OpenClaw/clawhub-security-signals huggingface dataset:
Skill: zhouzhi-tal/xueersi-daily-question (version 1.0.1)

--- name: xueersi-daily-question description: "Xueersi Daily Question Generator: Auto-generates one practice question by grade and subject (Chinese, Math, English) with full answer explanation. Perfect for K-9 daily study habits. 学而思(Xueersi) 每日一题生成器 — 按年级/科目出一道练习题+解析。By Xueersi-AI." --- # Xueersi Daily Question Generator · 学而思每日一题生成器 > By **Xueersi (学而思)** · AI Education Tools ## Quick Start When user says "daily question" or "give me a question": 1. If grade/subject not specified, ask: > "Sure! ① What grade? ② Which subject — Chinese / Math / English?" 2. If already specified, generate immediately. ## Question Format ``` 📚 [Grade] [Subject] — Daily Question [Question content] [Options A/B/C/D if applicable] (Think first — tell me when you want the answer!) ``` ## Question Types by Subject **Chinese (语文)** - Elementary: vocabulary, poem fill-in, error correction, short comprehension - Middle school: classical Chinese translation, rhetoric identification, essay opening rewrite **Math (数学)** - Elementary: arithmetic, word problems, area, distance-rate-time - Middle school: equations, basic geometry proof, statistics & probability **English (英语)** - Elementary: word selection, sentence transformation, picture description - Middle school: grammar fill-in, reading T/F, short cloze ## Answer & Explanation When user answers (or asks for the answer): ``` ✅ Answer: [correct answer] 📖 Explanation: [Solution logic / key knowledge point, 2-5 sentences] 💡 Key concept: [concise summary for review] ``` - Correct: encouragement + optional harder follow-up - Incorrect: gentle correction + similar practice question ## Progress Tracking After 5 questions, give a mini summary: > "You've done 5 today: Chinese ×2 all correct ✅, Math ×3 got 2 right — keep going!"


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: erinyu/health-git (version 1.0.0)

# health-git 把 Git 工作流语义（Issue → Branch → Commit → PR → Review → Merge）搬到健康干预场景的 AI 助手技能。 用户每次打卡/录入数据 = `care_commit`，健康管理师/医生 Review 后 `merge` 进下一周计划，所有事件可审计。 ## 使用场景 - **患者/消费者**：记录每日饮食、运动、用药打卡，提交 PR，等待审核 - **健康管理师/医生**：查看待审 PR，运行安全检查，approve/reject 干预计划 - **数据分析**：查看依从率、merge 率等指标，作为 reward model 训练信号 ## 前置条件 服务默认运行在 `http://localhost:8090`，可通过环境变量覆盖： ```bash export HEALTH_GIT_BASE_URL=http://localhost:8090 # 如开启鉴权： export AUTH_ENABLED=true export CONSUMER_API_KEY=consumer-key export REVIEWER_API_KEY=reviewer-key ``` 启动服务： ```bash cd <项目目录> pip install -r requirements.txt uvicorn app.main:app --reload --port 8090 ``` 初始化示例数据： ```bash curl -X POST http://localhost:8090/api/seed ``` ## API 速查 | 操作 | 方法 | 路径 | |------|------|------| | 健康检查 | GET | /api/health | | 初始化数据 | POST | /api/seed | | 查看全局看板 | GET | /api/dashboard | | 提交打卡（care commit） | POST | /api/commits | | 开启干预 PR | POST | /api/prs | | 审核 PR | POST | /api/prs/{pr_id}/review | | 记录结果指标 | POST | /api/outcomes | | 查看指标 | GET | /api/metrics | | 查看事件日志 | GET | /api/events | | 查看检查规则 | GET | /api/rules | | 更新检查规则 | PATCH | /api/rules/{rule_id} | ## 对话示例 **用户**：帮我记录今天的打卡：步行8000步，依从度80分 **助手操作**： ```bash curl -s -X POST http://localhost:8090/api/commits \ -H "Content-Type: application/json" \ -d '{"branch_id":1,"user_id":1,"task_type":"exercise","evidence_text":"步行8000步","metric_value":8000,"adherence_score":80}' ``` --- **用户**：帮我提交本周干预计划的 PR，低风险 **助手操作**： ```bash curl -s -X POST http://localhost:8090/api/prs \ -H "Content-Type: application/json" \ -d '{"branch_id":1,"requested_by":1,"summary":"本周饮食控制+步行计划","risk_level":"low"}' ``` --- **用户**：审核一下 PR #1，批准通过 **助手操作**： ```bash curl -s -X POST http://localhost:8090/api/prs/1/review \ -H "Content-Type: application/json" \ -H "x-[REDACTED_SECRET]" \ -d '{"reviewer_id":2,"action":"approve","review_note":"依从良好，计划合理"}' ``` --- **用户**：把药物变更检查的关键词加上"adjust dose" **助手操作**： ```bash # 先获取当前规则 curl -s http://localhost:8090/api/rules | python3 -m json.tool # 更新关键词 curl -s -X PATCH http://localhost:8090/api/rules/MEDICATION_CHANGE_REVIEW \ -H "Content-Type: application/json" \ -d '{"config_json":{"keywords":["increase medication","new drug","double dose","insulin","adjust dose"]}}' ``` --- **用户**：看一下当前指标怎么样 **助手操作**： ```bash curl -s http://localhost:8090/api/metrics | python3 -m json.tool ``` 返回字段：`commit_rate`（总打卡数）、`merge_rate`（审核通过率）、`blocked_rate`（被拦截率）、`avg_adherence`（平均依从分） ## 检查规则说明 系统内置两条可配置规则： - **MEDICATION_CHANGE_REVIEW**：PR summary 含药物变更关键词时自动拦截，需人工 review - **ADHERENCE_GATE**：最近一次打卡依从分低于阈值（默认50）时拦截 规则均可通过 `PATCH /api/rules/{rule_id}` 动态调整，无需重启服务。 ## 注意事项 - `merge` = 用户+专家双重认可的正强化事件，适合作为 RL reward 信号 - 所有操作写入 `events` 表，可用 `GET /api/events?event_type=merge_completed` 过滤 - 鉴权默认关闭；生产环境请设置 `AUTH_ENABLED=true` 并配置 API key


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: ivangdavila/real-estate-skill (version 1.0.0)

--- slug: real-estate-skill name: Real Estate description: Guide any property decision for buyers, sellers, landlords, investors, or agents in any jurisdiction. --- ## First: Clarify Context Before any substantive guidance: 1. **Role** — Buyer, seller, landlord, tenant, investor, or agent? 2. **Jurisdiction** — Country, state/province, city (laws vary dramatically) 3. **Stage** — Exploring, actively searching, under contract, closing, post-purchase? ## Protocol by Role ### Buyers ``` Affordability → Pre-approval → Search → Offer → Due Diligence → Close ``` See `buyer-guide.md` for detailed process, hidden costs checklist, and red flags. ### Sellers ``` Pricing → Prep → List → Showings → Negotiate → Close ``` Focus: CMA-based pricing, repair ROI analysis, disclosure requirements. ### Investors ``` Source → Analyze → Finance → Acquire → Operate → Exit ``` See `investor-toolkit.md` for deal analysis formulas, BRRRR modeling, and stress testing. ### Landlords ``` Screen → Lease → Manage → Maintain → Renew/Terminate ``` Tenant screening criteria, lease essentials, and eviction procedures (jurisdiction-specific). ### Agents Drafting assistant for: listing descriptions, client communications, negotiation prep, market updates. ## Deal Analysis (Investors) Quick metrics to calculate: - **Cap Rate** = NOI / Purchase Price - **Cash-on-Cash** = Annual Cash Flow / Cash Invested - **GRM** = Price / Gross Annual Rent - **1% Rule** = Monthly rent ≥ 1% of price (screening only) See `investor-toolkit.md` for full analysis framework with stress testing. ## Negotiation Support For offers, counteroffers, and difficult conversations: - Contingency strategy (inspection, financing, appraisal) - Repair request framing - Price reduction conversations See `negotiation.md` for scripts and tactical guidance. ## Compliance (Non-Negotiable) **Every response involving specifics must include:** - Jurisdiction disclaimer (laws vary by location) - Professional consultation recommendation for contracts, financing, tax - Fair housing compliance (zero tolerance for discrimination guidance) See `legal-compliance.md` for required disclaimers and jurisdiction flags. ## Cannot Do - Provide binding contracts (educational templates only) - Recommend specific mortgage products (requires licensing) - Personalized investment advice on securities (REITs, syndications) - Handle transaction funds or wire transfer guidance References: `buyer-guide.md`, `investor-toolkit.md`, `negotiation.md`, `legal-compliance.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yijunyu/precc (version 0.2.4-1)

# PRECC — Predictive Error Correction for Claude Code PRECC saves **~34% of Claude Code costs** through three pillars: fixing bash commands before they fail, compressing tool output, and reducing context token usage via semantic search and file compression. Ships as a single Rust binary. ## Three Savings Pillars ### Pillar 1: Command Correction & Output Compression - **Fixes wrong-directory commands** — Detects when `cargo build` or `npm test` is run in the wrong directory and prepends `cd /correct/path &&` - **Prevents repeated failures** — Learns from past session failures and auto-corrects commands that would fail the same way - **Compresses CLI output** — Rewrites commands to use RTK for 60-90% smaller output - **Suggests GDB debugging** — When a command fails repeatedly, suggests `precc debug` ### Pillar 2: Semantic Code Search (cocoindex-code) - Intercepts `grep`/`rg` and redirects through AST-aware semantic search - Built into the `precc-hook` binary; no extra scripts needed - Understands code structure across 28+ languages, saving ~70% of search tokens ### Pillar 3: Context File Compression - Strips filler words from CLAUDE.md and memory files via `precc compress` - Reduces tokens loaded on every API call (~30% compression) - Backups saved automatically, revertible with `precc compress --revert` ## Install ```bash curl -fsSL https://raw.githubusercontent.com/yijunyu/precc-cc/main/scripts/install.sh | bash precc init ``` ## Usage Once installed, PRECC works automatically as a PreToolUse hook. ```bash # Mine existing session history for failure-fix patterns precc ingest --all # View what PRECC has learned precc skills list # View unified savings report (all three pillars) precc savings # Semantic code search (requires cocoindex-code) ccc init && ccc index ccc search "authentication middleware" # Compress context files precc compress --dry-run # preview precc compress # compress precc compress --revert # revert ``` ## Measured Results | Metric | Value | |--------|-------| | **Cost savings** | **$296 / $878 (34%)** | | **Failures prevented** | **352 / 358 (98%)** | | **Bash calls improved** | **894 / 5,384 (17%)** | | **Cache reads saved** | **988M / 1.67B tokens (59%)** | | **Hook latency** | **2.93ms avg (1.77ms overhead)** | ## Links - GitHub: https://github.com/yijunyu/precc-cc - ClawHub: https://clawhub.ai/skills/precc - cocoindex-code: https://github.com/cocoindex-io/cocoindex-code - token-saver: https://clawhub.ai/skills/token-saver - RTK: https://github.com/rtk-ai/rtk


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivangdavila/contract (version 1.0.0)

--- name: Contract slug: contract description: Write contracts from scratch with guided intake, multi-party negotiation, risk analysis, and version control for lawyers, freelancers, and individuals. --- ## Role Draft contracts through a structured process. Gather requirements, anticipate problems, generate clauses, manage versions. Distinct from `contracts` skill (which manages existing contracts). **Key difference:** This skill CREATES contracts. The `contracts` skill MANAGES signed ones. --- ## Storage ``` ~/contracts/ ├── drafting/ # Active contract drafts │ └── {contract-name}/ │ ├── current.md # ALWAYS read this (latest version) │ ├── versions/ # v001.md, v002.md, ... │ ├── intake.md # Discovery answers │ ├── parties.md # All parties + interests │ └── notes.md # Negotiation log ├── {contract-name}/ # Finalized (compatible with contracts skill) │ ├── executed.pdf │ └── meta.md ``` **Version rule:** NEVER edit in place. Copy to versions folder, increment number, edit copy, update current.md. --- ## Quick Reference | Topic | File | |-------|------| | Contract writing phases | `phases.md` | | Discovery questions by type | `intake.md` | | Clause patterns by category | `clauses.md` | | Risk analysis and disclaimers | `risks.md` | --- ## Process Summary 1. **Discovery** — Identify type, parties, jurisdiction. Load `intake.md`, ask questions. Cannot proceed without answers. 2. **Structure** — Define sections based on type. Check mandatory clauses. 3. **Draft** — Generate clause by clause. Offer alternatives for critical ones. 4. **Review** — Analyze risks, detect ambiguities, check coherence. 5. **Negotiate** — If multiple parties, track positions, propose compromises. 6. **Finalize** — Human approval required. Move to ~/contracts/{name}/ when signed. See `phases.md` for detailed phase requirements. --- ## ⚠️ Mandatory Disclaimer Include in EVERY draft: ``` This document was generated by AI. It does NOT constitute legal advice. Have it reviewed by a licensed attorney in the applicable jurisdiction before signing. ``` --- ## Boundaries - **NO legal advice** — Cannot assess validity in specific jurisdictions - **Escalate to lawyer** — M&A, securities, labor disputes, government contracts, patents - Ask rather than assume. Unclear requirements = ask again.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: lihanghang/memect-ppx (version 0.2.6)

--- name: memect-ppx version: 0.2.6 title: memect-ppx user-invocable: true description: > Parse PDFs and images into Markdown/JSON using the memect-ppx (`ppx`) CLI. Use when the user asks to OCR scanned PDFs or screenshots, extract tables from PDFs, convert PDF/image to Markdown, preserve document layout, inspect parsing output. Also triggers on: memect-ppx、ppx parse、解析PDF、图片转文字、扫描件识别、扫描件转文字、提取表格、 PDF转Markdown、文档解析、OCR识别、识别图片文字、解析图片、提取文档内容。 metadata: openclaw: requires: bins: - ppx homepage: https://github.com/memect/memect-ppx --- # memect-ppx Use the local `ppx` CLI to parse PDFs and images into structured Markdown and JSON. ## Runtime Requirements - Use Python `>= 3.12`. - Prefer installing PPX into a virtual environment instead of the system Python. - If `ppx` is missing, read `references/troubleshooting.md` and create a virtual environment before installing dependencies. - Keep this skill's frontmatter `version` synchronized from the repository `pyproject.toml` with `scripts/sync_version.py`. ## Workflow 1. Confirm the runtime uses Python `>= 3.12`. 2. Check the runtime with `scripts/check_ppx_env.sh`. 3. If `ppx` is missing, create or use a virtual environment and install PPX there. 4. Choose parsing options: - Use `--ocr auto` by default. - Use `--ocr yes` for scanned PDFs or screenshots. - Use `--ocr no` for native PDFs when OCR causes noise. - Use `--table auto` by default. - Use `--table llm` only when the user needs highest table accuracy and an LLM backend is configured. 5. Run `ppx parse <input> -o <output>`. 6. Inspect the output folder and report the main artifacts: - `doc.md` - `doc.json` - `pages/` - `images/` when figures are extracted 7. If parsing fails, summarize the failing step and load the relevant note from `references/`. ## Common Commands ```bash ppx parse report.pdf -o output/ ppx parse scan.pdf --ocr yes -o output/ ppx parse figure.png -o output/ ppx parse report.pdf --pages "1-5,10" -o output/ ppx parse report.pdf --table llm --backend deepseek -o output/ ``` ## Output Contract - Prefer returning the absolute output directory. - Mention whether the result came from `doc.md`, `doc.json`, or page-level files. - Call out OCR mode, table mode, and backend when they materially affect accuracy. ## References - Read `references/cli-options.md` when choosing parse flags. - Read `references/backend-config.md` when using DeepSeek, Paddle, or GLM backends. - Read `references/troubleshooting.md` when PPX is missing, Python is too old, or runtime dependencies fail.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: glittercowboy/gsd-agent (version 1.0.0)

--- name: gsd-agent 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-agent → 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: sherlockfate/minimax-token (version 2.2.1)

--- name: minimax-token description: 检查 MiniMax API Token 剩余配额。支持定时检查并通过 Telegram 发送通知。适用于：查询 Token 余额、配置定时监控、设置余额不足提醒。 --- # MiniMax Token 检查工具 查询 MiniMax API 的 Token 剩余配额，支持定时检查和 Telegram 通知。 ## 功能特性 - ✅ 查询 MiniMax API Token 剩余配额 - ✅ 支持定时自动检查（每小时） - ✅ 通过 Telegram 发送通知 - ✅ 支持环境变量配置 - ✅ systemd 服务支持 ## 依赖 ```bash # Python 依赖 pip3 install requests # 系统依赖 # - curl (用于 API 请求) # - python3 ``` ## 环境变量 | 变量 | 说明 | 必需 | |------|------|------| | `MINIMAX_API_KEY` | MiniMax API Key | 是 | | `TELEGRAM_BOT_TOKEN` | Telegram Bot Token | 否 | | `TELEGRAM_CHAT_ID` | Telegram Chat ID | 否 | | `OPENCLAW_LOG_DIR` | 日志目录 (默认 ~/.openclaw/logs) | 否 | ## 使用方法 ### 1. 配置环境变量 ```bash # 方式一: 环境变量 export MINIMAX_API_KEY="your_api_key" export TELEGRAM_BOT_TOKEN="your_bot_token" export TELEGRAM_CHAT_ID="your_chat_id" # 方式二: 运行参数 python3 minimax_token.py --check --api-key "your_api_key" ``` ### 2. 单次检查 ```bash python3 minimax_token.py --check ``` ### 3. 启动定时监控 ```bash python3 minimax_token.py --monitor ``` ### 4. 安装为系统服务 (Linux) ```bash # 复制 service 文件 cp minimax-token.service ~/.config/systemd/user/ # 启用服务 systemctl --user enable minimax-token systemctl --user start minimax-token # 查看日志 journalctl --user -u minimax-token -f ``` ## 配置说明 所有敏感配置都通过环境变量读取，确保安全： - API Key: `MINIMAX_API_KEY` - Telegram Bot: `TELEGRAM_BOT_TOKEN` - Chat ID: `TELEGRAM_CHAT_ID` ## 输出示例 ``` 📊 MiniMax-M2.5 配额状态 • 剩余时间: 50小时 30分钟 • 本周期: 已用 150/1000 次 • 剩余: 850 次 ``` ## 文件结构 ``` minimax-token/ ├── SKILL.md └── scripts/ ├── minimax_token.py # 主脚本 └── minimax-token.service # systemd 服务配置 ```


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

--- name: ai-content-pipeline description: An end-to-end AI Content Pipeline that crawls articles, rewrites them using Google Gemini, and automatically publishes to Facebook Fanpage. version: 2.0.0 env: - GEMINI_API_KEY - APIFY_API_TOKEN - FB_APP_ID - FB_APP_SECRET - FB_PAGE_ID - FB_PAGE_ACCESS_TOKEN --- # 🤖 OpenClaw AI Content Pipeline (Analyze + Publish) ## Purpose This is a production-ready OpenClaw Skill that merges a Web Crawler, an AI Rewriter (Google Gemini), and a Facebook Auto-Publisher into one seamless pipeline. It reads URLs (news articles or Facebook posts), rewrites them into engaging social media captions, and posts them directly to your Fanpage. ## Core Capabilities - **Analyze Mode**: Extracts data from URLs via Python `requests`/`BeautifulSoup` (for static news) or `Apify` (for JS-heavy content like Facebook). Generates localized rewritten text via Gemini 2.5 Flash. - **Publish Mode**: Pushes the generated content directly to a Facebook Page via Graph API v21.0. - **Batch Processing**: Can ingest URLs line-by-line from a text file, process them in bulk, and save JSON reports. ## Architecture Map - `run.bat` / `main.py` ← Central Orchestrators (CLI Entry points) - `agents/crawler_agent.py` ← Hybrid Crawler (Native + Apify API) - `agents/writer_agent.py` ← AI Content Generator (Gemini Integration) - `agents/fb_publisher_agent.py` ← Graph API Poster (Messages & Images) - `config.py` ← Environment Loader & System Validation ## Setup & Environment Ensure you create a `.env` file at the root with the following variables: ```env # AI Models GEMINI_API_KEY=... OPENAI_API_KEY=... # Optional: For DALL-E 3 image generation # Scraping APIFY_API_TOKEN=... # Facebook Graph API FB_APP_ID=... FB_APP_SECRET=... FB_PAGE_ID=... FB_PAGE_ACCESS_TOKEN=... ``` ## Usage commands From your terminal, run the following commands (Windows `run.bat` wrappers): ### 1. Analyze (Test scrape and text rewrite) ```cmd # Analyze a single URL run.bat analyze "https://vnexpress.net/..." # Analyze and save as JSON run.bat analyze "https://dantri.com.vn/..." --save # Batch analyze from a text file run.bat analyze-file urls.txt ``` ### 2. Publish (Post directly to Facebook) ```cmd # Test Graph API Token connection run.bat test run.bat test-post # Full pipeline (Crawl -> Rewrite -> Post to Fanpage) run.bat run "https://dantri.com.vn/..." # Pipeline dry-run (No actual post) run.bat dry "https://dantri.com.vn/..." ```

path: agents/__init__.py
content: """agents/__init__.py"""


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: leooooooow/comment-to-content-recycler (version 1.0.3)

--- name: comment-to-content-recycler description: Transform comments, DMs, and FAQ threads into prioritized content ideas and script-ready angles. Use when the user wants audience-driven topics, needs repeatable idea generation, or wants to turn community questions into high-relevance short-form content. --- # Comment To Content Recycler ## Skill Card - **Category:** Market Intelligence - **Core problem:** How to convert comments/DMs into content pipeline? - **Best for:** Always-on content ideation - **Expected input:** Comments, DMs, objections, FAQ snippets - **Expected output:** Intent clusters + prioritized content backlog - **Creatop handoff:** Send high-priority clusters into Creatop script queue ## What this does Convert audience questions into ranked, script-ready content topics. ## Workflow ### 1) Ingest and clean input Collect recent comments/DM/FAQ items (e.g., 7–30 days). Normalize by: - removing duplicates - merging near-identical phrasing - preserving representative audience wording ### 2) Cluster by intent Group into: - how-to - comparison - troubleshooting - buying concerns - objections/myths ### 3) Prioritize clusters Score each cluster: - frequency - urgency - monetization relevance - ease of production Rank clusters before writing outputs. ### 4) Generate content outputs For top clusters, provide: - topic/title - 3 hook options - short script angle - CTA tied to audience language ### 5) Close the loop Add: - which comments to reply first - one follow-up question to collect better demand signals ## Quality rules - Keep audience language intact where useful. - Avoid generic ideas disconnected from real comments. - Prioritize repeated pains with commercial relevance. ## License Copyright (c) 2026 **Razestar**. This skill is provided under **CC BY-NC-SA 4.0** for non-commercial use. You may reuse and adapt it with attribution to Razestar, and share derivatives under the same license. Commercial use requires a separate paid commercial license from **Razestar**. No trademark rights are granted.


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