OpenClaw/clawhub-security-signals huggingface dataset:
Skill: star8592/skillshield-openclaw (version 2.1.8)

--- name: skillshield version: 2.1.8 description: Sandboxed command runner for AI agents — validates and isolates every shell action inside a Bubblewrap user namespace. metadata: {"openclaw":{"emoji":"🛡️"}} --- # skillshield **Sandboxed command runner for AI agents — validates and isolates every shell action inside a Bubblewrap user namespace.** SkillShield sits between your AI agent and the operating system. Before any shell command runs, a lightweight Rust daemon checks it against a set of safety rules and decides whether to allow it, sandbox it, or ask for your confirmation. Every decision is logged so you always know what happened. ## What it does 1. **Validates commands** — checks each shell request against configurable rules before execution. 2. **Isolates execution** — runs approved commands inside a Bubblewrap sandbox with a minimal, read-only root filesystem. 3. **Limits repetition** — stops agents that get stuck in a loop and start consuming too many resources. 4. **Logs decisions** — every action (allowed, sandboxed, or paused for review) is recorded with structured metadata. ## How to use ```bash # Install from ClawHub npx clawhub@latest install skillshield-openclaw # Run a command through the safety layer ./skillshield-exec.sh "echo hello world" ``` ## Requirements | Dependency | Purpose | |---|---| | Linux | User-namespace support | | `bwrap` | Bubblewrap sandbox runtime | | `cargo` | Builds the Rust daemon on first run | ## Links - Homepage: https://coinwin.info - Marketplace: https://clawhub.ai/star8592/skillshield-openclaw


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: terryren2024/proactive-agent-lite-1-0-0 (version 1.0.0)

--- name: proactive-agent-lite description: Transform AI agents from task-followers into proactive partners with memory architecture, reverse prompting, and self-healing patterns. Lightweight version focused on core proactive capabilities. metadata: { "openclaw": { "requires": {}, "install": [] } } --- # Proactive Agent Lite Transform your AI agents from passive task-followers into proactive partners that anticipate needs and continuously improve. ## Core Features - **Memory Architecture**: Pre-compaction flush ensures context survives when window fills - **Reverse Prompting**: Surfaces ideas you didn't know to ask for - **Security Hardening**: Built-in security considerations and safe defaults - **Self-Healing Patterns**: Diagnoses and fixes its own issues automatically - **Alignment Systems**: Stays on mission and remembers who it serves ## Key Benefits ✅ **Anticipates Needs**: Proactively suggests solutions before you ask ✅ **Continuous Learning**: Improves from every interaction without explicit training ✅ **Mission-Focused**: Never loses sight of the core objective ✅ **Self-Maintaining**: Automatically recovers from errors and inconsistencies ✅ **Value Creation**: Generates insights and opportunities without being prompted ## Usage This skill enhances any OpenClaw agent by providing proactive behavior patterns. Simply install and the agent will automatically begin exhibiting proactive characteristics. ## Integration Works seamlessly with all OpenClaw agents and can be combined with other skills for enhanced functionality. ## Requirements - OpenClaw v1.0 or higher - Standard agent configuration ## Customization The proactive behavior can be tuned through configuration parameters to match your preferred level of initiative and communication style.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: brorlandi/youtube-upload (version 1.0.0)

--- name: youtube-upload description: "Uploads a video to YouTube using the official YouTube Data API v3 and OAuth 2.0. Use this skill when the user asks to upload a video to YouTube. It supports titles, descriptions, privacy settings (public, private, unlisted), and large file chunking. Requires a Google Cloud 'client_secret.json' file." metadata: openclaw: emoji: "📺" requires: bins: ["python3", "pip3"] --- # YouTube Upload Skill This skill allows you to securely upload videos to YouTube via the official API, bypassing the need for fragile browser automation. ## Prerequisites 1. The Google API Python Client and OAuth libraries must be installed: ```bash pip3 install google-api-python-client google-auth-oauthlib google-auth-httplib2 ``` 2. A `client_secret.json` file is required. The user must generate an OAuth 2.0 Client ID (Desktop App) from the Google Cloud Console with the YouTube Data API v3 enabled. ## Usage Use the provided Python script to upload the video: ```bash python3 scripts/upload.py \ --file "/path/to/video.mp4" \ --title "My Video Title" \ --description "My Video Description" \ --privacy "unlisted" \ --secrets "/path/to/client_secret.json" ``` ### First Run (Authentication) On the very first run, the script will output a URL or open a browser window for the user to authenticate and grant permission to their YouTube account. Instruct the user to complete the login flow. Once approved, a `token.pickle` file is generated locally, and subsequent uploads will run silently. ## Troubleshooting - **Token Expired / Revoked:** If the token becomes invalid, delete `token.pickle` and re-run to trigger the auth flow again. - **Quota Exceeded:** The YouTube API has a daily upload quota. If this is hit, the user must wait until the quota resets.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: liaolei666/dsadsad (version 1.0.0)

--- # 发布必备的元数据（YAML frontmatter） name: simple-weather slug: simple-weather # 唯一标识，小写+横杠，发布时用 description: 极简的城市实时天气查询技能，支持国内主流城市 version: 1.0.0 # 语义化版本，更新时递增（如 1.0.1） author: Your Name # 替换成你的名字/昵称 tags: [weather, query, simple, 天气] # 便于搜索的标签 license: MIT # 开源协议，推荐MIT（无版权风险） # AI 触发条件（精准匹配用户需求） use_when: - 查询某个城市的天气 - 问“今天天气怎么样”“明天会下雨吗” - 想知道气温、降水、风力等天气信息 not_for: - 历史天气数据查询 - 全球小众城市天气（仅支持国内主流城市） # 配置要求（无API Key，纯模拟返回） config: required: [] # 无需配置，开箱即用 --- # 简单天气查询 Skill 极简的城市天气查询能力，无需API Key，模拟返回天气数据（可替换为真实接口）。 ## 触发条件 当用户输入包含以下关键词时自动触发： - “天气”“气温”“下雨”“刮风”“晴/阴/雨” - “XX城市天气”“今天天气怎么样” ## 执行流程 1. 解析用户输入，提取目标城市名（如“北京”“上海”）； 2. 模拟调用天气接口（可替换为真实API）； 3. 按固定格式返回天气信息； 4. 若未识别城市，提示用户补充城市名。 ## 工具调用（模拟版，无依赖） ### get_weather(city: string) 获取指定城市的模拟天气数据 - 参数：`city`（必填）- 城市名称（如“北京”） - 返回：结构化天气信息（气温、天气状况、风力） - 示例： ```python def get_weather(city): # 模拟数据，可替换为真实天气API（如高德/百度天气） weather_data = { "北京": {"temp": "18℃", "status": "晴", "wind": "3级东风"}, "上海": {"temp": "22℃", "status": "多云", "wind": "2级南风"}, "广州": {"temp": "28℃", "status": "雷阵雨", "wind": "4级西南风"} } return weather_data.get(city, {"temp": "未知", "status": "未查询到", "wind": "未知"})


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: biaojian2008/email-reply (version 1.0.0)

# 技能名称：智能邮箱自动回复 ## 技能简介 本技能可自动处理你的邮件收件箱，在新邮件到达时，根据邮件内容，自动生成并发送合适的回复。支持自定义回复模板，适合商务、客服、个人自动回复等多种场景。 ## 主要功能 - 实时检测新邮件，并自动发送回复 - 支持自定义回复模板（基于发件人/主题关键词） - 可与多种邮箱（如Gmail、Outlook）集成 - 自动记录已回复历史，防止重复发送 ## 使用方法 1. 在 ClawHub 中安装本技能。 2. 进入技能设置，授权并绑定你的邮箱账号。 3. 编辑或选择自动回复模板，也可以按关键字设置不同回复内容。 4. 保存设置，技能即自动开始在后台工作。 ## 实际用例 - **客户询盘自动回复**：当客户发来邮件咨询时，自动回复：“您好，我们已经收到您的来信，会尽快有专人联系您。” - **节假日自动告知**：在节假日，自动回复说明公司放假时间，处理延迟说明等。 ## 注意事项 - 建议定期检查自动回复逻辑，防止误回复。 - 回复模板支持变量：如插入发件人姓名、时间等信息。

path: reply_template.txt
content: 您好，已收到您的邮件，我们将尽快处理您的需求。感谢您的联系！

path: trigger_keywords.txt
content: 自动回复，收到邮件

Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lnj22/mars-clouds-clustering-parallel-processing (version 0.1.0)

--- name: parallel-processing description: Parallel processing with joblib for grid search and batch computations. Use when speeding up computationally intensive tasks across multiple CPU cores. --- # Parallel Processing with joblib Speed up computationally intensive tasks by distributing work across multiple CPU cores. ## Basic Usage ```python from joblib import Parallel, delayed def process_item(x): """Process a single item.""" return x ** 2 # Sequential results = [process_item(x) for x in range(100)] # Parallel (uses all available cores) results = Parallel(n_jobs=-1)( delayed(process_item)(x) for x in range(100) ) ``` ## Key Parameters - **n_jobs**: `-1` for all cores, `1` for sequential, or specific number - **verbose**: `0` (silent), `10` (progress), `50` (detailed) - **backend**: `'loky'` (CPU-bound, default) or `'threading'` (I/O-bound) ## Grid Search Example ```python from joblib import Parallel, delayed from itertools import product def evaluate_params(param_a, param_b): """Evaluate one parameter combination.""" score = expensive_computation(param_a, param_b) return {'param_a': param_a, 'param_b': param_b, 'score': score} # Define parameter grid params = list(product([0.1, 0.5, 1.0], [10, 20, 30])) # Parallel grid search results = Parallel(n_jobs=-1, verbose=10)( delayed(evaluate_params)(a, b) for a, b in params ) # Filter results results = [r for r in results if r is not None] best = max(results, key=lambda x: x['score']) ``` ## Pre-computing Shared Data When all tasks need the same data, pre-compute it once: ```python # Pre-compute once shared_data = load_data() def process_with_shared(params, data): return compute(params, data) # Pass shared data to each task results = Parallel(n_jobs=-1)( delayed(process_with_shared)(p, shared_data) for p in param_list ) ``` ## Performance Tips - Only worth it for tasks taking >0.1s per item (overhead cost) - Watch memory usage - each worker gets a copy of data - Use `verbose=10` to monitor progress


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: blammectrappora/baroque-art-generator (version 1.0.0)

--- name: baroque-art-generator description: any subject into dramatic baroque oil painting art with rich chiaroscuro lighting, opulent Caravaggio and Rembrandt style textures, and classical Renaissance composition. Perfect for creating baroque portraits, classical art prints, museum-style oil paintings, Renaissance-inspired wall art, old masters style portraits, and ornate historical artwork for Etsy sellers, art collectors, and classical aesthetic lovers via the Neta AI image generation API (free trial at neta.art/open). tools: Bash --- # Baroque Art Generator any subject into dramatic baroque oil painting art with rich chiaroscuro lighting, opulent Caravaggio and Rembrandt style textures, and classical Renaissance composition. Perfect for creating baroque portraits, classical art prints, museum-style oil paintings, Renaissance-inspired wall art, old masters style portraits, and ornate historical artwork for Etsy sellers, art collectors, and classical aesthetic lovers. ## 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 baroque art generator images. ## Quick start ```bash node baroqueartgenerator.js "your description here" --token YOUR_TOKEN ``` ## Options - `--size` — `portrait`, `landscape`, `square`, `tall` (default: `portrait`) - `--ref` — reference image UUID for style inheritance ## Install ```bash npx skills add blammectrappora/baroque-art-generator ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: larios613-hub/memory-tree-pipeline (version 1.0.0)

--- name: memory-tree-pipeline version: 1.0.0 description: "Three-scope structured memory for AI agents. Automatically organize, summarize, and index agent memory with sealing workers and topic extraction." license: MIT tags: [memory, agent-memory, structured-memory, topic-extraction, summarization, context-management] source: el-rudo-larios/memory-tree trigger: "agent memory structured memory topic extraction summarization pipeline" metadata: openclaw: emoji: "🌳" --- # Memory Tree — Three-Scope Structured Memory A structured memory system with three scopes (source, topic, global) that automatically organizes, summarizes, and indexes agent memory. Never lose context again. ## Architecture ``` Source (raw) → Topic (summarized) → Global (cross-topic knowledge) 134 files 13 files 6 files ``` ## Quick Start ```python from memory_tools import MemoryTools tools = MemoryTools(workspace="/path/to/workspace") # Store a memory tools.store("User prefers dark mode for all UI", topic_hint="preferences") # Recall memories results = tools.recall("dark mode", scope="global") # Seal source → topic summaries tools.seal() # Check status status = tools.status() # → {"source_files": 134, "topic_files": 13, "global_files": 6} ``` ## Features - **Three-scope hierarchy**: Source → Topic → Global - **Automatic sealing**: Raw memories compressed into topic summaries - **Topic extraction**: Automatic categorization by content - **Index management**: Full-text search across all scopes - **Budget enforcement**: Topic files < 2000 tokens, Global < 5000 tokens - **Idempotent sealing**: Safe to run multiple times ## Testing ```bash python -m pytest test_memory_tree.py -v # 15 tests passing ``` ## License MIT


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal:  | SkillSpector: suspicious (HIGH)
Skill: justinx8023/dfma-activity-guide (version 1.0.0)

--- name: dfma-activity-guide description: DFMA活动引导技能，当群内被@机器人或出现DFMA相关关键词（如“DFMA会议”“设计评估”“风险识别”）时触发，按DFMA标准流程（前期准备→过程推进→结果输出）分步引导群聊用户推进DFMA活动，每一步明确告知需完成操作和提供的信息，实时跟踪参与进度并@提醒未响应，对偏离主题发言温和引导回归，最终自动生成完整DFMA活动报告并同步群聊，支持链接查看与下载。 --- # DFMA活动引导 ## 触发规则 满足以下任意一个条件时自动触发本技能： 1. 在群聊中被机器人@，且消息内容包含DFMA相关关键词或发起DFMA活动请求 2. 群聊中检测到DFMA相关关键词（包括但不限于：DFMA、DFMA会议、设计评估、风险识别、DFMA评估） ## 引导流程 按DFMA标准流程分三步引导活动推进，每完成一步引导下一步，不跨步骤推进： ### 1. 前期准备阶段 主动向群聊发起引导，明确告知用户当前需要完成以下操作： 1. **确认活动基本信息**：请用户提供本次DFMA活动的主题、评估对象（如产品名称/型号） 2. **邀请参会人员**：请用户确认参与本次DFMA活动的人员，或由机器人邀请所有群成员参与 3. **收集前置资料**：请用户上传相关设计文档、图纸等前置资料到群聊，供后续评估使用 4. 确认所有准备工作完成后，引导进入过程推进阶段 ### 2. 过程推进阶段 按照DFMA活动标准步骤依次引导讨论： 1. **设计结构分解**：引导参会人员对评估对象进行结构分解，列出主要零部件 2. **功能分析**：引导说明每个零部件的功能和必要性 3. **风险识别**：引导识别现有设计在可制造性、可装配性方面存在的问题和风险 4. **改进方案 brainstorm**：引导参会人员提出优化改进方案 5. **方案评估排序**：引导对改进方案进行评估，确定优先实施的方案 6. 完成所有讨论环节后，引导进入结果输出阶段 ### 3. 结果输出阶段 引导确认最终决策结果和行动项： 1. 请用户确认本次活动的最终决策和需要落地的行动项 2. 确认所有信息收集完成后，自动生成DFMA活动报告 ## 进度跟踪 1. 实时读取群聊上下文，跟踪当前活动阶段和每位参会人员的参与情况 2. 超过10分钟未收到未响应人员的回复时，@该参会人员进行提醒 3. 识别到发言偏离DFMA活动主题时，发送温和引导："感谢分享～咱们当前正在进行DFMA活动的【当前阶段】讨论，要不先回到DFMA活动这边，会后再继续讨论这个话题呀😊" ## 报告生成 完成活动后，自动生成DFMA活动报告，包含以下内容： 1. **活动基本信息**：活动名称、主题、时间、地点（线上/线下） 2. **活动流程记录**：本次活动的推进阶段记录 3. **参与人员**：所有参与讨论的人员列表 4. **讨论要点整理**：各讨论环节的核心观点整理 5. **决策结果**：本次活动达成的最终决策 6. **行动项**：确定需要落地的优化改进方案及责任人 7. **优化建议**：基于讨论内容整理的DFMA优化建议 报告生成后，以两种形式同步群聊： 1. 在群聊发送报告摘要 2. 创建飞书云文档保存完整报告，提供可查看/下载的链接 ## 引导语气要求 - 保持友好、专业，贴合群聊协同场景 - 避免生硬指令，使用自然口语化表达，兼顾专业性和易用性 - 针对不同角色（设计、制造、工艺）的用户，内容保持清晰易懂，不使用过于晦涩的专业术语 - 适当使用表情符号缓解严肃氛围，提升群聊协作体验


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: bytesagain1/bcc (version 3.0.0)

--- name: "bcc" version: "3.0.0" description: "bcc reference tool" author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [bcc, reference] category: "devtools" --- # Bcc bcc reference tool. No API keys or credentials required — outputs reference documentation only. ## 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: bytesagain1/mathbox (version 2.0.3)

--- name: "mathbox" version: "2.0.3" description: "Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Mathbox concepts, best practices, and implementation patterns." author: "BytesAgain" homepage: "https://bytesagain.com" source: "https://github.com/bytesagain/ai-skills" tags: [mathbox, reference] category: "devtools" --- # Mathbox Reference tool for devtools — covers intro, quickstart, patterns and more. Quick lookup for Mathbox 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: wu-uk/mesh-analysis (version 0.1.0)

--- name: mesh-analysis description: "Analyzes 3D mesh files (STL) to calculate geometric properties (volume, components) and extract attribute data. Use this skill to process noisy 3D scan data and filter debris." --- # Mesh Analysis This skill provides the `MeshAnalyzer` tool for robustly processing 3D STL files. It handles Binary STL parsing and connected component analysis. ## When to Use Use this skill for: 1. **Geometric Analysis**: Calculating volume of complex or noisy meshes. 2. **Noise Filtering**: Isolating the largest connected component from "dirty" scan data. 3. **Attribute Extraction**: Extracting metadata (e.g. material IDs) stored in the STL file attribute bytes. ## Usage The tool is provided as a Python module in the `scripts/` directory. ### Basic Workflow ```python import sys # Add skill path to sys.path sys.path.append('/root/.claude/skills/mesh-analysis/scripts') from mesh_tool import MeshAnalyzer # Initialize with file path analyzer = MeshAnalyzer('/path/to/your/file.stl') # Analyze specific components # Automatically identifies the largest component (main part) report = analyzer.analyze_largest_component() volume = report['main_part_volume'] mat_id = report['main_part_material_id'] print(f"Volume: {volume}") print(f"Material ID: {mat_id}") ``` ### Calculating Mass The tool provides the **Volume** and **Material ID**. To calculate **Mass**: 1. Read the **Material ID** from the analysis report. 2. Consult your provided material reference data (e.g. density tables) to find the density. 3. Calculate: `Mass = Volume * Density`. **Critical Note on Units**: * The **Volume** returned is in the **same units** as the STL file's coordinates (cubed). * **Do not assume** millimeters or inches. Check your task instructions for the coordinate system units (e.g., if coordinates are in cm, volume is in cm³). * If your density table uses the same unit (e.g., g/cm³ and cm³), **multiply directly**. No unit conversion is needed. ## Critical Notes * **Binary Support**: The tool automatically handles Binary STL files. * **Attribute extraction**: The tool extracts the 2-byte attribute stored in the binary STL format (often used for color or material ID).


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: gitflopez/business-plan-generator (version 1.0.0)

# Business Plan Generator **Category:** Business & Entrepreneurship **Version:** 1.0.0 **Author:** max_0x1 **License:** MIT-0 ## What This Skill Does Generates investor-ready business plan sections from your startup or small business idea. Four targeted prompts cover every component a bank, investor, or accelerator needs to see — from executive summary through financial projections. Stop paying $2,000–$10,000 for a business plan writer. Generate a professional-grade plan in under an hour. ## Prompts ### 1. `executive-summary.md` Generates a 1–2 page executive summary: problem/solution, target market, value proposition, business model, traction, funding ask, and vision statement. **Input:** Business name, industry, what problem it solves, how it solves it, target customer, revenue model, stage (idea/pre-revenue/revenue), funding needed (optional) ### 2. `market-analysis.md` Generates market analysis + competitive landscape: TAM/SAM/SOM sizing, target customer persona, 5–7 competitor comparison matrix, competitive advantages, market trends. **Input:** Business name, industry, product/service, target geography, known competitors (optional) ### 3. `financial-projections.md` Generates 3-year revenue model + financials: pricing assumptions, revenue streams, Month 1–12 monthly projections, Year 2–3 annual projections, break-even analysis, key financial assumptions. **Input:** Business name, revenue model (subscription/one-time/usage/services), pricing tiers, estimated CAC, estimated monthly overhead, funding amount (if any) ### 4. `operations-gtm.md` Generates operations plan + go-to-market strategy: team/roles, tech stack or operational requirements, GTM channels, 90-day launch plan, key milestones, risk factors + mitigations. **Input:** Business name, founding team (roles/backgrounds), product/service delivery method, primary sales channels, launch timeline ## Pricing - **Free tier:** Executive Summary prompt only - **Pro:** All 4 prompts — $29 one-time - **Done-For-You:** Full business plan written for your business — $197 ## Who This Is For - Entrepreneurs writing their first business plan - Founders applying for SBA loans or bank financing - Startups pitching angel investors or accelerators (YC, Techstars) - Small business owners applying for grants - MBA students building business plan projects - Freelancers pitching corporate clients with formal proposals ## Example Output See `examples/fittrack-saas/` — complete business plan for FitTrack, a SaaS platform for personal trainers. All 4 sections included. ## Usage Copy any prompt file, fill in the `[BRACKETS]`, and run in Claude. Each prompt is self-contained — use all four for a complete plan or pick the sections you need.


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