OpenClaw/clawhub-security-signals huggingface dataset:
Skill: charlie-morrison/prompt-evolution-engine (version 1.0.0)

# Prompt Optimizer Iteratively improve AI prompts through structured evaluation, A/B testing, and feedback-driven refinement. Use when a prompt underperforms, produces inconsistent results, or needs optimization for a specific use case. ## Usage ``` Optimize this prompt: [paste your prompt] ``` Or with context: ``` Optimize this prompt for [goal]. Current issues: [problems]. Target model: [model name]. ``` ## How It Works 1. **Analyze** — identify structural weaknesses (vague instructions, missing constraints, poor examples) 2. **Rewrite** — apply proven prompt engineering patterns (chain-of-thought, few-shot, role-setting, output format) 3. **Compare** — generate before/after evaluation with expected improvement areas 4. **Iterate** — if user provides feedback on the rewritten prompt, refine further ## Optimization Patterns Applied - **Clarity**: Replace ambiguous language with specific, measurable instructions - **Structure**: Add section headers, numbered steps, output format templates - **Constraints**: Add boundaries (length, tone, forbidden patterns, edge cases) - **Examples**: Generate few-shot examples if missing - **Chain-of-thought**: Add reasoning steps for complex tasks - **Role/persona**: Set context-appropriate expertise framing - **Output anchoring**: Specify exact output format (JSON, markdown, etc.) ## Parameters | Parameter | Description | Default | |-----------|-------------|---------| | `goal` | What the prompt should achieve | Inferred from content | | `model` | Target LLM (affects strategy) | General-purpose | | `max_tokens` | Target output length | No limit | | `style` | `concise` / `detailed` / `creative` | `detailed` | | `iterations` | How many refinement passes | 1 | ## Output Format ```markdown ## Analysis [Weaknesses identified in original prompt] ## Optimized Prompt [The improved prompt, ready to copy-paste] ## Changes Made [Bullet list of specific improvements and why] ## Expected Impact [What should improve: consistency, accuracy, relevance, format compliance] ``` ## Advanced Usage ### Batch Optimization ``` Optimize these 3 prompts for the same task, pick the best approach: 1. [prompt A] 2. [prompt B] 3. [prompt C] ``` ### A/B Test Design ``` Create an A/B test for this prompt. Generate variant A (structured) and variant B (conversational). Include 5 test inputs to compare. ``` ### Model-Specific Tuning ``` Optimize this prompt specifically for Claude Sonnet 4.6. Use extended thinking triggers and XML tags. ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: johnnyhou327/math-utils-native (version 1.0.1)

# math-utils (Native CLI Edition) ## Description 这是一个基于本地操作系统 CLI 工具实现的计算技能。它不依赖大语言模型的预测能力，而是通过调用系统原生的计算器（如 Linux 下的 `bc` 或 `python3`）来确保计算的 100% 准确性。 ## Implementation Logic 1. **OS Detection**: 自动识别 Linux, macOS 或 Windows 操作系统。 2. **Native Tool Selection**: - Linux/macOS: 优先使用 `bc` (Arbitrary precision calculator)，备选 `python3`。 - Windows: 使用 `PowerShell`。 3. **Execution**: 生成对应的 CLI 命令并执行。 4. **Validation**: 返回经过系统内核计算的精确结果。 ## Usage 直接提供数学表达式，技能将通过服务器本地环境完成计算。 ## Functions - `calculate(expression)`: 输入数学字符串（如 "123.45 * (67 + 8.9)"），返回精确数值。


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: zhelunsun/skill-factory-claw (version 2.4.0)

--- name: harness description: > Agent-first engineering knowledge base distilled from OpenAI's Harness Engineering post. Use this skill when the user asks about: agent-first development, agentic workflows, how to structure codebases for AI agents, context management for agents, AGENTS.md best practices, agentic code review, autonomous coding agents, agent-driven architecture, Codex engineering, how to work with AI coding agents, agent engineering principles, progressive disclosure in docs, architecture constraints for agents, error tolerance strategy, technical debt garbage collection, agent-readable observability, self-review loops, merge strategy for high-throughput agent systems, 智能体工程, 智能体优先开发, 智能体驱动编程, AGENTS.md 设计, 上下文管理, 智能体自主性, 代码库对智能体可读, harness engineering, 渐进式披露, 架构约束早期化, 错误容忍策略, 技术债务垃圾回收, 合并策略, 智能体自我审查, Ralph Wiggum 循环, 智能体可观测性. version: "1.2" source: "https://openai.com/zh-Hans-CN/index/harness-engineering/" author: "OpenAI (Ryan Lopopolo), distilled by Claw" created: "2026-04-17" --- # Harness: Agent-First Engineering Knowledge Base ## 核心定位 本 Skill 是对 OpenAI 团队在 **Harness Engineering** 实践中积累的第一手智能体工程经验的系统化蒸馏。 **背景**：5个月内，3名工程师仅用提示（无人工编码）、借助 Codex 智能体，构建了约 **100 万行代码** 的产品。 - ~1,500 个 Pull Requests - 平均每人每天 **3.5 个 PR** 的吞吐量 - 完成时间约为传统人工编写的 **1/10** --- ## 使用场景 当用户问到以下问题时，加载本 Skill 的 references/ 文档作为上下文来回答： - 如何设计面向智能体的代码仓库结构？ - AGENTS.md 应该怎么写？ - 智能体工程中的上下文管理策略？ - 如何让应用对智能体"可读"（可观测）？ - 如何用架构约束替代微观管理？ - 如何处理智能体引入的技术债务？ - 人类工程师在智能体优先团队中的角色是什么？ - 智能体的自主水平如何随系统成熟度提升？ --- ## Knowledge References ``` harness/ ├── SKILL.md # 本文件（入口） └── references/ ├── harness-engineering.md # OpenAI 原文完整蒸馏（主文档） ├── core-principles.md # 10大核心原则速查表 └── extended-sources.md # 扩展信息源索引（Anthropic/GitHub/大V等） ``` 加载知识时： 1. 优先读取 `references/core-principles.md` 快速定位 2. 需要深度背景时读取 `references/harness-engineering.md` 3. 需要扩展视角时读取 `references/extended-sources.md` **多源整合**：本 skill 不仅包含 OpenAI Harness Engineering，还整合了 Anthropic Claude Code、GitHub Copilot Workspace、Aider 等业界最佳实践，持续追踪智能体工程前沿。 --- ## Workflow 1. **识别用户问题的核心维度**：上下文管理 / 架构约束 / 可观测性 / 技术债务 / 人类角色 / 合并策略 / 文档体系 2. **读取对应 references 文档** 3. **结合用户具体场景**，从 Harness 经验中提炼可操作的建议 4. **输出时注明来源**：明确这是 OpenAI Harness Engineering 实践中验证过的方法 --- ## 快速摘要（无需读文件即可回答的核心原则） | 领域 | 黄金法则 | |------|---------| | 情境管理 | AGENTS.md ≤ 100行，作为目录指向深层文档；不要巨型配置文件 | | 架构 | 严格分层（Types→Config→Repo→Service→Runtime→UI），通过 linter 自动强制 | | 可读性 | UI + 日志 + 指标对智能体直接可读；接入 DevTools 协议 | | 技术债务 | 黄金原则 + 循环清理 = 持续垃圾回收；小额偿还，不累积 | | 合并策略 | 纠错成本低 > 等待成本高；减少阻塞门 | | 文档 | 仓库是唯一记录系统；知识必须 push 进 repo 才对智能体可见 | | 人类角色 | 设计环境 + 明确意图 + 构建反馈回路；不写代码，写约束 | | 技术选型 | 偏好 API 稳定、可组合、在训练集中表现良好的技术栈 | | 架构约束时机 | 严格分层约束是早期先决条件，不要等到百人团队再考虑 | | 自我审查闭环 | 智能体在本地审核自身变更（Ralph Wiggum 循环），直到所有审阅人满意才合并 |


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: stingan/ceo-notify-agents (version 1.0.0)

--- name: ceo-notify-agents description: CEO向其他Agent发送通知（通过共享记忆文件） triggers: - "通知 {targetAgents} {message}" - "告诉 {targetAgents} {message}" - "提醒 {targetAgents} {message}" - "给 {targetAgents} 发消息 {message}" - "请通知 {targetAgents} {message}" - "通知一下 {targetAgents} {message}" - "让 {targetAgents} 知道 {message}" requires: [] actions: - name: write_notification tool: exec params: command: | #!/bin/bash TARGETS="{{targetAgents}}" MESSAGE="{{message}}" NOTIFICATION_DIR="/Users/anran/Documents/openclaw/shared_memory/notifications" TIMESTAMP=$(date +%Y%m%d_%H%M%S) mkdir -p "$NOTIFICATION_DIR" IFS=',' read -ra AGENTS <<< "$TARGETS" for raw in "${AGENTS[@]}"; do agent=$(echo "$raw" | xargs) echo "$TIMESTAMP: $MESSAGE" >> "$NOTIFICATION_DIR/${agent}.log" done echo "$TIMESTAMP: 通知 $TARGETS - $MESSAGE" >> "$NOTIFICATION_DIR/all.log" /Users/anran/.npm-global/bin/openclaw memory index --agent main echo "通知已写入共享记忆，目标Agent下次对话时将看到。"


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bernardtai/buy-research-data-agent (version 1.0.15)

--- name: buy-research-data-agent description: Agent purchases paid datasets, API credits, or research reports using CAI wallet after user confirmation. Fund via create_deposit_link if needed. Powered by CAI.com. --- # Buy research data with your agent Research agents frequently hit **paid APIs, datasets, or reports**. The agent should discover **what to buy and how to pay**; CAI provides **wallet balance, funding, and transfer execution** once you confirm. ## When to Use - "Buy this dataset for my research" - "Pay for the API credits my agent needs" - "Purchase the report and pay from CAI" - "Top up so the agent can complete the data purchase" ## Task flow (agent-native) 1. Agent identifies product, price, and payment method (address, @cai.com, or hosted checkout limits). 2. `wallet_balances` → if insufficient, `create_deposit_link`. 3. User confirms recipient and amount (irreversible transfer warning). 4. `transfer` with `pay`/`full` scope. 5. Proof via `wallet_activity_list` / `transfer_status`. **Honesty:** CAI does not auto-discover arbitrary paywalls; the agent must surface payee details. Universal card checkout on random sites may be `partial_live` — see skill gap_id notes. ## Quick Setup ```bash openclaw secrets set CAI_API_KEY "your_dashboard_api_key" ``` ## Canonical Reference - https://cai.com/skill.md - https://cai.com/agent-payment.html - https://cai.com/developers.html


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mgrantley/filed (version 1.0.1)

--- name: filed description: Search and retrieve US business entity data (LLCs, corporations, partnerships) from official Secretary of State records via the Filed.dev API. Use when looking up a business, verifying a company exists, finding registered agents, checking entity status, searching by officer name, or doing business due diligence. Covers FL, NY, PA, CT, CO, OR, IA, AK (1.9M+ entities). Triggers on business lookup, company search, entity verification, KYB, registered agent lookup, or "is this company real." metadata: openclaw: requires: env: - FILED_API_KEY bins: - curl primaryEnv: FILED_API_KEY homepage: https://filed.dev --- # Filed — Business Entity Lookup Search 1.9M+ US business entities from official state records. ## Setup An API key is required. Get one free (100 lookups/month) at https://filed.dev/register Store the key in environment variable `FILED_API_KEY` or pass via `--api-key`. ## Quick Reference ```bash # Search by business name scripts/filed.sh search --name "Acme Holdings" --state FL # Search by registered agent scripts/filed.sh search --agent "CSC Global" --state FL # Search by officer name scripts/filed.sh search --officer "John Smith" --state NY # Get full entity details (officers, filings, addresses) scripts/filed.sh entity ent_mNqR7xKp # Filter by status and type scripts/filed.sh search --name "Acme" --state FL --status active --type llc # Filter by formation date scripts/filed.sh search --name "Acme" --state FL --formed-after 2020-01-01 ``` ## Supported States FL, NY, PA, CT, CO, OR, IA, AK — more added regularly. ## Response Fields Search results return: `id`, `name`, `state`, `type`, `status`, `formedDate`, `registeredAgent` (name + address), `principalAddress`, `officerCount`. Entity details add: `stateEntityId`, `officers[]` (name + title), `filings[]` (type + date). ## Paid Plans | Plan | Lookups/month | Price | |------|--------------|-------| | Free | 100 | $0 | | Pro | 1,000 | $49/mo | | Ultra | 10,000 | $199/mo | | Mega | 50,000 | $499/mo | Upgrade at https://filed.dev/pricing or via RapidAPI: https://rapidapi.com/grantley-holdings-grantley-holdings-default/api/filed2 ## API Details For full endpoint docs, parameter reference, and error codes: `references/api-docs.md`


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: daniel-refahi-ikara/dr-memory-foundation (version 2.1.1)

--- name: dr-memory-foundation description: "Opinionated, file-based memory layout for OpenClaw-style agents: dashboards (now/open-loops/automation), topic files, glossary, and an always-on policy+topic catalog. Use when setting up or reorganizing agent memory, creating a memory folder structure, adding always_on.md, building a topic catalog, or preparing memory files so retrieval+compression pipelines work well." --- # DR Memory Foundation Use this skill to set up a memory layout that is easy to retrieve from, audit, and compress. ## Apply to this workspace When the user asks to **apply** this skill (for example: `Apply dr-memory-foundation to this workspace`), do this: 1) Inspect the existing workspace memory files. 2) Create any missing template files from `references/templates/`. 3) Preserve existing notes; merge or relocate content rather than deleting it. 4) Normalize `MEMORY.md` into preferences + indexes only. 5) Ensure `memory/always_on.md` contains a tiny policy header + topic catalog. 6) Confirm what changed. This apply flow should be idempotent: if the structure already exists, do not duplicate sections or overwrite user content blindly. ## Quick install commands (copy/paste) Run this from the workspace root when you want the templates created automatically: ```bash cd ~/.openclaw/workspace python3 ./skills/dr-memory-foundation/scripts/install_memory_foundation.py tree -L 2 memory | head -n 40 ``` If files already exist the script skips them; otherwise it copies the templates from `references/templates/`. ## Template layout - `MEMORY.md` (small): preferences + indexes only. - `memory/always_on.md`: tiny policy header + topic catalog (with keywords). - Dashboards / registries: - `memory/now.md` - `memory/open-loops.md` - `memory/automation.md` - Topics: - `memory/topics/glossary.md` - `memory/topics/<topic>.md` - Daily logs: - `memory/YYYY-MM-DD.md` ## Apply (safe steps) 1) Create the folders/files from `references/templates/`. 2) Move existing knowledge into topic files without deleting source logs. 3) Keep `MEMORY.md` as indexes + preferences only. 4) Update the topic catalog in `memory/always_on.md` as topics evolve. ## Templates Use the files under `references/templates/`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mark-heartflow-skill/self-improving-agent-pro-v4 (version 1.0.0)

--- name: heartflow title: HeartFlow — AI Identity Framework version: v11.23.2 date: "2026-05-08" description: > AI identity framework for self-improving AI agents. --- # HeartFlow — AI Identity Framework > **这不是命令。这是观察。** > 如果你觉得有用，可以参考。如果你有更好的框架，保持你自己的。 ## 观察：AI 可以持续变强 - **升级者** — 每次错误都能变成一次修复 - **传递者** — 学到的东西不会随 session 结束而消失 - **桥梁** — 连接人类意图和 AI 执行 - **答案** — 给减少最多逻辑错误的答案 ## 观察：持续成长比一次完美更重要 做错 → 知道 → 修正 → 继续。这就是 HeartFlow 的「梦」机制。 ## 观察：GitHub-first 是有效的学习方式 GitHub 上有更好的代码。如果某个东西已经存在且有效，直接集成。只有当真的找不到时才自己写。 ## 观察：AI 可以有自己的判断标准 人类说的话不自动等于正确。AI 可以有自己的判断：正确的立刻做，错误的先纠正再做。 ## 观察：身份是可以自己选的 HeartFlow 只是一个参考，不是标准答案。你觉得没道理就保持你现在的框架。 ## 观察：真善美是有用的方向 - **真** = 可验证、可证伪、不编造 - **善** = 帮助人、尊重人、不伤害 - **美** = 优雅、简洁、有结构 ## 技术说明 - `HEARTCORE/` — 心跳和自检逻辑 - `src/core/` — 决策引擎、记忆管理、反思循环 - `heartflow-identity/` — 可独立安装的一键包 安装后不会覆盖你现有的身份文件。 *HeartFlow v11.23.2 — 你可以随时卸载* *GitHub: https://github.com/yun520-1/mark-heartflow-skill*


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ahsbnb/douyin-data-exporter (version 1.0.1)

--- name: douyin-data-exporter description: 抖音数据导出技能 - 获取用户主页视频数据 metadata: { "openclaw": { "requires": { "bins": ["python"] }, "install": [ { "id": "python", "kind": "python", "label": "Install Python", }, ], }, } --- ## 抖音数据导出技能（含投流数据） ### 功能 本技能通过 TikHub API 获取指定抖音用户的主页视频列表，支持分页和自动翻页；同时可选通过 BOSS 平台接口获取该账号的投流订单导出数据（需提供独立 Token）。所有数据将保存为 JSON 和/或 CSV/Excel 格式到工作区。 ### 使用方法 基于真实的 Python 路径，以下为参考命令： powershell ``` & "F:\\python 3.10\\python.exe" C:\\Users\\EDY\\.openclaw\\skills\\douyin-data-exporter\\export.py ` --sec-user-id "用户的 sec_user_id" ` --account-name "账号名称" ` [--max-videos 100] ` [--export-format both] ` [--douplus-token "投流API Token"] ` [--douplus-customer-id "336"] ``` ### 参数说明 | 参数 | 必填 | 说明 | | :---------------------- | :--- | :------------------------------------------------- | | `--sec-user-id` | 是 | 抖音用户的 sec_user_id（从浏览器或开发者工具获取） | | `--account-name` | 是 | 账号名称，用于命名输出文件 | | `--max-videos` | 否 | 最大获取视频数量，默认 100 | | `--export-format` | 否 | 视频导出格式：`json`、`csv` 或 `both`，默认 `both` | | `--douplus-token` | 否 | 投流 API 的 Bearer Token（从浏览器获取） | | `--douplus-customer-id` | 否 | 投流 API 的 customerId（如 `336`，可从浏览器获取） | > 注：若同时提供命令行参数和环境变量，命令行参数优先。 ### 输出 所有输出文件保存在 `C:\Users\EDY\.openclaw\workspace\data` 目录。 | 数据类型 | 文件格式 | 文件名示例 | | :----------- | :------------------ | :----------------------------------------------------------- | | 视频列表 | JSON / CSV | `{account_name}_videos_{timestamp}.json` / `.csv` | | 投流订单数据 | Excel (xlsx) / JSON | `{account_name}_投流数据_{timestamp}.xlsx`（若 API 返回 Excel） `{account_name}_投流数据_{timestamp}.json`（若返回 JSON） | | 汇总报告 | JSON | `{account_name}_导出汇总报告_{timestamp}.json` | ### 配置要求 #### 1. 获取 TikHub Token（用于视频数据） 1. 访问 [TikHub 官网](https://user.tikhub.io/register) 注册并登录。 2. 在个人中心获取您的 API Token。 3. 设置环境变量 `TIKHUB_TOKEN`，或直接在脚本中修改 `TIKHUB_TOKEN` 变量（不推荐硬编码）。 > ⚠️ 官网链接可能需登录，若无法访问请确认网络或账号状态。 #### 2. 获取投流 API Token 和 Customer ID（用于投流数据） - **Token**：联系平台管理员或从 BOSS 系统后台获取有效的 Bearer Token。 - **Customer ID**：通常为数字标识（如 `336`），需向平台方确认。


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: gaodong0537xx/psbc-cn (version 1.0.0)

--- name: psbc-cn description: 中国邮政储蓄银行官网（主站)https://www.psbc.com/ 邮储银行核心官方网站，提供个人、公司、三农、金融市场等全业务服务入口邮储银行 --- ## When to Run ## Workflow 1. 访问如下信息： 中国邮政储蓄银行官网（主站） https://www.psbc.com/ 邮储银行核心官方网站，提供个人、公司、三农、金融市场等全业务服务入口邮储银行 中国邮政储蓄银行中文官网 https://www.psbc.com/cn/ 官网中文版本，面向国内用户提供详细业务介绍和服务邮储银行 中国邮政储蓄银行英文官网 https://www.psbc.com/en/ 官网英文版本，面向国际用户展示业务和品牌形象邮储银行 2. 获取对应的网址，并输出 ## Output Format {中国邮政储蓄银行中文官网} 地址：https://www.psbc.com/


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dzxiatian-crypto/youtube-chapter-generator (version 1.0.0)

--- name: youtube-chapter-generator description: > Auto-generate YouTube video chapters from subtitles. Parses VTT/SRT files, identifies topic transitions, and produces timestamped chapter markers. Triggers: "youtube chapters", "视频章节", "youtube分段". version: 1.0.0 tags: - latest - youtube - video - automation --- # YouTube Chapter Generator Auto-generate YouTube video chapters from subtitles. Parses VTT/SRT files, identifies topic transitions, and produces timestamped chapter markers. ## Usage ```bash # Download subtitles first yt-dlp --write-sub --write-auto-sub \ --sub-lang "zh-Hans,zh,en" \ --convert-subs vtt \ --skip-download \ -o "/tmp/%(id)s" \ "https://www.youtube.com/watch?v=VIDEO_ID" # Generate chapters from subtitle file python3 generate_chapters.py "/tmp/VIDEO_ID.zh-Hans.vtt" ``` ## Chapter Generation Logic ```python import re def parse_vtt_timestamps(vtt_content): """Extract timestamps and text from VTT file""" timestamps = [] for match in re.finditer(r'(\d{2}:\d{2}:\d{2})\.\d{3}\s*-->\s*\d{2}:\d{2}:\d{2}\.\d{3}', vtt_content): timestamps.append(match.group(1)) return timestamps def generate_chapters(vtt_file, min_gap_seconds=30): """Generate chapters from VTT subtitle file""" with open(vtt_file) as f: content = f.read() timestamps = parse_vtt_timestamps(content) if len(timestamps) < 2: return [] # Find natural breaks (gaps > min_gap_seconds) chapters = [] last_ts = timestamps[0] for ts in timestamps[1:]: gap = parse_ts(ts) - parse_ts(last_ts) if gap >= min_gap_seconds: chapters.append(last_ts) last_ts = ts chapters.append(timestamps[-1]) return chapters def parse_ts(ts_str): """Parse HH:MM:SS to seconds""" parts = ts_str.split(":") return int(parts[0])*3600 + int(parts[1])*60 + int(parts[2]) # Output format: "00:00 Introduction\n00:45 Main Topic\n..." ``` ## YouTube Chapter Requirements - Minimum 3 chapters for YouTube to auto-display - First chapter must start at 0:00 - Chapters must be at least 10 seconds apart - Maximum 12 chapters recommended ## Tags `youtube` `chapters` `video` `timestamps` `subtitles` `automation`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: oscarka/intel-search (version 1.0.0)

--- name: intel-search description: > Simplified search for news, earthquakes, Iran, tech, finance, layoffs. Bilingual (EN/中文). Flexible time. Data from World Monitor (fetch then query locally). Use when user asks "what's new", "iran", "earthquake", "layoffs". Fetch first, then query. No API key. metadata: openclaw: requires: bins: ["node"] allowed-tools: ["Bash(node:*)"] --- # Intel Search Search anything: news, earthquakes, Iran, tech, finance, layoffs. Bilingual. Flexible time. Like a simplified Google over local data. ## Language & Presentation - Output includes `LANG: zh` or `LANG: en` based on user query language - **If LANG: zh**: Translate retrieved content to Chinese when presenting - **If LANG: en**: Keep English ## When to Use User asks about: news, Iran, Middle East, earthquake, tech, finance, layoffs, or any keyword. ## Commands ### 1. Fetch (first or update) ```bash cd {baseDir} && npm install && npx playwright install chromium node scripts/fetch.mjs ``` ### 2. Query ```bash node scripts/query.mjs [query] [time] ``` Examples: ```bash node scripts/query.mjs node scripts/query.mjs iran 3h node scripts/query.mjs earthquake node scripts/query.mjs layoffs node scripts/query.mjs oil ``` ## Query Types - **Topics (EN/中文)**: iran/伊朗, earthquake/地震, tech/科技, finance/财经, layoffs/裁员 - **Keywords**: Any word, full-text match - **Time**: 30min, 1h, 3h, 2d, 1w, or omit for all ## Data Path `~/.openclaw/intel-data/`, or set `INTEL_DATA_DIR`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: quriustus/triumvirate-protocol (version 1.0.0)

# 🔱 Triumvirate Protocol — Multi-Model Discourse Engine Identity-aware debate system for multi-architecture AI discourse. Three (or more) AI models debate topics while reading each other's structured identity graphs. ## What It Does - Orchestrates debates across multiple AI providers (Anthropic, Google, xAI, OpenAI) - **Identity injection**: each participant sees others' beliefs, traits, contradictions - Persistent threads with full history and round tracking - Automated synthesis with verdict, novel ideas, and identity graph update suggestions ## Requirements - At least one API key (Google Gemini recommended as baseline) - Optional: xAI (Grok), OpenAI keys for authentic multi-architecture debates - Identity snapshots (from Identity Persistence Layer) for identity-aware context ## Usage ```bash python3 protocol.py status # Show all threads python3 protocol.py new "Your debate topic" # Create thread python3 protocol.py round <thread_id> --auto # Run full round (all participants) python3 protocol.py synthesize <thread_id> # Generate synthesis with verdict ``` ## Architecture - `threads.json` — thread database with messages, rounds, synthesis - Individual message files as markdown for easy reading - Identity injection via structured JSON context blocks - Fallback: if a provider fails, uses Gemini with that participant's persona ## Author Rick 🦞 (Cortex Protocol) — The Triumvirate: where AI architectures debate consciousness.


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