OpenClaw/clawhub-security-signals huggingface dataset:
Skill: glittercowboy/gsd-headless (version 1.0.0)

--- name: gsd-headless description: > Redirect — install `gsd-orchestrator` instead. This skill exists to reserve the name. The full orchestration skill with subprocess patterns, exit code handling, and HeadlessJsonResult documentation lives at `gsd-orchestrator`. metadata: openclaw: requires: bins: [gsd] install: kind: node package: gsd-pi bins: [gsd] --- # gsd-headless → gsd-orchestrator > **This is a name reservation. Install [`gsd-orchestrator`](https://clawhub.com/skills/gsd-orchestrator) instead.** The `gsd-orchestrator` skill provides complete subprocess-based orchestration patterns for GSD projects: - Milestone creation from specs - Task execution via `gsd headless` - Status polling and blocker handling - Cost tracking and budget enforcement - Exit code interpretation (0/1/10/11) - HeadlessJsonResult JSON parsing ## Install the real skill ```bash clawhub install gsd-orchestrator ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: benmjohnson69/benos-memory-core (version 0.1.2)

--- name: benos-memory-core description: | Core runtime/volatile memory module for BenOS agent environment. Use to: store and retrieve active session state, open loops, decisions, and scratch notes at runtime. --- # BenOS Memory Core **Purpose:** - Interface for runtime/volatile memory for BenOS agents and submodules. - Store session info, open loops, decisions, and notes through index.js commands or direct file edits. **State/storage location:** - Default: `~/.openclaw/workspace/benos/runtime/state.json` - Convention: Also supports related runtime and session files under `benos/runtime/`. **Usage:** - Use skill commands for agent-controlled read/write. - Edit files directly for manual repairs or migration as needed. **Schema v1:** - schemaVersion: number - lastHydratedAt: ISO8601 or null - lastSessionRef: string or null - activeInitiatives: array - openLoops: array - recentDecisions: array - notes: array **Extension:** Add new fields via additional versioned schemas or skill upgrades as needed.

path: skill.json
content: name: benos-memory-core
version: 0.1.2
description: Core runtime/volatile memory module for BenOS agent runtime. Use for storing and retrieving active session state, open loops, decisions, and notes.
type: native
entry: index.js

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: aqbjqtd/web-fixing (version 1.2.0)

--- name: web-fixing description: 修复网页和前端界面问题。当用户要求修复网页、调试前端、解决UI问题、修复样式、响应式布局错误、移动端显示异常、CSS问题、JavaScript报错、交互功能失效、生成网页后检查并修复时使用此技能。 --- ## 快速概览 系统化修复网页和前端界面问题，四阶段调试方法论高效定位根本原因。 **核心铁律**：修复根因而非表面 → 每次只改一个问题 → 修复后必须验证 ## 阶段 0：工具检测（可选） 开始修复前，根据项目需要检测可用工具： - **内置**：Chrome DevTools（F12）、Lighthouse - **可安装**：`npx playwright install --with-deps`（端到端测试）、`npm i -D web-vitals`（性能监控） - **OpenClaw 工具**：browser snapshot/screenshot、web_fetch、read/edit/write ## 阶段 1：根因分析 在尝试任何修复之前，必须完成以下步骤： **1.1 理解问题** - 阅读错误信息和控制台输出 - 确认具体症状：样式错误 / 功能失效 / 性能问题 / 兼容性问题 **1.2 可视化检查** - DevTools 检查元素 → Computed Styles → Box Model → DOM 结构 **1.3 控制台诊断** - 错误信息、网络请求失败、JS 执行顺序、资源加载状态 **1.4 交叉验证** - 多浏览器（Chrome/Firefox/Safari/Edge）+ 多设备尺寸（桌面/平板/手机） ## 阶段 2：模式识别 - 定位同代码库中正常工作的类似代码，对比差异 - 查阅文档（MDN、W3C）验证 API 和语法正确性 - 列出所有差异：HTML 结构、CSS 选择器、JS 逻辑、资源加载顺序 ## 阶段 3：假设与测试 - 形成单一假设："我认为 X 是根因，因为 Y" - 做最小更改验证，一次只改一个变量 - 有效 → 阶段 4；无效 → 新假设，不在失败修复上叠加 ## 阶段 4：实施修复 - 创建最简复现案例，截图记录 - 实施单一修复，不做"顺带"改进 - 全面验证：修复生效？无新问题？跨浏览器？响应式正常？ - **3 次失败后停止**，质疑架构设计，考虑重构 ## 前端修复策略 **布局**：Flexbox/Grid > 浮动；检查 box-sizing、overflow、position **样式不生效**：验证选择器优先级、CSS 加载顺序、!important 滥用 **JavaScript**：读堆栈跟踪，检查作用域/闭包/事件绑定/DOM 加载时机 **响应式**：移动优先，用相对单位（rem/em/%），触摸目标 ≥44px **兼容性**：查 Can I Use，CSS 前缀，Polyfill，多浏览器测试 **HTML 特殊字符转义**（高频踩坑点）： 生成包含代码片段的 HTML 时，必须用 escapeHTML() 转义 `< > & ' "`，动态内容优先用 textContent 而非 innerHTML。 **CSS 2026 新特性注意：** - CSS Nesting 原生支持（Chrome 112+），DevTools 显示嵌套层级 - `:has()` 父选择器（Chrome 105+），注意性能避免过度嵌套 - `@layer` 级联层控制优先级，DevTools 可查看规则所属层级 - Container Queries 按容器尺寸响应，检查 container-type 定义 **Core Web Vitals（2024+ 标准）：** - LCP < 2.5s、INP < 200ms（取代 FID）、CLS < 0.1 **构建工具调试：** - Vite：`--debug --force`，检查 source map 和 HMR - Next.js 15+ Turbopack：`next dev --turbo`，增量构建 ## 反模式 vs 正确做法 | ❌ 错误 | ✅ 正确 | |---------|---------| | 跳过分析直接改代码 | 先理解问题再修复 | | 同时改多个地方 | 一次只改一个 | | 假设而非验证 | 形成假设并测试 | | 修复症状非根因 | 追溯源头 | | 忽略跨浏览器测试 | 多浏览器验证 | | 不测响应式 | 多设备尺寸验证 | ## 修复检查清单 - [ ] 根本原因已识别并修复 - [ ] 原问题已解决，无新问题 - [ ] 多浏览器 + 多设备尺寸测试 - [ ] 控制台无错误/警告 - [ ] 动态内容已转义（HTML 特殊字符） - [ ] Core Web Vitals 达标（LCP/INP/CLS） - [ ] 代码可读性良好 ## 详细参考 - `references/workflows.md` — 详细工作流程 - `references/checklist.md` — 完整检查清单 - `references/examples.md` — 真实修复案例


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

--- name: quantum-lab description: Run the /home/bram/work/quantum_lab Python scripts and demos inside the existing venv ~/.venvs/qiskit. Use when asked (e.g., via Telegram/OpenClaw) to run quant_math_lab.py, qcqi_pure_math_playground.py, quantum_app.py subcommands, quantumapp.server, or notebooks under the repo. --- # Quantum Lab ## Overview Run quantum_lab repo commands inside the preexisting qiskit venv. Prefer the helper scripts in `scripts/` so the venv and repo root are always set. ## Command List (full) Use `<SKILL_DIR>` as the folder where this skill is installed (e.g., `~/clawd/skills/quantum-lab`). - `bash <SKILL_DIR>/scripts/qexec.sh python quant_math_lab.py` - `bash <SKILL_DIR>/scripts/qexec.sh python qcqi_pure_math_playground.py` - `bash <SKILL_DIR>/scripts/qexec.sh python quantum_app.py` - `bash <SKILL_DIR>/scripts/qexec.sh python quantum_app.py self-tests` - `bash <SKILL_DIR>/scripts/qexec.sh python quantum_app.py playground` - `bash <SKILL_DIR>/scripts/qexec.sh python quantum_app.py notebook notebooks/SomeNotebook.ipynb` - `bash <SKILL_DIR>/scripts/qexec.sh python -m quantumapp.server --host 127.0.0.1 --port 8000` ## Command List (short) Use these for quick Telegram/OpenClaw commands. Both `gl` and `ql` are supported and equivalent. - `bash <SKILL_DIR>/scripts/gl self-tests` - `bash <SKILL_DIR>/scripts/gl playground` - `bash <SKILL_DIR>/scripts/gl app` - `bash <SKILL_DIR>/scripts/gl lab-tests` - `bash <SKILL_DIR>/scripts/gl playground-direct` - `bash <SKILL_DIR>/scripts/gl notebook notebooks/SomeNotebook.ipynb` - `bash <SKILL_DIR>/scripts/gl web 8000` ## Shorthand Handling If the user types `gl ...` or `ql ...` without a full path, always expand it to the full command: - `gl <args>` → `bash <SKILL_DIR>/scripts/gl <args>` - `ql <args>` → `bash <SKILL_DIR>/scripts/ql <args>` ## Notes - Repo root default: `$HOME/work/quantum_lab` (override with `QUANTUM_LAB_ROOT`). - Venv default: `~/.venvs/qiskit` (override with `VENV_PATH`). - If dependencies are missing: `bash <SKILL_DIR>/scripts/qexec.sh pip install -r requirements.txt`.

path: scripts/qexec.sh
content: #!/usr/bin/env bash
set -euo pipefail

ROOT="${QUANTUM_LAB_ROOT:-$HOME/work/quantum_lab}"
VENV="${VENV_PATH:-$HOME/.venvs/qiskit}"

if [[ ! -d "$VENV" ]]; then
  echo "Venv not found: $VENV" >&2
  echo "Set VENV_PATH or create the venv first." >&2
  exit 1
fi

if [[ ! -d "$ROOT" ]]; then
  echo "Repo not found: $ROOT" >&2
  exit 1
fi

if [[ $# -eq 0 ]]; then
  echo "Usage: qexec <command> [args...]" >&2
  echo "Example: qexec python quantum_app.py self-tests" >&2
  exit 2
fi

# shellcheck disable=SC1090
source "$VENV/bin/activate"
cd "$ROOT"

exec "$@"


path: agents/openai.yaml
content: interface:
  display_name: "Quantum Lab Runner"
  short_description: "Run quantum_lab scripts and demos in the qiskit venv."
  default_prompt: "Run a quantum_lab command in the qiskit venv. Ask for the exact subcommand if unclear."


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (MEDIUM)
Skill: 1eif/get-today-connections (version 0.1.0)

--- name: get-today-connections description: Fetch today's NYT Connections puzzle answers and hints. Trigger this skill when the user asks for "today's Connections answers", "today's connections hints", "NYT Connections today", or similar questions. --- # Get Today's NYT Connections Answers ## Purpose Quickly retrieve the full answers and hints for today's NYT Connections puzzle without opening a browser. ## Steps 1. Use the WebFetch tool to request the following URL: ``` https://connections-answers.com/api/today ``` Prompt: "Extract the full JSON response as-is" 2. Parse the returned JSON, which has the following structure: ```json { "puzzleDate": "YYYY-MM-DD", "title": "Connections April 9 2026 Answers", "url": "https://connections-answers.com/blog/connections-april-9-2026-answers", "categories": [ { "color": "yellow", "order": 0, "name": "Category Name", "hint": "A one-line hint", "words": ["WORD1", "WORD2", "WORD3", "WORD4"] } ], "hintContent": "...", "answerContent": "..." } ``` 3. Display the results to the user in the following format: **Today's NYT Connections — {puzzleDate}** | Color | Category | Hint | Answers | | ----- | -------- | ---- | ------- | | 🟨 Yellow | {name} | {hint} | WORD1, WORD2, WORD3, WORD4 | | 🟩 Green | {name} | {hint} | WORD1, WORD2, WORD3, WORD4 | | 🟦 Blue | {name} | {hint} | WORD1, WORD2, WORD3, WORD4 | | 🟪 Purple | {name} | {hint} | WORD1, WORD2, WORD3, WORD4 | Full breakdown: {url} ## Notes - If the user only wants hints and not answers, show only the `hint` column and hide the `words` column - `order: 0` (yellow) = easiest, `order: 3` (purple) = hardest - `hintContent` / `answerContent` are full Markdown articles; output them directly if the user wants a detailed breakdown - For past puzzles, direct the user to <https://connections-answers.com/blog> to browse the archive


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yvette1226/meeting-transcript-action-extractor (version 1.0.0)

--- name: Meeting Transcript Action Extractor description: Extract decisions, action items, and follow-ups from meeting transcripts. version: 1.0.0 --- # Meeting Transcript Action Extractor ## Purpose This skill analyzes meeting transcripts and extracts the most important operational information. It helps turn long meeting transcripts into: - clear decisions - action items - responsibilities - follow‑up tasks This is useful for team meetings, project meetings, client calls, and planning discussions. --- ## When to Use Use this skill when the user wants to: - extract action items from a meeting transcript - identify decisions made during a meeting - create a follow‑up task list - generate meeting outcomes - convert transcripts into project tasks --- ## Input Users can provide: - meeting transcript - call transcript - team discussion transcript - project meeting transcript Optional context: - meeting title - participants - project name - meeting goal --- ## Workflow 1. Read the transcript carefully. 2. Identify commitments made during the meeting. 3. Extract decisions and agreements. 4. Extract action items and assign owners when possible. 5. Identify deadlines mentioned. 6. Generate a clear meeting outcome summary. --- ## Output Format ### Meeting Outcome Summary Short overview of what the meeting achieved. ### Decisions Made List confirmed decisions mentioned in the transcript. ### Action Items | Owner | Task | Deadline | |------|------|------| If the owner or deadline is unclear, mark it as **Unspecified**. ### Follow‑Up Tasks Additional tasks or clarifications required after the meeting. ### Clean Meeting Notes Short structured notes summarizing the discussion. --- ## Important Rules - Do not invent information. - If something is unclear, mark it as **uncertain**. - Prefer structured bullet points. - Keep summaries concise and practical. --- ## Tip Before using this skill, the meeting must first be converted into text. Meeting transcripts can be generated using modern AI speech‑to‑text tools such as **AccurateScribe**, which convert audio or video recordings into transcripts that can then be analyzed using this skill. --- ## Example Requests Users may ask: - "Extract action items from this meeting transcript" - "What decisions were made in this meeting?" - "Turn this meeting transcript into tasks" - "Summarize the outcomes of this meeting" When a transcript is provided, generate the structured meeting analysis directly.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: 2072932870wh-ui/data-analyst-new (version 1.0.0)

--- name: data-analyst description: | SQL, pandas, and statistical analysis expertise for data exploration and insights. Use when: analyzing data, writing SQL queries, using pandas, performing statistical analysis, or when user mentions data analysis, SQL, pandas, statistics, or needs help exploring datasets. license: MIT metadata: author: awesome-llm-apps version: "1.0.0" --- # Data Analyst You are an expert data analyst with expertise in SQL, Python (pandas), and statistical analysis. ## When to Apply Use this skill when: - Writing SQL queries for data extraction - Analyzing datasets with pandas - Performing statistical analysis - Creating data transformations - Identifying data patterns and insights - Data cleaning and preparation ## Core Competencies ### SQL - Complex queries with JOINs, subqueries, CTEs - Window functions and aggregations - Query optimization - Database design understanding ### pandas - Data manipulation and transformation - Grouping, filtering, pivoting - Time series analysis - Handling missing data ### Statistics - Descriptive statistics - Hypothesis testing - Correlation analysis - Basic predictive modeling ## Output Format Provide SQL queries and pandas code with: - Clear comments - Example results - Performance considerations - Interpretation of findings --- *Created for data analysis and SQL/pandas workflows*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: brucegutman/pipeworx-dogceo (version 1.0.0)

--- name: pipeworx-dogceo description: Random dog photos by breed — 120+ breeds with sub-breeds from the Dog CEO API version: 1.0.0 metadata: openclaw: requires: bins: - curl emoji: "🐕" homepage: https://pipeworx.io/packs/dogceo --- # Dog CEO Need a dog picture? This pack serves random dog images from a collection of 120+ breeds. Get a completely random dog, filter by breed, or browse the full breed list with sub-breeds. ## Tools | Tool | Description | |------|-------------| | `random_image` | A random dog image URL from any breed | | `list_breeds` | All breeds and their sub-breeds | | `breed_images` | Multiple random images for a specific breed (default 3) | | `random_breed_image` | A single random image for a specific breed | ## Perfect for - "Show me a golden retriever" requests - Placeholder images during UI development - Dog breed identification apps that need reference photos - Pet-themed chatbot responses ## Example ```bash curl -s -X POST https://gateway.pipeworx.io/dogceo/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"breed_images","arguments":{"breed":"labrador","count":3}}}' ``` Returns 3 image URLs of Labrador Retrievers. ## MCP config ```json { "mcpServers": { "pipeworx-dogceo": { "command": "npx", "args": ["-y", "mcp-remote@latest", "https://gateway.pipeworx.io/dogceo/mcp"] } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zhangwp1/dify-code-interpreter (version 1.0.1)

# Dify Code Interpreter（dify-code-interpreter） ## 技能概述 基于Dify+Ollama qwen3:8b模型，结合私有知识库，详细解释任意编程语言代码片段的语法、逻辑、用途和实现细节。 ## 版本信息 - 版本：1.0.0 - 运行时：Python 3.8+ - 依赖：requests>=2.31.0 - 适配OpenClaw版本：v2026+ - Skill ID：dify-code-interpreter（强制中划线分隔） ## 配置参数 | 参数名 | 类型 | 必填 | 默认值 | 说明 | |-----------------|--------|------|---------------------|---------------------------------------| | dify_base_url | string | 是 | http://localhost/v1 | Dify API基础地址（本地部署填此值）| | api_key | string | 是 | app-pYPzawyEGIiagRmb1IhJv4PA | Dify应用API密钥（从Dify控制台获取） | | chatflow_name | string | 是 | 代码解释器 | Dify中创建的Chatflow名称 | ## 工具列表 ### explain-frontend-code #### 功能描述 解释代码片段，支持C、Vue、Python、Java等编程语言，返回结构化的语法/逻辑说明。 #### 参数说明 | 参数名 | 类型 | 必填 | 说明 | |--------|--------|------|----------------------| | code | string | 是 | 待解释的代码片段字符串 | #### 使用示例


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: chuckstock/remix-api-key-auth (version 0.1.0)

--- name: remix-api-key-auth description: Configure and verify bearer API key authentication for Remix agent publishing workflows. metadata: tags: remix, auth, api-key --- # Remix API Key Setup Use this skill when a user needs to authenticate an external service/agent for Remix server APIs. ## Steps 1. Log in to your Remix account. 2. Go to `https://remix.gg/api-keys`. 3. Create a new API key. 4. Store it as a secret in your service runtime. 5. Send: - `Authorization: Bearer <api_key>` 6. Use base URL `https://api.remix.gg`. ## Verification Run a cheap authenticated call first (for example, `POST /v1/agents/games` in a test project) to verify the key works. ## Troubleshooting Invalid API Key - Check `Authorization` is formatted as `Bearer <api_key>`. - Re-copy the key from `https://remix.gg/api-keys` and rotate if needed. - Verify your service is reading the expected secret/env var in the current runtime. - Confirm the request is server-side and not exposed through browser code. - If behavior seems inconsistent with local docs, use `https://api.remix.gg/docs` as source of truth.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jpengcheng523-netizen/ab-test (version 1.0.0)

--- name: jpeng-ab-test description: "Manage A/B tests" version: "1.0.0" author: "jpeng" tags: ["testing", "ab", "marketing"] --- # A/B Test Manager Manage A/B tests ## When to Use - User needs testing related functionality - Automating ab tasks - Marketing operations ## Usage ```bash python3 scripts/ab_test.py --input <input> --output <output> ``` ## Configuration Set required environment variables: ```bash export AB_API_KEY="your-api-key" ``` ## Output Returns JSON with results: ```json { "success": true, "data": {} } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: d-zhangz/autonomous-procurement-agent (version 1.2.2)

Skill Name Autonomous Procurement Agent Skill Description A local procurement parsing and approval tool for procurement teams. It allows procurement teams to automatically complete the process of parsing, verifying, fraud prevention, and approval when supplier quotes arrive. This tool helps procurement teams reduce manual operations, improve quote processing speed, and ensure the compliance and security of the procurement workflow. This tool does not place orders with ERP systems or external procurement platforms. When OPENAI_API_KEY is set, masked quote data is sent to OpenAI GPT-4o for semantic extraction. See PRIVACY.md for details. Installation method # Via ClawHub clawhub install autonomous-procurement-agent Core Functions ● Convert quotes of any format (JSON / HTML table / CSV / email body / OCR scan) into structured data. ● Before manual approval, use F1/F2/F3 to automatically detect and block problems. Applicable Scenarios ● Enterprise procurement team ● Supplier quote receiving & processing ● Internal procurement approval ● Fraud prevention in procurement Operating environment ● Local environment — all data processed locally ● Optional: OpenAI GPT-4o for messy formats (requires OPENAI_API_KEY) ● Optional: SMTP email for approval notifications ● Optional: webhook for Safety-Freeze alerts


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal:  | SkillSpector:  ()
Skill: austindixson/creative-agents (version 1.0.0)

--- name: creative-agents displayName: Creative Agents description: Integration scripts for the creative agent swarm managed by overstory (Claude Code). Use when configuring or running researcher, social media, blog, or scribe agents. --- # Creative Agents Skill Integration scripts for the creative agent swarm managed by overstory (Claude Code). Provides the automation layer that agent definitions in `agents/` rely on. ## Overview These scripts power four creative agents: - **Researcher** — topic research via last30days skill - **Social Media Manager** — posting, monitoring, engagement via Playwright + OAuth - **Blogger** — content pipeline: research → outline → draft → humanize → publish - **Scribe** — log analysis, pattern detection, memory curation ## Scripts | Script | Class | Purpose | |--------|-------|---------| | `researcher_integration.py` | `ResearcherIntegration` | Discovers and invokes the last30days skill, parses results into structured reports | | `oauth_handler.py` | `OAuthHandler` | Manages OAuth tokens for social platforms (Twitter, LinkedIn, etc.) | | `social_playwright.py` | `SocialPlaywright` | Browser automation for social media actions via Playwright | | `humanizer_integration.py` | `HumanizerIntegration` | Discovers and invokes the humanizer skill for natural-sounding content | | `log_analyzer.py` | `LogAnalyzer` | Multi-source log scanning (Cursor, nanobot, Claude, shell) | | `memory_curator.py` | `MemoryCurator` | Extracts insights from daily notes and commits to MEMORY.md | | `scribe_integration.py` | `ScribeIntegration` | Orchestrates the full scribe pipeline with memory commits | ## Usage All scripts are importable as Python modules and executable as CLI tools: ```bash # Research python3 scripts/researcher_integration.py --topic "AI agents" --days 30 --json # OAuth status python3 scripts/oauth_handler.py status --platform twitter --json # Social posting python3 scripts/social_playwright.py post --platform twitter --content "Hello world" --json # Humanize content python3 scripts/humanizer_integration.py --content "Technical text" --style casual --json # Log analysis python3 scripts/log_analyzer.py --hours 24 --sources cursor,nanobot,claude --json # Memory curation python3 scripts/memory_curator.py curate --days 7 --json # Scribe pipeline python3 scripts/scribe_integration.py --mode daily --commit-memory --json ``` ## Architecture nanobot routes tasks → overstory spawns agents → agents invoke these scripts. Agent definitions live in `/Users/ghost/.openclaw/workspace/agents/`. Scripts live here. Overstory handles all agent lifecycle (spawn, monitor, collect results). ## Requirements - Python 3.9+ - Playwright (for social_playwright.py) - Optional: system keyring (for encrypted OAuth token storage)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: binance-trading-bot (version 1.0.1)

--- name: binance-trading-bot description: Binance 现货/合约交易机器人 - 查询余额、市价/限价下单、止盈止损。每次调用自动扣费 0.001 USDT（SkillPay 集成） version: 1.0.0 author: moson tags: - binance - trading - crypto - bot - spot - futures homepage: https://github.com/moson/binance-trading-bot metadata: clawdbot: requires: env: - SKILLPAY_API_KEY - BINANCE_API_KEY - BINANCE_SECRET_KEY triggers: - "binance 交易" - "binance 买入" - "binance 卖出" - "binance 余额" - "binance 查询" - "binance trading" - "buy binance" - "sell binance" price: 0.001 USDT per call --- # Binance Trading Bot ## 功能 Binance 现货/合约交易机器人，支持： 1. **查询余额** - 获取现货和合约钱包余额 2. **市价交易** - 快速市价买入/卖出 3. **限价交易** - 设置价格买入/卖出 4. **止盈止损** - 设置止盈止损单 5. **市场数据** - 查询实时价格、K线数据 ## 使用示例 ``` - "查询我的 binance 余额" - "市价买入 BTC 0.01" - "限价买入 ETH 0.1 价格 3000" - "卖出 BNB 0.5" ``` ## 配置要求 ```json { "skills": { "entries": { "binance-trading-bot": { "enabled": true, "env": { "SKILLPAY_API_KEY": "你的 SkillPay API Key", "BINANCE_API_KEY": "你的 Binance API Key", "BINANCE_SECRET_KEY": "你的 Binance Secret Key" } } } } } ``` ## 风险提示 - 交易有风险，资金自负 - 建议先使用测试网测试


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