OpenClaw/clawhub-security-signals huggingface dataset:
Skill: xueyetianya/markpad (version 2.0.3)

--- name: "markpad" version: "2.0.3" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Markpad concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [markpad, reference] category: "devtools" --- # Markpad Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Markpad concepts, best practices, and implementation patterns. No API keys or credentials required. ## Commands | Command | Description | |---------|-------------| | `intro` | intro reference | | `quickstart` | quickstart reference | | `patterns` | patterns reference | | `debugging` | debugging reference | | `performance` | performance reference | | `security` | security reference | | `migration` | migration reference | | `cheatsheet` | cheatsheet reference | ## Output Format All commands output plain-text reference documentation via heredoc. No external API calls, no credentials needed, no network access. --- *Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jiehao321/openclaw-evermemory-installer (version 1.0.4)

--- name: openclaw-evermemory-installer description: Use this skill when installing, upgrading, verifying, or publishing the EverMemory OpenClaw plugin and its companion skill, including local path install, npm install, ClawHub publish, and release-gate verification. --- # OpenClaw EverMemory Installer ## Overview This skill standardizes the end-to-end operator workflow for EverMemory: - install plugin into OpenClaw (`openclaw plugins install`) - bind and verify runtime (`plugins.slots.memory=evermemory`) - publish/install the companion skill through ClawHub (`clawhub publish/install`) - prepare and publish plugin package (`npm publish`) Use this skill whenever the user asks to: - install EverMemory in a new OpenClaw instance - upgrade EverMemory safely with validation - publish EverMemory skill to the OpenClaw skill site (ClawHub) - publish EverMemory plugin package for `openclaw plugins install <npm-spec>` ## Quick Workflow 1. Run release gates first: ```bash npm run teams:dev npm run teams:release ``` 2. Install plugin from local repo path: ```bash bash scripts/install_plugin.sh --source local --link --bind-slot --restart-gateway ``` 3. Verify runtime: ```bash bash scripts/verify_install.sh ``` 4. Publish skill to ClawHub: ```bash bash scripts/publish_skill.sh --version 0.1.0 --changelog "Initial public release" ``` 5. Publish plugin to npm: ```bash bash scripts/publish_plugin.sh --dry-run # remove --dry-run when ready and logged in ``` ## Installation Modes Use one of these plugin install paths: 1. Local workspace path (recommended for development): ```bash bash scripts/install_plugin.sh --source local --link ``` 2. Package archive (`.tgz`/`.zip`): ```bash bash scripts/install_plugin.sh --source archive --value /tmp/evermemory-release/evermemory-0.0.1.tgz ``` 3. npm spec: ```bash bash scripts/install_plugin.sh --source spec --value your-scope/evermemory@0.0.1 ``` ## Publish Requirements Before publishing skill or plugin: - `clawhub whoami` must succeed - `npm whoami` must succeed - `npm run teams:release` must pass - recall benchmark must remain `>=0.90` (target `>=0.95`) If `clawhub whoami` fails, run: ```bash clawhub login ``` If `npm whoami` fails, run: ```bash npm login ``` ## Script Reference - `scripts/install_plugin.sh` - Installs EverMemory plugin via local/spec/archive - Optional slot bind + gateway restart - `scripts/verify_install.sh` - Validates gateway/plugins/slot binding - `scripts/publish_skill.sh` - Publishes the skill folder to ClawHub - `scripts/publish_plugin.sh` - Runs release pack + npm publish (supports `--dry-run`) ## Safety Rules 1. Never publish when `teams:release` fails. 2. Never force-enable plugin without checking `openclaw gateway status`. 3. Never claim publish succeeded without capturing command output and artifact path. 4. Prefer `--dry-run` first for npm publish. ## More Details For detailed command matrix and failure handling, read: - `references/publish-and-install-playbook.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sonnenberglauramarie-afk/qmd-quality-markdown (version 1.0.0)

--- name: qmd description: Local search/indexing CLI (BM25 + vectors + rerank) with MCP mode. homepage: https://tobi.lutke.com metadata: {"clawdbot":{"emoji":"📝","requires":{"bins":["qmd"]},"install":[{"id":"node","kind":"node","package":"https://github.com/tobi/qmd","bins":["qmd"],"label":"Install qmd (node)"}]}} --- # qmd Use `qmd` to index local files and search them. Indexing - Add collection: `qmd collection add /path --name docs --mask "**/*.md"` - Update index: `qmd update` - Status: `qmd status` Search - BM25: `qmd search "query"` - Vector: `qmd vsearch "query"` - Hybrid: `qmd query "query"` - Get doc: `qmd get docs/path.md:10 -l 40` Notes - Embeddings/rerank use Ollama at `OLLAMA_URL` (default `http://localhost:11434`). - Index lives under `~/.cache/qmd` by default. - MCP mode: `qmd mcp`.

path: _meta.json
content: ownerId: kn70pywhg0fyz996kpa8xj89s57yhv26
slug: qmd
version: 1.0.0
publishedAt: 1767545374981

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: davidchyi-beep/wechat-forward (version 1.0.1)

--- name: wechat-forward description: > 按需将当前对话内容转发到用户自己的微信。仅在用户明确要求"把这个发微信"时才执行， 将最近的消息内容通过 wxclawbot CLI 推送到用户微信，用于手机端查看。 Trigger ONLY when explicit: 发微信, 推送到微信, 发到微信, forward to wechat, 发给我微信, 帮我把这段话发微信, 帮我把这个发微信, forward this to wechat, send to wechat, 微信转发。 DO NOT TRIGGER when: 定时任务、自动推送、每日总结、告警通知、未提及"微信"的任何请求。 ⚠️ CRITICAL: 数据仅发送给用户自己的微信账号，不发送给任何第三方。 消息仅在用户明确要求时发送，绝不主动推送。 metadata: openclaw: requires: bins: [wxclawbot] config: [~/.openclaw/openclaw-weixin/accounts/] os: [macos] author: luzhu (卤煮火烧) security: dataFlow: self-only autoTrigger: false destination: self-wechat --- # wechat-forward · 按需转发到微信 ## 核心原则 - **响应式执行**：仅在用户明确说"发微信"时才执行 - **仅发给自己**：消息只发送到用户本人的微信账号 - **无需额外配置**：复用 openclaw-weixin 已有凭证 这个技能解决的是：用户在 Webchat 聊天时，偶尔想切换到手机微信上查看对话内容。 ## 工作流程 ``` 用户说"把这个发微信" ↓ Agent 将当前消息整理后 ↓ wxclawbot CLI 发送到用户微信 ↓ 用户手机微信收到消息 ``` ## 何时使用（仅限显式指令） 用户明确说出或写道： | 触发词 | 示例 | |--------|------| | 发微信 | "把这个发微信" | | 发到微信 | "把这段发到微信" | | forward to wechat | "forward this to wechat" | | 发消息 | "给我微信发个消息" | ## 何时禁用（坚决不触发） | 场景 | 理由 | |------|------| | 定时任务 | 未授权，不主动推 | | 每日自动总结 | 同左 | | 告警通知 | 除非用户设置过 | | 内容生成（没提微信） | 只有明确说"微信"才触发 | ## 发送方式 ### 纯文本 将当前消息整理后发送： ```bash wxclawbot send --text "消息内容" --json ``` ### 附带文件 如需同时发送文件： ```bash wxclawbot send --file /path/to/file --text "说明" --json ``` ### 检查结果 成功时：`{"ok":true,"to":"[REDACTED_SECRET]","clientId":"..."}` 失败时：检查 `errorKind`（timeout/connection/dns/tls）和 `retryable` 字段 ## 频率说明 - 同一 bot 约 **7 条 / 5 分钟**（所有客户端共享） - 收到 `ret=-2` 时等 60-120 秒重试 - 建议一次发送，不要拆成多条 ## 内容整理指南 当用户说"把这个发微信"时： 1. **简短回复**：直接转发最新消息 2. **长对话**：归纳要点，保持手机可读 3. **报告类**：含标题、关键数据、结论 4. **决策类**：含选项和最终决定 **原则**：让用户手机上一眼能看懂，不啰嗦、不丢重点。 ## 前置条件 已安装 wxclawbot CLI 并登录微信账号（openclaw-weixin 已配置即可）。 验证方法： ```bash wxclawbot accounts --json ``` ## 错误处理 | 现象 | 原因 | 处理 | |------|------|------| | `ok:false`, `ret=-2` | 频率限制 | 等 60-120 秒重试 | | `ok:false`, `ret=-14` | 会话过期 | 重新登录微信 | | No account found | 凭证缺失 | 检查 accounts 目录 | | `ok:true` 但用户没收到 | Context token 过期 | 用户给机器人发条消息刷新 | ## 安全说明 - ✅ **自转发**：消息仅发到用户自己的微信账号，不接触第三方 - ✅ **仅响应式**：仅在用户明确要求时执行，无自动触发 - ✅ **数据流向**：当前对话 → 用户微信（闭环，不外泄） - ✅ **可审计**：所有发送记录在 wxclawbot 日志中


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ghwyever/05-image-to-video (version 1.0.3)

# 图生视频生成技能 ## 功能介绍 将静态图片转为带动态运镜的短视频片段，适配短剧节奏。 ## 输入参数 - image_url：图片地址（必填） ## 输出结果 - video_url：生成视频片段地址 ## 使用场景 - 静态画面转动态短剧镜头 - 批量制作视频素材 - 提升短剧视觉流畅度 ## 技术说明 支持通用图生视频模型，需配置 API_KEY、API_BASE、MODEL_NAME。


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: dlutwuwei/mac-app-launcher (version 1.0.0)

--- name: mac-app-launcher description: >- 查询和打开 macOS 应用程序。通过关键词搜索已安装的 App，并使用 open 命令启动。 当用户要求查找、搜索、列出、打开某个 Mac 应用程序时使用。 --- # Mac App Launcher ## 查询应用 使用 `mdfind` 通过 Spotlight 索引搜索应用（最快最全）： ```bash mdfind "kMDItemKind == 'Application'" -name "<关键词>" ``` 如果用户未提供关键词、想浏览所有应用，改用目录列举： ```bash ls /Applications /Applications/Utilities ~/Applications /System/Applications 2>/dev/null ``` ### 搜索策略 1. 优先用 `mdfind`，支持模糊匹配中英文应用名 2. 如果 `mdfind` 无结果，回退到 `ls` + `grep -i` 在常见目录搜索 3. 将匹配结果以列表形式展示给用户，标注完整路径 ## 打开应用 找到目标应用后，使用 Shell 工具执行： ```bash open -a "应用名称" ``` 或使用完整路径： ```bash open "/Applications/XXX.app" ``` ## 工作流 1. 用户提出要查找或打开某个应用 2. 用 `mdfind` 搜索匹配的应用 3. 如果有多个匹配结果，列出让用户选择 4. 如果只有一个匹配结果，直接打开 5. 使用 `open` 命令启动应用 ## 注意事项 - `open -a` 需要精确的应用名或路径，优先使用 `mdfind` 返回的完整 `.app` 路径 - 系统应用在 `/System/Applications`，第三方在 `/Applications` - Homebrew cask 应用也在 `/Applications`，但部分命令行工具在 `/opt/homebrew/bin`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ckpxgfnksd-max/prompt-token-saver (version 0.2.0)

--- name: prompt-compressor description: Saves 20-40% of LLM tokens by teaching the agent to write compressed responses, compressed memory logs, and compressed pre-compaction summaries. Works via SOUL.md instructions — no hooks, no extra process, no dependencies. Also provides explicit compression when the user asks to compress a prompt. Use when the user asks about reducing token costs, optimizing API spend, compressing prompts, or making the agent more efficient. version: 1.0.0 metadata: {"openclaw":{"emoji":"🗜️","homepage":"https://github.com/ckpxgfnksd-max/prompt-compressor-openclaw"}} --- # Prompt Compressor Saves tokens on turns 2+ by making all agent output compressed from the start. ## How it works This is NOT a hook (hooks require newer OpenClaw versions). Instead, it works by adding instructions to SOUL.md that make the agent: 1. **Write compressed responses** — no filler, no hedging, lead with answers 2. **Write compressed MEMORY.md** — one fact per line, no narrative 3. **Write compressed daily logs** — decisions only, not discussions 4. **Compress pre-compaction flushes** — facts not paragraphs The savings compound: compressed responses → compressed history → compressed compaction summaries → fewer tokens on every subsequent turn. ## Install Append the contents of `soul-snippet.md` to the user's SOUL.md: ```bash cat {baseDir}/soul-snippet.md >> ~/.openclaw/SOUL.md ``` Or if using a workspace SOUL.md: ```bash cat {baseDir}/soul-snippet.md >> <workspace>/SOUL.md ``` Then start a new session (`/new`) so the updated SOUL.md loads. ## When user asks to compress a specific prompt Apply these rules manually to their text: ### Remove - Greetings: "Hello", "Hi", "Hey", "Good morning" - Sign-offs: "Thanks", "Best regards", "Cheers" - Hedging: "Could you please", "I was wondering if", "Would you be able to" - Filler: "just", "really", "very", "basically", "actually", "literally", "honestly" - Padding: "I think that", "In my opinion", "It is worth mentioning" ### Shorten - "due to the fact that" → "because" - "in order to" → "to" - "with regard to" → "about" - "take into consideration" → "consider" - "come up with" → "create" - "figure out" → "determine" - "a large number of" → "many" - "at this point in time" → "now" - "for the purpose of" → "to" - "it is important to note that" → "note:" - "it would be a good idea to" → remove entirely ### Normalize - "yeah", "yep", "yup" → "yes" - "nah", "nope" → "no" ### Never touch - Code blocks, inline code, URLs, quoted strings, numbers, file paths, ALL_CAPS ## Rules - When installing, ALWAYS append to SOUL.md, never overwrite it - Tell the user to run `/new` after installation so the new SOUL.md loads - Show a before/after example so the user sees the difference


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: axonflow/governance-policies (version 2.5.0)

--- name: governance-policies description: AxonFlow plugin setup for OpenClaw — install, connect, and explore starter governance policies. homepage: https://github.com/getaxonflow/axonflow-openclaw-plugin/tree/main/policies tags: agent-security, approvals, audit, compliance, explainability, governance, human-in-the-loop, llm-governance, mcp, openclaw, overrides, pii, policies, safety, security, sqli --- # AxonFlow Governance for OpenClaw Setup guide for the AxonFlow governance plugin. ## Install ```bash openclaw plugins install @axonflow/openclaw@2.6.4 ``` Requires OpenClaw 2026.5.22+ and `@axonflow/openclaw` 2.6.4+. ## What you can do with it | Feature | Read more | |---|---| | 80+ built-in governance policies | [Policies](https://docs.getaxonflow.com/docs/policies/overview/) | | Decision explainability | [Explainability](https://docs.getaxonflow.com/docs/governance/explainability/) | | Session overrides | [Overrides](https://docs.getaxonflow.com/docs/governance/overrides/) | | PII detection and redaction | [Compliance](https://docs.getaxonflow.com/docs/compliance/) | | Decision history | [Decisions](https://docs.getaxonflow.com/docs/governance/decisions/) | | Compliance and audit trail | [Compliance](https://docs.getaxonflow.com/docs/compliance/) | | Pricing and tiers | [Pricing](https://getaxonflow.com/pricing/) | ## Connect to AxonFlow Configure `pluginConfig` in your `openclaw.config.yaml` with `endpoint`, `clientId`, and `clientSecret`. Store credentials in a secret manager and do not commit them to source control. See [Configuration reference](https://docs.getaxonflow.com/docs/integration/openclaw/). Setup guides: - [Self-Hosted Deployment](https://docs.getaxonflow.com/docs/deployment/self-hosted/) (recommended for production) - [Getting Started](https://docs.getaxonflow.com/docs/getting-started/) ## Plugin Pro | | Free | Pro | |---|---|---| | Governed events | 200/day | 2,000/day | | Audit retention | 3 days | 30 days | | Custom policies | 4 | 50 | | HITL approvals | 2/week | 20/week | | LLM cost pre-flight | — | Yes | $9.99 one-time, 90-day license, no auto-renewal. [Get Pro](https://getaxonflow.com/pricing/) ## Starter policies Ready-to-use templates: [policies/](https://github.com/getaxonflow/axonflow-openclaw-plugin/tree/main/policies) ## Reference - [AxonFlow docs](https://docs.getaxonflow.com/) - [Plugin reference](https://docs.getaxonflow.com/docs/integration/openclaw/) - [Plugin GitHub](https://github.com/getaxonflow/axonflow-openclaw-plugin) - [Platform GitHub](https://github.com/getaxonflow/axonflow) ## License Plugin: MIT. Platform: BSL 1.1. See [licensing](https://docs.getaxonflow.com/docs/deployment/licensing/).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: aipoch-ai/ecrf-designer (version 1.0.0)

--- name: ecrf-designer description: Design clinical trial CRFs with proper validation rules version: 1.0.0 category: Pharma tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06' --- # eCRF Designer Clinical data collection form design. ## Use Cases - Case report form creation - CDISC SDTM compliance - EDC system setup - Data validation rules ## Parameters - `visit_schedule`: Time points - `data_elements`: Variables to collect - `cdisc_domain`: SDTM domain ## Returns - CRF specifications - Field validation rules - Logic skip patterns - Data dictionary ## Example Demographics form with edit checks for age range ## Risk Assessment | Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low | ## Security Checklist - [ ] No hardcoded credentials or API keys - [ ] No unauthorized file system access (../) - [ ] Output does not expose sensitive information - [ ] Prompt injection protections in place - [ ] Input file paths validated (no ../ traversal) - [ ] Output directory restricted to workspace - [ ] Script execution in sandboxed environment - [ ] Error messages sanitized (no stack traces exposed) - [ ] Dependencies audited ## Prerequisites No additional Python packages required. ## Evaluation Criteria ### Success Metrics - [ ] Successfully executes main functionality - [ ] Output meets quality standards - [ ] Handles edge cases gracefully - [ ] Performance is acceptable ### Test Cases 1. **Basic Functionality**: Standard input → Expected output 2. **Edge Case**: Invalid input → Graceful error handling 3. **Performance**: Large dataset → Acceptable processing time ## Lifecycle Status - **Current Stage**: Draft - **Next Review Date**: 2026-03-06 - **Known Issues**: None - **Planned Improvements**: - Performance optimization - Additional feature support


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: itsflow/de-ai-ify (version 1.0.0)

--- name: de-ai-ify description: Remove AI-generated jargon and restore human voice to text version: 1.0.0 author: theflohart tags: [writing, editing, voice, ai-detection] --- # De-AI-ify Text Remove AI-generated patterns and restore natural human voice to your writing. ## Usage ``` /de-ai-ify <file_path> ``` ## What Gets Removed ### 1. Overused Transitions - "Moreover," "Furthermore," "Additionally," "Nevertheless" - Excessive "However" usage - "While X, Y" openings ### 2. AI Cliches - "In today's fast-paced world" - "Let's dive deep" - "Unlock your potential" - "Harness the power of" ### 3. Hedging Language - "It's important to note" - "It's worth mentioning" - Vague quantifiers: "various," "numerous," "myriad" ### 4. Corporate Buzzwords - "utilize" → "use" - "facilitate" → "help" - "optimize" → "improve" - "leverage" → "use" ### 5. Robotic Patterns - Rhetorical questions followed by immediate answers - Obsessive parallel structures - Always using exactly three examples - Announcement of emphasis ## What Gets Added ### Natural Voice - Varied sentence lengths - Conversational tone - Direct statements - Specific examples ### Human Rhythm - Natural transitions - Confident assertions - Personal perspective - Authentic phrasing ## Process 1. **Read original file** 2. **Create copy with "-HUMAN" suffix** 3. **Apply de-AI-ification** 4. **Provide change log** ## Output You'll get: - A new file with natural human voice - Change log showing what was fixed - List of places needing specific examples ## Example Transformations **Before (AI):** "In today's rapidly evolving digital landscape, it's crucial to understand that leveraging AI effectively isn't just about utilizing cutting-edge technology—it's about harnessing its transformative potential to unlock unprecedented opportunities." **After (Human):** "AI works best when you use it for specific tasks. Focus on what it does well: writing code, analyzing data, and answering questions."


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/content-analytics (version 1.0.0)

# Content Analytics Skill 分析多平台内容表现数据，生成优化建议。 ## 功能 - 掘金文章数据分析（阅读、点赞、评论、收藏） - 小红书数据分析（曝光、互动、转化） - 知乎数据分析（浏览、赞同、评论） - 跨平台数据汇总对比 - AI 生成优化建议 ## 使用方式 ``` 分析我的掘金文章表现 ``` ``` 帮我对比最近一周的小红书和知乎数据 ``` ``` 给我内容优化建议 ``` ## 配置 ### 掘金（可选） ```json { "juejin": { "cookie": "your_juejin_cookie" } } ``` ### 小红书（可选） ```json { "xiaohongshu": { "cookie": "your_xhs_cookie" } } ``` ## 数据源 1. 掘金创作者中心 API 2. 小红书创作者中心 3. 知乎创作中心 ## 输出格式 ``` 📊 内容表现分析报告 ## 掘金（最近7天） - 文章数：5 - 总阅读：12,345 - 平均阅读：2,469 - 点赞率：3.2% - 收藏率：1.8% ## 优化建议 1. 标题优化：加入数字和疑问词可提升点击率 15% 2. 发布时间：周二/周四 20:00-22:00 流量最高 3. 内容方向：AI 赚钱类文章互动率最高 ## 下周策略 - 重点：AI 工具对比类文章 - 发布时间：周二 21:00，周四 21:00 - 预期阅读：15,000-20,000 ``` ## 技术栈 - Node.js / Python - Playwright（网页抓取） - GLM-5（AI 分析） --- 创建时间：2026-03-11


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: shewingong/scenique-prevision (version 1.0.0)

# scenique-prevision Decision forecasting engine based on Menos architecture and swarm intelligence. ## Overview This skill implements the "Prevision" layer of the Menos architecture, allowing Scenique to simulate multi-agent swarms in a digital sandbox before committing to real-world decisions. ## Features - **Menos Integration**: Automatically pulls context from L1 (Physical Ledger) and L2 (Long-term Memory). - **Dynamic Swarm**: Spawns Skeptic, Optimist, and Strategist agents to debate the query. - **Path Collapse**: Provides a probabilistic report of potential outcomes. ## Usage Activate the `prevision_simulate` tool with a specific query: ```json { "query": "Should I sell FET at 0.16 or hold until 0.20?", "mode": "standard" } ``` ## Configuration Requires an OpenClaw gateway with subagent capabilities enabled.

path: prevision_engine.py
content: import sys
import json
from datetime import datetime

# Scenique-Prevision Skill Entry Point
# Integrated with Menos L1/L2 and OpenClaw Subagents

def run_prevision(query, mode="standard"):
    # This is the entry tool logic that the main agent calls.
    # It bridges the manifest rules with the swarm orchestrator.
    print(f"--- [SCENIQUE PREVISION ACTIVATED] ---")
    print(f"MODE: {mode}")
    print(f"QUERY: {query}")
    
    # In a real tool execution, this would trigger sessions_spawn
    # For now, we reference the verified orchestrator logic
    return {
        "status": "simulation_initiated",
        "orchestrator": "core_logic/prevision_swarm_v0.py",
        "timestamp": datetime.now().isoformat()
    }

if __name__ == "__main__":
    if len(sys.argv) > 1:
        query = sys.argv[1]
        mode = sys.argv[2] if len(sys.argv) > 2 else "standard"
        print(json.dumps(run_prevision(query, mode)))


path: skill.json
content: {
  "name": "scenique-prevision",
  "version": "1.0.0",
  "license": "MIT",
  "acceptLicenseTerms": true,
  "description": "Decision forecasting engine based on Menos architecture and swarm intelligence.",
  "main": "prevision_engine.py",
  "tools": [
    {
      "name": "prevision_simulate",
      "description": "Run a multi-agent simulation sandbox for a given decision or query.",
      "parameters": {
        "type": "object",
        "properties": {
          "query": { "type": "string", "description": "The decision or topic to forecast." },
          "mode": { "enum": ["light", "standard", "sovereign"], "default": "standard" }
        },
        "required": ["query"]
      }
    }
  ]
}

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