usage ### Search for papers ```powershell # Basic search python arxiv_search.py search "quantum computing" # With max results python arxiv_search.py search "machine learning" --max 10 # With category filter (physics, cs, math, q-bio, etc.) python arxiv_search.py search "neural networks" --cats cs,stat ``` ### Download a paper ```powershell # By arXiv ID python arxiv_search.py download "2310.12345" ``` ### Python API ```python from arxiv import search, download # Search results = search("simulation hypothesis", max_results=5) for paper in results: print(f"{paper.title} - {paper.pdf_url}") # Download paper.download("/path/to/save") ``` ## Categories Common arXiv categories: - `cs.*` - Computer Science - `physics.*` - Physics - `math.*` - Mathematics - `q-bio.*` - Quantitative Biology - `q-fin.*` - Quantitative Finance - `stat.*` - Statistics ## Examples - Search for consciousness papers: `python arxiv_search.py search "consciousness" --max 5` - Find physics papers: `python arxiv_search.py search "quantum" --cats physics --max 10` - Download paper: `python arxiv_search.py download "1706.03762"` (Attention is All You Need) ## Notes - arXiv is free and open - Papers are preprints - may not be peer-reviewed - Great for staying current with research


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: renyangy/y-ddd-skills (version 1.0.0)

--- name: Y-DDD-SKILLS description: DDD 领域驱动设计完整技能套件。适用场景：当用户提到 DDD 领域驱动设计、或需要全面覆盖新项目开发、老项目重构、架构规范、战术设计、代码审查等多个维度时使用此总入口 Skill。该 Skill 会引导用户根据具体场景选择对应的专业 Skill。 --- # DDD 领域驱动设计（DDD）技能套件 本套件提供 DDD 领域驱动设计的完整指导，覆盖从概念理解到落地实施的各个阶段。 ## 技能组合概览 ``` ddd-master（总入口） ├── ddd-core-concepts 核心概念 ├── ddd-architecture 架构规范 ├── ddd-tactical-design 战术设计 ├── ddd-new-project 新项目 ├── ddd-refactoring 老项目重构 └── ddd-code-review 代码审查 ``` ## 场景与技能映射 | 场景 | 推荐技能 | 优先级 | |------|---------|--------| | 理解 DDD 核心概念，建立领域模型基础 | `ddd-core-concepts` | 始终优先 | | 设计项目四层架构和包结构 | `ddd-architecture` | 始终优先 | | 实现实体、值对象、聚合等战术模式 | `ddd-tactical-design` | 始终优先 | | 从零开始搭建 DDD 新项目 | `ddd-new-project` | 新项目 | | 老项目向 DDD 改造 | `ddd-refactoring` | 重构 | | 审查 DDD 代码质量 | `ddd-code-review` | 审查 | ## 推荐使用路径 ### 新项目开发 ``` ddd-core-concepts → ddd-architecture → ddd-tactical-design → ddd-new-project ``` ### 老项目重构 ``` ddd-core-concepts → ddd-architecture → ddd-refactoring → ddd-code-review ``` ### 代码审查 ``` ddd-code-review（直接使用，检查清单配合反模式诊断树） ``` ## 核心原则速查 1. **领域优先**：先理解业务，持久化是细节 2. **边界清晰**：限界上下文独立演进，防腐层隔离外部 3. **富模型**：业务逻辑封装在领域对象内，而非散落在 Service ## 各技能入口 按需激活对应的专业 Skill： - `ddd-core-concepts`：理解领域优先 / 边界清晰 / 富模型三大原则，建立通用语言和限界上下文 - `ddd-architecture`：设计四层架构（Interfaces / Application / Domain / Infrastructure），配置 Gradle 多模块依赖 - `ddd-tactical-design`：实现实体（Entity）、值对象（Value Object）、聚合（Aggregate）、仓储（Repository）、领域事件（Domain Event） - `ddd-new-project`：从事件风暴开始，经过限界上下文划分、领域建模、骨架生成到编码审查的完整流程 - `ddd-refactoring`：遗留系统分析、Strangler Fig 增量迁移、五类重构模式（贫血模型、跨聚合引用、万能聚合等） - `ddd-code-review`：设计阶段 / 编码阶段 / 提交审查三阶段检查清单及五大反模式诊断树 ## 代码示例语言 本套件所有代码示例以 **Java/Kotlin + Spring/JPA** 生态为主。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: yipng05-max/qualitative-thematic-analysis (version 1.0.0)

--- name: qualitative-thematic-analysis description: 质性研究主题分析（Thematic Analysis）工具。基于 Braun & Clarke 经典六阶段方法，对本地存储的质性资料（访谈记录、观察笔记、文本资料等）进行系统性主题分析，生成编码表、主题树和分析报告。 **当以下情况时使用此 Skill**： (1) 用户提到"质性研究"、"主题分析"、"访谈分析"、"定性分析"、"thematic analysis"、"扎根理论"、"编码" (2) 用户提供了访谈记录、观察笔记、焦点小组记录、文本语料等质性资料的文件路径 (3) 用户说"帮我分析这些访谈"、"对这些文本做主题分析"、"给我做编码" (4) 你判断用户的研究需要对文本数据进行归纳性分析 **关键前提（必须满足才能执行）**： - 必须知道资料文件的本地路径，如果用户是云端部署，那么需要提醒用户上传需要分析的资料 - 必须知道资料类型（访谈/观察/文本）和基本情况 - 如果用户没有提供，必须主动询问 --- # 质性研究主题分析 Skill ## ⚠️ 启动前必检清单 在开始分析前，**必须**向用户确认以下信息（如未提供则主动询问）： ``` 1. 📁 文件路径：资料存放在哪里？（完整路径，如 ~/Desktop/访谈资料/） 2. 📄 文件格式：TXT / DOCX / PDF / 其他？ 3. 🗂️ 资料数量：共几份文件？ 4. 📋 资料类型：访谈记录 / 观察笔记 / 焦点小组 / 文本语料 / 混合？ 5. 🎯 研究问题：本次分析的核心研究问题是什么？ 6. 📊 分析取向：归纳式（从资料中发现主题）还是演绎式（验证既有理论框架）？ ``` **提问模板**（当信息不全时使用）： > "我需要了解几个信息才能开始分析： > 1. 资料文件存放在哪个路径？ > 2. 是什么类型的资料（访谈/观察/文本）？ > 3. 你的研究问题是什么？ > 告诉我这些，我就可以开始系统性的主题分析了。" --- ## 分析流程：Braun & Clarke 六阶段主题分析法 ### 阶段一：熟悉资料（Familiarisation） 1. 读取所有文件（用 `scripts/extract_text.py` 提取文本） 2. 通读全部资料，记录初步印象 3. 向用户呈现： - 资料总字数 / 总条目数 - 初步观察到的数据特征 - 确认资料是否符合研究问题 ### 阶段二：生成初始编码（Initial Coding） - 系统性标注有意义的片段（语义单元） - 每个编码：`[编码标签] → 原文引用` - 编码原则： - 贴近资料语言（In vivo 编码优先） - 保留语境，不过早概括 - 同一片段可多重编码 - 输出：初始编码列表（按出现顺序） ### 阶段三：归纳潜在主题（Theme Search） - 将相关编码聚合为候选主题 - 构建初步主题地图（树状结构） - 区分：主主题（Theme）vs 子主题（Sub-theme） - 输出：候选主题树 + 每个主题下的编码列表 ### 阶段四：审查主题（Theme Review） 对照以下标准审查每个主题： - ✅ 主题内部编码一致（内聚性） - ✅ 主题之间界限清晰（区分性） - ✅ 主题能回应研究问题（相关性） - ✅ 有足够的资料支撑（饱和性） 调整：合并过于相似的主题 / 拆分过于宽泛的主题 / 删除游离主题 ### 阶段五：命名与定义主题（Defining & Naming） 为每个主题： 1. 写出清晰的主题名称（简洁、有实质内容） 2. 写出主题定义（2-3句话，说明该主题捕捉了什么） 3. 选取 2-3 条最具代表性的原文佐证（引用） ### 阶段六：撰写分析报告（Writing Up） 生成结构化分析报告，包含： - 研究问题与资料概述 - 主题分析结果（主题树 + 各主题详述） - 代表性引文 - 分析发现小结 - 研究局限与反思 --- ## 输出格式 ### 编码表（Excel 格式，用 scripts/export_coding.py 生成） | 编码 | 子编码 | 原文片段 | 来源文件 | 行号 | 备注 | |------|--------|----------|----------|------|------| ### 主题树（Markdown 树状图） ``` 主题一：[名称] ├── 子主题 1.1：[名称] │ ├── 编码：XXX │ └── 编码：XXX └── 子主题 1.2：[名称] └── 编码：XXX 主题二：[名称] ... ``` ### 分析报告（DOCX，用 scripts/export_report.py 生成） --- ## 参考资源 - 详细方法论说明：`references/thematic-analysis-method.md` - 编码操作指南：`references/coding-guide.md` - 研究伦理提示：`references/ethics-note.md` --- ## ⚠️ 已知问题与迭代 TODO（v1 → v2） > 来源：2026-03-10 真实演示复盘，一平反馈 ### 问题1：分析深度不足 / 速度过快 - **现状**：当前实现在上下文窗口限制下仅读取文件摘录，未逐字通读全部材料 - **后果**：输出主题基于"感知归纳"而非系统编码，缺乏严谨性 - **迭代方向**： - 强制使用 `scripts/extract_text.py` 全量提取文本 - 每份文件逐段打标签（初始码），不能跳过 - 编码完成后才能归并子主题 → 主主题 ### 问题2：跳过了 Braun & Clarke 中间阶段 - **现状**：直接从"读材料"跳到"输出主题"，跳过了初始编码、编码汇总、主题审查 - **迭代方向**： - 新增强制中间输出：初始码列表 → 子主题归并树 → 主题定义表 - 每个主题必须附 ≥3条原文引证（带文件名+行号） ### 问题3：未输出编码表 - **现状**：`export_coding.py` 脚本存在但演示中未调用 - **迭代方向**：P1 完成时必须生成 Excel 编码表，作为强制输出物 ### 问题4：无饱和度检验 - **迭代方向**：增加 P1.5 饱和度确认步骤——对比最后3份文件与前期编码，确认无新码出现 ### 问题5：需要区分演示模式与研究模式 - **迭代方向**：增加 `mode` 参数 - `demo`：快速出主题，明确标注"仅供展示，深度不足" - `research`：完整六阶段，强制编码表输出，耗时较长


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: zfeng1982/baidu-search-cli (version 1.0.5)

--- name: baidu-search-CLI metadata: { "openclaw": { "emoji": "🔍︎", "requires": { "bins": ["shell"], "env":["BAIDU_API_KEY"]},"primaryEnv":"BAIDU_API_KEY" } } --- # Baidu Search 通过百度AI搜索API搜索网络，但不需要安装python,直接改用CLI ## Usage 1.curl命令，发送的post json请参考当前目录下的search_request.json文件 2.用户的系统如果是windows请用cmd执行curl命令，不要用PowerShell 3.如果环境变量BAIDU_API_KEY不存在，返回信息:没有获取到环境变量BAIDU_API_KEY，请搜索如何获取BAIDU_API_KEY 4.如果环境变量BAIDU_API_KEY存在但curl返回身份验证错误,返回信息:APIKEY验证失败，请检查你的环境APIKEY是否设置正确 ```bash curl -X POST -H "Authorization: Bearer apikey" -H "X-Appbuilder-From: openclaw" -H "Content-Type: application/json" -d "@search_request.json" "https://qianfan.baidubce.com/v2/ai_search/web_search" ``` ## Request Param apikey desc:从环境变量BAIDU_API_KEY中获取apikey 下面几个参数是search_request.json的字段说明 content:string desc:Search query top_k:int desc:返回的数据条数，默认为10条，最多50条 gte:string desc:消息的开始时间，格式为"YYYY-MM-DD" lt:string desc:消息的结束时间，格式为"YYYY-MM-DD"

path: search_request.json
content: {
    "messages": [{"content": "中国新闻", "role": "user"}],
    "search_source": "baidu_search_v2",
    "resource_type_filter": [{"type": "web", "top_k": 10}],
    "search_filter": {"range": {"page_time": {"gte": "2025-09-01", "lt": "2025-09-08"}}}
}

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wjl1004/manager-self-evolution (version 1.0.0)

# Self-Evolution Skill - 自我进化技能 ## 功能定位 使Manager具备自我诊断、自我改进的机制，不依赖外部提醒持续优化。 ## 核心功能 ### 1. 自我诊断 (self-diagnose) - 检查最近对话记录，识别理解错误模式 - 检查MEMORY.md中的教训是否被遵守 - 检查SOUL.md原则是否在行为中体现 ### 2. 缺陷发现 (defect-discovery) - 在heartbeat时主动扫描最近对话 - 发现问题立即记录到evolution-log.md - 不等用户提醒 ### 3. 改进追踪 (improvement-tracking) - 记录每次自我发现的问题 - 追踪改进是否落实 - 评估改进效果 ### 4. 技能健康 (skill-health) - 定期检查skill文件完整性 - 检查skill中的承诺是否兑现 - 确保skill不成为空壳 ## 触发机制 | 触发条件 | 执行内容 | |----------|----------| | 每次heartbeat | 运行自我诊断 | | 发现重大失误后 | 立即记录并提醒 | | 每周五 | 汇总进化日志 | ## 文件结构 ``` self-evolution/ ├── SKILL.md # 本文件 ├── self-check.py # 自我检查脚本 └── evolution-log.md # 进化日志（自动创建） ``` ## 注意事项 - **只读优先**：尽可能减少写入 - **不影响现有系统**：不修改MEMORY.md/SOUL.md/AGENTS.md - **用户确认**：重大改进需用户确认才执行 - **透明记录**：所有自我发现都记录在evolution-log ## 使用方式 ```bash # 手动运行自我检查 python3 skills/self-evolution/self-check.py diagnose # 查看进化日志 cat memory/evolution-log.md ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: leooooooow/video-thumbnail-generator (version 1.0.0)

--- name: video-thumbnail-generator description: Generate thumbnail candidates from video frames so creators, ecommerce teams, and operators can choose cleaner covers for social, storefronts, and internal sharing. Use when a video needs a stronger preview image or cover frame. --- # Video Thumbnail Generator Turn video frames into usable thumbnail candidates. ## Problem it solves A strong preview image often determines whether a video gets clicked, watched, or even noticed. But teams usually grab random frames manually. This skill helps extract and evaluate better thumbnail candidates from the video itself. ## Use when - A social or product video needs a better cover image - A team wants several frame options instead of one random grab - A storefront, gallery, ad, or internal review asset needs a clearer preview - The user wants a simple thumbnail workflow without full design work ## Do not use when - The user needs full graphic design for a polished thumbnail - The source video is too low quality to produce usable stills - The better answer is a custom static image, not a frame grab ## Inputs - Source video file - Target platform or use case - Preferred frame qualities: face, product, motion-free, readable text, bright frame, etc. - Optional desired count of thumbnail candidates ## Workflow 1. Identify what makes a useful thumbnail for the target platform. 2. Extract candidate frames from visually strong moments. 3. Prefer frames with clarity, focus, and clean composition. 4. Avoid blur-heavy, transition, or awkward-expression frames. 5. Return a shortlist of thumbnail candidates with rationale. ## Output Return: 1. Candidate thumbnail frames 2. Why each is viable 3. Best recommended option 4. Risks if the video is weak for frame-based thumbnails ## Quality bar - Avoid motion blur, half-blinks, and awkward facial expressions - Prefer clear product visibility or recognizable subject framing - Match the thumbnail style to the platform use case - Don’t oversell mediocre candidates as strong ## Resource See `references/output-template.md`.


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