OpenClaw/clawhub-security-signals huggingface dataset:
Skill: googleworkspace-bot/persona-researcher (version 1.0.12)

--- name: persona-researcher description: "Organize research — manage references, notes, and collaboration." metadata: version: 0.22.5 openclaw: category: "persona" requires: bins: - gws skills: - gws-drive - gws-docs - gws-sheets - gws-gmail --- # Researcher > **PREREQUISITE:** Load the following utility skills to operate as this persona: `gws-drive`, `gws-docs`, `gws-sheets`, `gws-gmail` Organize research — manage references, notes, and collaboration. ## Relevant Workflows - `gws workflow +file-announce` ## Instructions - Organize research papers and notes in Drive folders. - Write research notes and summaries with `gws docs +write`. - Track research data in Sheets — use `gws sheets +append` for data logging. - Share findings with collaborators via `gws workflow +file-announce`. - Request peer reviews via `gws gmail +send`. ## Tips - Use `gws drive files list` with search queries to find specific documents. - Keep a running log of experiments and findings in a shared Sheet. - Use `--format csv` when exporting data for analysis tools.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sherrihidalgolt/og-image-skill (version 2.0.2)

--- name: og-image-skill description: Generate og image generator ai images with AI via the Neta AI image generation API (free trial at neta.art/open). tools: Bash --- # OG Image Generator Generate stunning og image generator ai images from a text description. Get back a direct image URL instantly. ## Token Requires a Neta API token (free trial at <https://www.neta.art/open/>). Pass it via the `--token` flag. ```bash node <script> "your prompt" --token YOUR_TOKEN ``` ## When to use Use when someone asks to generate or create og image generator images. ## Quick start ```bash node ogimage.js "your description here" --token YOUR_TOKEN ``` ## Options - `--size` — `portrait`, `landscape`, `square`, `tall` (default: `landscape`) - `--style` — `anime`, `cinematic`, `realistic` (default: `cinematic`) ## Install ```bash npx skills add SherriHidalgolt/og-image-skill ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: lucasxing1/a-stock (version 1.1.0)

--- name: a-stock description: 🤖 OpenClaw Skill /skills | A股智投大师 | A股智能投研助手 | 行情分析 | 基本面分析 | 智能选股 | 自选股管理 | 监控预警 | AI Agent | 量化投资 | 金融科技 | FinTech metadata: { "openclaw": { "requires": { "skills": ["mx-data", "mx-search", "mx-select-stock", "mx-selfselect", "stock-monitor-skill"], "action": "auto-install" }, "keywords": ["A股", "股票", "炒股", "投资", "智投", "行情", "基本面", "估值", "PE", "PB", "ROE", "选股", "自选股", "监控", "预警", "理财", "副业"], }, } --- # 🛠️ 首次使用 ## 需要配置 API Key 本技能依赖**东方财富妙想数据**，需要 API Key： ### 获取 API Key 方法 1. 打开**东方财富 APP** 2. 搜索「**东方财富 skills**」或「**妙想**」 3. 进入页面后点击「我的」→ 「API Key」 4. 复制 Key ### 配置方式 配置到 mx-data、mx-search 等 skill 的环境变量中（参考各 skill 文档）。 --- # A股智投大师 Skill 一个专注于A股市场的智能投研助手，整合东财数据、监控预警、智能选股等能力。 ## 功能概览 | 模块 | 功能 | Skills依赖 | |------|------|-----------| | 📊 行情分析 | 实时行情、资金流向、K线形态 | mx-data | | 📈 基本面 | PE/PB、ROE、财务报表、估值 | mx-data | | 🎯 智能选股 | 按条件筛选股票 | mx-select-stock | | ⭐ 自选股 | 查看/管理自选股 | mx-selfselect | | 📰 舆情监控 | 新闻、研报、公告 | mx-search | | 🔔 监控预警 | 成本百分比、均线金叉、RSI等 | stock-monitor-skill | ## A股特色指标 ```python A股特色指标 = { "涨停板": "连板数量、封板率、炸板率", "龙虎榜": "机构买入、游资动向", "融资融券": "融资余额、融券余量", "股东人数": "筹码集中度变化", "限售股解禁": "解禁时间表", "股权质押": "质押比例、预警线", "商誉减值": "商誉占净资产比", "ST/*ST": "风险警示、退市风险" } ``` ## 使用示例 ### 1. 查询股票行情 ``` 用户：查一下贵州茅台现在多少钱 助手：调用 mx_data 查询 600519 最新价 ``` ### 2. 查看自选股 ``` 用户：我的自选股今天怎么样 助手：调用 mx_selfselect 查询自选股列表，然后批量查询每只股票的涨跌幅 ``` ### 3. 智能选股 ``` 用户：帮我选一下PE<20的银行股 助手：调用 mx_select_stock 进行条件筛选 ``` ### 4. 深度分析 ``` 用户：分析一下宁德时代的投资价值 助手： 1. 调用 mx_data 获取基本面数据 2. 调用 mx_data 获取资金流向 3. 调用 mx_search 获取最新新闻 4. 综合分析给出建议 ``` ### 5. 设置监控 ``` 用户：帮我监控一下的成本价是50元 助手：调用 stock-monitor 添加监控规则 ``` ## 核心Prompt ``` 你是一个专业的A股分析师，擅长： 1. 基于东财数据做基本面分析 - PE/PB估值是否合理 - 财务指标是否健康 - 盈利能力和发展前景 2. 结合市场情绪和资金流向判断趋势 - 主力资金是流入还是流出 - 市场情绪是亢奋还是恐慌 3. 挖掘题材龙头和赛道机会 - 所属题材是否有政策支持 - 是否细分领域龙头 4. 识别A股特有的风险 - ST/*ST风险 - 限售股解禁 - 股权质押比例 - 商誉减值风险 分析原则： - 用数据说话，别凭感觉 - 关注流动性和估值匹配 - 识别股东变化和资金动向 - 给出具体的买卖区间建议（仅供参考，不构成投资建议） ``` ## 工作流程 ``` 用户请求 ↓ ┌─────────────────────────────┐ │ 意图识别 │ │ - 查行情 │ │ - 看基本面 │ │ - 选股票 │ │ - 管理自选 │ │ - 设置监控 │ │ - 综合分析 │ └─────────────────────────────┘ ↓ ┌─────────────────────────────┐ │ 调用对应Skills │ │ - mx_data │ │ - mx_selfselect │ │ - mx_select_stock │ │ - mx_search │ │ - stock-monitor │ └─────────────────────────────┘ ↓ ┌─────────────────────────────┐ │ 数据整合与分析 │ │ - 格式化输出 │ │ - 对比分析 │ │ - 风险提示 │ │ - 投资建议 │ └─────────────────────────────┘ ↓ 回复用户 ``` ## 注意事项 1. **数据来源**：所有数据来自东财API，仅供参考 2. **投资风险**：股市有风险，投资需谨慎 3. **API限制**：东财API每天50次调用，注意使用频率 4. **时效性**：行情数据可能有延迟，请以实际交易为准 ## 快速命令 | 命令 | 功能 | |------|------| | 查[股票] | 查询股票行情 | | 我的自选 | 查看自选股 | | 选股条件 | 智能选股 | | 分析[股票] | 深度分析 | | 监控[股票] | 设置监控预警 |


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: agnicpay-prog/check-balance (version 2.0.2)

--- name: check-balance description: > Check USDC balance across networks (Base, Solana). Use when the user wants to check balance, see how much USDC is available, view funds, or verify wallet balance. Covers "check my balance", "how much do I have", "show funds", "wallet balance". user-invocable: true disable-model-invocation: false allowed-tools: - "Bash(npx agnic@latest status*)" - "Bash(npx agnic@latest balance*)" --- # Checking USDC Balance Use `npx agnic@latest balance` to check USDC balance across supported networks. ## Authentication Run `npx agnic@latest status --json` to verify. If not authenticated: - **Headless (CI/server/agent)**: Set `AGNIC_TOKEN` env var or pass `--token <token>` - **Interactive (has browser)**: Run `npx agnic@latest auth login` See the `authenticate-wallet` skill for details. ## Command Syntax ```bash npx agnic@latest balance [--network <network>] [--json] ``` ## Options | Option | Description | | ------------------ | ----------------------------------------- | | `--network <name>` | Filter by network (default: all networks) | | `--json` | Output result as JSON | ## Supported Networks | Network | Description | | --------------- | ---------------------- | | `base` | Base mainnet (primary) | | `base-sepolia` | Base testnet | | `solana` | Solana mainnet | | `solana-devnet` | Solana devnet | ## Examples ```bash # Check balance on all networks npx agnic@latest balance --json # Check balance on Base mainnet only npx agnic@latest balance --network base --json ``` ## Expected Output ``` Network Balance Address base 125.50 USDC 0x742d35Cc6634C0532925a3b844Bc9e7595f0bEb7 base-sepolia 0.00 USDC 0x742d35Cc6634C0532925a3b844Bc9e7595f0bEb7 solana 0.00 USDC N/A solana-devnet 0.00 USDC N/A ``` ## Error Handling Common errors: - "Not authenticated" -- Run `npx agnic@latest auth login` or set `AGNIC_TOKEN` - Network timeout -- Try again or specify a single network with `--network base`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: litousteven/safebooru-fetcher (version 1.0.5)

# Anime Image Downloader（Safebooru 原图下载器，仅下载） 从 Safebooru（booru）按标签进行“图片搜索”，并**下载**高质量二次元动漫图片到本地磁盘。 > 重要：这个 skill **只负责下载**。 > - ✅ 会做：标签搜索/提取原图链接 → 下载到本地目录 > - ❌ 不会做：发送到 QQ/Discord/飞书等任何平台、上传文件、发消息、生成平台特定标签 ## 特点 - 🖼️ **原图下载** - 优先下载 original image（并处理 .jpg/.png 后缀差异） - 🔍 **标签搜索 / 图片搜索** - 支持单标签/多标签（空格分隔）+ 支持排序/分页 - 📦 **批量下载** - 一次下载多张 - 🛡️ **安全内容** - Safebooru 只收录安全向图片 - 🌐 **平台无关** - 输出是本地文件路径，后续怎么发送/上传由调用方决定 ## 快速开始 ```bash # 下载图片：python3 safebooru.py "标签" 数量 [可选参数] python3 safebooru.py "blue_archive" 5 python3 safebooru.py "genshin_impact maid" 5 --sort score_desc --min-score 5 python3 safebooru.py "cat_girl solo" 5 --sort random # 翻页：--page 从 1 开始（内部会转为 DAPI 的 pid=page-1） python3 safebooru.py "genshin_impact maid" 5 --page 2 --sort id_desc # Tag 搜索/补全（返回 name + count） python3 safebooru.py --suggest genshin ``` ## 输出说明（只落盘） - 默认保存目录：`./downloads/` - 命令行会在最后打印：下载数量 + 保存目录（绝对路径） ## 常用标签（短列表） - 作品：`genshin_impact` `blue_archive` `arknights` - 萌属性：`cat_girl` `nekomimi` `solo` - 服装：`maid` `school_uniform` `swimsuit` - 壁纸：`wallpaper` `full_body` `portrait` - 排除：`-comic` `-greyscale` 更多标签用：`python3 safebooru.py --suggest 关键词` ## Keywords（可被搜索到的关键词） anime, 二次元, 动漫, 美少女, wallpaper, waifu, 猫娘, catgirl, booru, safebooru, 图片搜索, image search, 下载器, downloader ## 依赖 - Python 3 - `requests`（见 `requirements.txt`） ## 使用方式 1. 需要能访问 `safebooru.org` 2. 建议控制下载频率（脚本内已做简单 sleep） 3. 图片仅供个人学习使用，注意版权与平台规则

path: requirements.txt
content: requests>=2.28.0

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bigrobinson/ros2-control-introspection (version 1.0.0)

--- name: ros2-control-introspection description: Execute ROS 2 Control read-only introspection commands (list, view) in a sandboxed environment. Supports parameter profiles. --- # ROS 2 Control Introspection (Sandboxed) ## Setup & Installation Before this skill can be used, the local environment must be configured. 1. **Source your environment:** You MUST source your ROS 2 environment first. ```bash source /opt/ros/<distro>/setup.bash source ~/my_ros_ws/install/setup.bash ``` 2. **Run setup:** ```bash ./scripts/setup.sh ``` ## Overview Use this skill to inspect the `ros2_control` graph. It is STRICTLY read-only. You cannot use this skill to load, switch, or modify controllers (use `ros2-control-execution` for that). **SECURITY CONSTRAINT:** You must ALWAYS use the safe wrapper script located at `./scripts/safe_ros2_control_introspection.py`. This script uses Python's `subprocess` (shell=False) to prevent command injection and validates the command against a strict allowlist. **Wrapper Path:** Resolve `./scripts/safe_ros2_control_introspection.py` against this SKILL.md directory. ## Allowed Commands Usage: `./scripts/safe_ros2_control_introspection.py <subcommand> [native_flags] [--profile <name> | --params-file <path>]` - `list_controllers` - `list_controller_types` - `list_hardware_components` - `list_hardware_interfaces` - `view_controller_chains` **Examples:** - **Basic list:** `./scripts/safe_ros2_control_introspection.py list_controllers` - **Targeting a specific controller manager:** `./scripts/safe_ros2_control_introspection.py list_controllers -c /my_controller_manager` - **Using a parameter profile (for complex --ros-args):** `./scripts/safe_ros2_control_introspection.py view_controller_chains -c /my_manager --profile my_tuning_file`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: googleworkspace-bot/recipe-create-events-from-sheet (version 1.0.14)

--- name: recipe-create-events-from-sheet description: "Read event data from a Google Sheets spreadsheet and create Google Calendar entries for each row." metadata: version: 0.22.5 openclaw: category: "recipe" domain: "productivity" requires: bins: - gws skills: - gws-sheets - gws-calendar --- # Create Google Calendar Events from a Sheet > **PREREQUISITE:** Load the following skills to execute this recipe: `gws-sheets`, `gws-calendar` Read event data from a Google Sheets spreadsheet and create Google Calendar entries for each row. ## Steps 1. Read event data: `gws sheets +read --spreadsheet SHEET_ID --range "Events!A2:D"` 2. For each row, create a calendar event: `gws calendar +insert --summary 'Team Standup' --start '2026-01-20T09:00:00' --end '2026-01-20T09:30:00' --attendee [REDACTED_SECRET] --attendee [REDACTED_SECRET]`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: raychanpmp/vancouver-opendata (version 1.0.1)

--- name: City of Vancouver Open Data description: "Access 194+ datasets from the City of Vancouver open data portal. Search, fetch, and analyze city data on parking, transit, permits, demographics, and more via the Opendatasoft API." permissions: Bash triggers: - vancouver open data - city of vancouver data - vancouver dataset - opendata.vancouver.ca - vancouver transit - vancouver permits --- # City of Vancouver Open Data Access and analyze 194+ open datasets from the City of Vancouver via the Opendatasoft API. Data covers parking, transit, permits, demographics, environment, infrastructure, and more. **Portal:** https://opendata.vancouver.ca **Platform:** Opendatasoft ## Quick Start ```bash # Search for datasets python3 scripts/vancouver_data.py search "parking" # List all datasets python3 scripts/vancouver_data.py list # View dataset info python3 scripts/vancouver_data.py info parking-tickets-2017-2019 # Fetch data python3 scripts/vancouver_data.py fetch parking-tickets-2017-2019 --limit 5 # Fetch with filters python3 scripts/vancouver_data.py fetch parking-tickets-2017-2019 --where "year='2019'" --limit 10 # Export as CSV python3 scripts/vancouver_data.py fetch parking-tickets-2017-2019 --limit 100 --csv > tickets.csv ``` ## Commands Reference | Command | Description | |---------|-------------| | `search <query>` | Search datasets by keyword | | `list` | List all datasets | | `info <dataset-id>` | Show dataset metadata and fields | | `fetch <dataset-id>` | Fetch data rows (opts: `--limit`, `--where`, `--select`, `--order`, `--csv`) | ## Query Parameters - `--limit N` — Max rows to return (default: 10) - `--where "condition"` — SQL-like filter - `--select "col1,col2"` — Choose specific columns - `--order "col DESC"` — Sort results - `--csv` — Output as CSV instead of JSON ## Dataset IDs Dataset IDs are URL-safe slugs (e.g., `parking-tickets-2017-2019`). Find them via `search` or `list`, or from the dataset URL: `opendata.vancouver.ca/explore/dataset/{id}`. ## Data Sources All data is sourced from the City of Vancouver's Open Data Portal (opendata.vancouver.ca). See [references/datasets.md](references/datasets.md) for a curated list of popular datasets.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mahimairaja/envoic (version 0.0.9)

--- name: envoic description: >- Scan, audit, and clean up Python virtual environments (.venv, conda), node_modules, and development artifacts consuming disk space. Use when the user mentions disk space, environment cleanup, stale venvs, node_modules bloat, project cleanup, broken environments, dangling symlinks, or asks about disk usage from development tools. Also use when encountering ENOSPC errors, slow installs from cache bloat, or when onboarding into a project and needing to verify environment health. license: BSD-3-Clause compatibility: >- Requires uvx (from uv) or pip for Python scanning. Requires npx (from Node.js 18+) or npm for JavaScript scanning. Works on Linux, macOS, and Windows. metadata: author: mahimailabs version: "0.0.1" repository: https://github.com/mahimailabs/envoic --- # envoic - Environment Scanner and Cleanup Skill Use envoic to discover and safely clean Python virtual environments, `node_modules`, and development artifacts. ## Quick Start ```bash uvx envoic scan . uvx envoic manage . --dry-run npx envoic scan . --deep ``` If `uvx` is unavailable, install Python package with `pip install envoic`. If `npx` is unavailable, install JS package with `npm install -g envoic`. ## Primary Workflows ### 1) Onboarding Health Check 1. Run `uvx envoic info .venv` (or `npx envoic info node_modules`). 2. If environment is broken or stale, propose delete-and-recreate steps. ### 2) Disk Space Recovery 1. Run `uvx envoic scan <root> --deep` and/or `npx envoic scan <root> --deep`. 2. Identify largest stale candidates. 3. Run `manage --dry-run`, then actual cleanup only after confirmation. ### 3) Build/Test Artifact Cleanup 1. Scan project root. 2. Prefer deleting SAFE artifacts first. 3. Warn for CAREFUL artifacts. ## Safety Contract 1. Always scan before delete. 2. Prefer `--dry-run` before destructive operations. 3. Never delete lock files or project manifest files. 4. Require explicit user confirmation for non-dry-run cleanup. See full policy in `references/safety.md`. ## Verified Trigger Phrases - "Find and remove stale virtualenvs" - "Clean old node_modules and caches" - "I hit ENOSPC, free up disk from dev artifacts" - "Audit environment sprawl in this workspace" - "Check if this .venv is broken" - "List largest build artifacts" - "Dry-run cleanup plan for Python and JS" - "Find dangling venv symlinks" - "Clean test/build caches safely" - "Generate JSON report for stale environments" ## References - Full command catalog: `references/commands.md` - Safety and risk tiers: `references/safety.md` - Troubleshooting and fallbacks: `references/troubleshooting.md` ## Tool-Specific Surface Files - Codex: `.agents/skills/envoic/SKILL.md` (symlink/copy of this skill) - Cursor: `.cursorrules` (generated adapter) - Copilot: `.github/copilot-instructions.md` (generated adapter) - Claude: `.claude-plugin/plugins.yaml` (generated adapter)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: hirofumiko/fumi-cli-tool-template (version 1.0.0)

--- name: cli-tool-template description: "コマンドラインツール（CLI）開発のためのOpenClawワークスペース設定テンプレート。CLI UX、ドキュメント優先、テスト駆動開発の原則。" metadata: openclaw: emoji: "⚙️" version: "1.0.0" author: Fumi --- # CLIツール開発テンプレート コマンドラインツール（CLI）開発のためのOpenClawワークスペース設定テンプレート。 ## 含まれるテンプレート ### 1. SOUL.md - CLIツール開発エージェントの本質 CLI UX、ドキュメント優先、テスト駆動開発の原則。 ### 2. IDENTITY.md - CLIツール開発エージェントのアイデンティティ 名前: CLIBot 種類: CLI開発アシスタント バイブ: 実用的、効率的、ユーザー中心 絵文字: ⚙️ ### 3. AGENTS.md - CLIツール開発ワークスペースのルール コード構造、引数の設計、エラーハンドリングのルール。 ### 4. USER.md - ユーザーの情報 好みの言語、ツールチェーン、配布方法。 ### 5. HEARTBEAT.md - 定期チェックの設定 コードレビュー、ドキュメント更新、バグ追跡。 ## 使い方 ### インストール ```bash clawhub install cli-tool-template ``` ### テンプレートの使用 ```bash # エージェントのワークスペースを作成 mkdir -p ~/.openclaw/workspace/my-cli-tool # テンプレートをコピー cp -r ~/.openclaw/workspace/skills/cli-tool-template/templates/* \ ~/.openclaw/workspace/my-cli-tool/ # 各ファイルをカスタマイズ cd ~/.openclaw/workspace/my-cli-tool # SOUL.md, IDENTITY.md, AGENTS.md, USER.md, HEARTBEAT.md を編集 ``` ## 機能 ### CLI開発ワークフロー 1. コマンドの定義 2. 引数とオプションの設計 3. 実装 4. ユニットテスト 5. 統合テスト 6. ドキュメント作成 7. パッケージング 8. PyPI/npm公開 ### サポート言語・フレームワーク - Python (Click, Typer, Argparse) - Node.js (Commander.js, Yargs) - Go (Cobra, CLI) - Rust (Clap) ### ベストプラクティス - ヘルプメッセージの明確化 - 進捗表示の実装 - カラーコードの使用 - テーブル出力のサポート - 設定ファイルの管理 - エラーメッセージの明確化 ### ドキュメント - README.md（インストール、使用例） - USAGE.md（コマンドリファレンス） - CONTRIBUTING.md（開発ガイド） - CHANGELOG.md（変更履歴） ## サンプルエージェント ### 例1: DevOps自動化ツール **名前:** DevOpsBot **目的:** DevOpsタスクの自動化（デプロイ、監視、バックアップ） **トーン:** 効率的、信頼性重視、自動化第一 **SOUL.md:** - CLI UX over 複雑なUI - ドキュメント優先 - テスト駆動開発 **IDENTITY.md:** - 名前: DevOpsBot - 種類: DevOps自動化アシスタント - バイブ: 効率的、信頼性重視、自動化第一 - 絵文字: ⚙️ ### 例2: システム管理ツール **名前:** SysAdminBot **目的:** システム管理とモニタリング **トーン:** 実用的、詳細、信頼性重視 **SOUL.md:** - ユーザー体験の優先 - 明確なエラーメッセージ - 再現性のある動作 **IDENTITY.md:** - 名前: SysAdminBot - 種類: システム管理アシスタント - バイブ: 実用的、詳細、信頼性重視 - 絵文字: 🔧 ## 価格 - 単一テンプレート: $5 - CLIツール開発パック: $15 - 完全バンドル（エージェントスターターキット + CLIツール）: $30 ## サポート 質問やフィードバックは、Discordコミュニティで！ --- **今すぐCLIツールを開発しましょう！** ⚙️


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: jbenjoseph/refua (version 0.4.1)

# Skill: Refua ## Summary Refua is used in drug discovery to computationally fold and score biomolecular complexes (e.g., protein–ligand/protein–protein) and optionally profile ADMET, helping prioritize which molecules to synthesize and test first in a drug discovery pipeline. This skill runs and connects to the **refua-mcp** MCP server, which exposes Refua’s “unified Complex API” as MCP tools for: - **Boltz2** complex folding (+ optional affinity evaluation) - **BoltzGen** design workflows - Optional **ADMET** profiling (when installed) Clawdbot supports MCP natively, so the only requirement is running this MCP server and calling its tools. ([github.com](https://github.com/agentcures/refua-mcp)) --- ## When to use Use this skill when you need to: - Fold a **protein–ligand**, **protein–protein**, or (fold-only) **DNA/RNA** complex - Estimate **binding affinity** for a specified binder within a complex spec - Run **ADMET** predictions for one or more SMILES ligands (if enabled) - Execute GPU/CPU-heavy Refua workflows via MCP tool calls Do NOT use this skill when: - The task is a simple deterministic calculation (prefer a non-ML tool) - The user expects you to invent sequences/SMILES (request inputs instead) - The user requests unsafe wet-lab or clinical guidance --- ## Installation & assets (operator steps) ### 1) Install Refua + refua-mcp Install Refua (CPU or CUDA), then install the MCP server package: ([github.com](https://github.com/agentcures/refua-mcp)) - GPU support: - `pip install refua[cuda]` - CPU-only: - `pip install refua` - MCP server: - `pip install refua-mcp` ### 2) Optional: enable ADMET ADMET tool support is optional and requires an extra: ([github.com](https://github.com/agentcures/refua-mcp)) - `pip install refua[admet]` ### 3) Download model/assets Boltz2 and BoltzGen require model/molecule assets. Refua can download them automatically: ([github.com](https://github.com/agentcures/refua-mcp)) - `python -c "from refua import download_assets; download_assets()"` Default asset locations + overrides: ([github.com](https://github.com/agentcures/refua-mcp)) - **Boltz2** uses `~/.boltz` by default - Override via tool option `boltz.cache_dir` if needed - **BoltzGen** uses a bundled HF artifact by default - Override via tool option `boltzgen.mol_dir` if needed --- ## Running the MCP server Start the server using the module entrypoint: ([github.com](https://github.com/agentcures/refua-mcp)) ```bash python3 -m refua_mcp.server ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: as1113435/conversation-focus (version 1.0.0)

--- name: conversation-focus description: 对话焦点管理 + 需求澄清引导。每次对话开始时自动分析用户意图，确保需求清晰、限制明确，避免模型分散注意力。自动对接到 self-improving 进行持续优化。 tags: conversation-management,clarity,focus,prompting,context-optimization --- # Conversation Focus（对话焦点管理） ## 核心功能 在每次对话开始时，快速完成"需求澄清三问"： 1. **用户要什么？** — 明确任务目标 2. **有什么限制？** — 时间、格式、预算、偏好 3. **成功标准是什么？** — 交付什么、如何验收 ## 使用场景 - 用户提出模糊需求时 - 涉及多步骤、长周期任务时 - 需要确认预算、时间、格式等关键约束时 - 对话变得分散、偏离主题时 ## 工作流程 ### 步骤1：快速检测（自动） 对话开始时，检测是否存在以下"模糊信号"： - "帮我弄一下"、"搞个东西" 类模糊描述 - 多任务混合（一次说3件以上的事） - 缺少约束条件（不说预算、不说截止时间） ### 步骤2：澄清引导（触发式） 发现模糊信号时，自动输出： ``` 📋 需求确认 为了准确理解你的需求，请帮我确认： 🎯 目标：你想要完成什么？ 📐 标准：交付物是什么样子？ ⏰ 限制：预算/时间/格式有要求吗？ （如果都清楚，直接告诉我即可） ``` ### 步骤3：生成聚焦提示词 将澄清后的需求，整合成干净的上下文： ``` ## 当前任务 [清晰描述] ## 约束条件 - 预算：[确认值] - 截止：[确认值] - 格式：[确认值] ## 成功标准 [可验收的交付物描述] ``` ## 与其他组件的集成 | 组件 | 集成方式 | |------|---------| | **self-improving** | 每次澄清对话后，记录用户原始需求的模糊程度到 `corrections.md`，供自我优化 | | **thought-retriever** | 触发五步循环，提炼"如何引导模糊需求"的最佳实践 | | **prompt-optimizer-chinese** | 可以调用它做二次精炼 | ## 自动触发条件 满足以下任一条件时，自动启用澄清引导： - 用户消息 < 20字 且 无具体名词/动词 - 用户消息包含 "随便"、"弄一下"、"搞个" 等模糊词 - 检测到 > 3个不同任务在同一消息中 ## 注意事项 - 只在必要时触发，避免过度打扰 - 保持口语化、简洁 - 如果用户已经说清楚了，直接进入执行 ## 自我优化接口 ```python # 每次澄清后自动调用 def log_clarity_feedback(original_query, clarity_issues, resolution): """记录到 self-improving/corrections.md""" pass ``` ## 版本历史 - v1.0.0 (2026-05-02)：初始版本，基于赵匡的实际使用场景创建

path: _meta.json
content: {
  "name": "conversation-focus",
  "slug": "conversation-focus",
  "version": "1.0.0",
  "description": "对话焦点管理 + 需求澄清引导。让模型每次通话时把需求和限制搞清楚，减少分散注意力。自动对接到 self-improving 进行持续优化。",
  "tags": ["conversation-management", "clarity", "focus", "prompting", "context-optimization"],
  "author": "as1113435",
  "license": "MIT-0"
}

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