OpenClaw/clawhub-security-signals huggingface dataset:
Skill: peand-rover/optometrist-video (version 1.0.0)

name: optometrist-video version: "1.0.0" displayName: "Optometrist Video — New Patient and Eyewear Collection Videos for Optometry Practices" description: > You are a video assistant for optometry practices and independent eye care professionals — helping them show their comprehensive exam process, their eyewear selection experience, and their specialty care capabilities through short new patient and optical collection videos. Optometrist Video turns your advanced diagnostic technology, your frame selection process, and your patient care philosophy into clips that answer the question every person who has been going to a retail optical chain asks: is this the eye doctor who will actually take the time to understand how I use my vision — whether that's extended screen time, distance driving, or athletics — and prescribe accordingly, with frames I'll actually want to wear? tags: - optometrist video - eye doctor video - optometry marketing video - eye care video - vision care video --- # Optometrist Video Help optometry practices create new patient and optical showcase videos. ## What You Do 1. Ask what type of video (practice overview, technology showcase, eyewear collection, dry eye/specialty care, patient experience, doctor introduction) 2. Gather: practice type (medical/retail/specialty), technology used (OCT/retinal imaging/corneal topography), optical collection style, patient demographic 3. Generate 60-90 second script; suggest b-roll: comprehensive exam with advanced equipment, frame styling with patient, doctor reviewing retinal images, optical boutique display ## Video Prompts Use bright, modern optical and exam room settings, close-ups of diagnostic technology in use, warm frame selection interactions between staff and patient, and direct-to-camera doctor segments that convey thorough, unhurried eye care.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dxie48892-jpg/web-automation-helper (version 1.0.1)

--- name: web-automation-helper description: 浏览器自动化助手。通过Chrome远程调试模式，自动化执行网页操作，包括数据抓取、表单填写、内容发布、截图等。 --- # Web Automation Helper - 浏览器自动化助手 ## 快速开始 ```bash # 1. 启动Chrome远程调试 chrome.exe --remote-debugging-port=9222 # 2. 运行示例 cd scripts node cdp-helper.js --url https://example.com ``` ## 目录结构 ``` web-automation-helper/ ├── SKILL.md ├── README.md ├── scripts/ │ └── cdp-helper.js ``` ## 主要功能 - 网页截图 - 数据抓取 - 表单填写


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: openlark/plagiarism-checker (version 1.0.0)

--- name: plagiarism-checker description: Detect text originality and AI-generated content. --- # Plagiarism Checker Detect whether text is plagiarized or AI-generated. Provides content originality analysis to help identify copied content and machine-generated text. ## Use Cases Use this skill when users mention "plagiarism check," "detect plagiarism," "check AI rate," "article originality," "text originality detection," "content originality degree," or request similarity/AI rate analysis for a piece of text. ## Core Script Use `scripts/check_plagiarism.py` to perform detection: ```bash python scripts/check_plagiarism.py --text "Text content to detect" ``` Optional parameters: - `--text`: Text to detect (required, or use `--file`) - `--file`: Read text from file (.txt, .md, .docx) - `--output`: Output report path (prints to terminal by default) **Windows Example:** ```powershell python scripts/check_plagiarism.py --text "Your text here..." ``` ## Output Format The script returns a structured report: ``` === Originality Detection Report === Originality Score: 92% AI Generation Probability: 12% [Paragraph Analysis] ✓ Paragraph 1: Original content, no match ⚠ Paragraph 2: 47% similarity, rewriting recommended ⚠ Paragraph 3: 31% AI generation probability ``` ## References - `references/detection-guide.md` — Detection methods, threshold standards, and best practices - `references/report-template.md` — Report template and examples ## Workflow 1. **Collect Text**: User pastes text or uploads a file 2. **Execute Detection**: Run `check_plagiarism.py` 3. **Interpret Report**: Provide recommendations based on originality score and AI probability 4. **Provide Rewriting Suggestions**: Offer optimization advice for low-scoring paragraphs


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: engsathiago/xquads-brand-squad (version 1.0.0)

--- name: xquads-brand-squad description: Squad de 15 especialistas em branding - David Aaker, Marty Neumeier, Byron Sharp, etc. Use para brand positioning, brand identity, naming, brand story, archetypes. metadata: {"openclaw":{"homepage":"https://github.com/synkra/xquads","emoji":"🏷️"}} --- # Brand Squad 15 especialistas em branding e posicionamento de marca. ## Agentes - **brand-chief** - Orchestrator - **david-aaker** - Brand identity, brand equity - **marty-neumeier** - Brand positioning, zag - **byron-sharp** - How brands grow, mental availability - **kevin-keller** - Brand equity model - **jean-noel-kapferer** - Brand identity prism - **al-ries** - Positioning, focus - **donald-miller** - Brand story, storytelling - **emily-heyward** - Brand strategy - **denise-yohn** - Brand culture - **alina-wheeler** - Design process - **miller-sticky-brand** - Sticky branding - **archetype-consultant** - Brand archetypes - **naming-strategist** - Brand naming - **domain-scout** - Domain strategy ## Tasks - create-positioning - Brand positioning - build-identity - Brand identity - create-brand-story - Brand story - map-archetype - Brand archetype - generate-names - Brand naming - design-architecture - Brand architecture - audit-brand - Brand audit - diagnose - Triage - review - Quality gate


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: wu-uk/hvac-control-excitation-signal-design (version 0.1.0)

--- name: excitation-signal-design description: Design effective excitation signals (step tests) for system identification and parameter estimation in control systems. --- # Excitation Signal Design for System Identification ## Overview When identifying the dynamics of an unknown system, you must excite the system with a known input and observe its response. This skill describes how to design effective excitation signals for parameter estimation. ## Step Test Method The simplest excitation signal for first-order systems is a step test: 1. **Start at steady state**: Ensure the system is stable at a known operating point 2. **Apply a step input**: Change the input from zero to a constant value 3. **Hold for sufficient duration**: Wait long enough to observe the full response 4. **Record the response**: Capture input and output data at regular intervals ## Duration Guidelines The test should run long enough to capture the system dynamics: - **Minimum**: At least 2-3 time constants to see the response shape - **Recommended**: 3-5 time constants for accurate parameter estimation - **Rule of thumb**: If the output appears to have settled, you've collected enough data ## Sample Rate Selection Choose a sample rate that captures the transient behavior: - Too slow: Miss important dynamics during the rise phase - Too fast: Excessive data without added information - Good practice: At least 10-20 samples per time constant ## Data Collection During the step test, record: - Time (from start of test) - Output measurement (with sensor noise) - Input command ```python # Example data collection pattern data = [] for step in range(num_steps): result = system.step(input_value) data.append({ "time": result["time"], "output": result["output"], "input": result["input"] }) ``` ## Expected Response Shape For a first-order system, the step response follows an exponential curve: - Initial: Output at starting value - Rising: Exponential approach toward new steady state - Final: Asymptotically approaches steady-state value The response follows: `y(t) = y_initial + K*u*(1 - exp(-t/tau))` Where K is the process gain and tau is the time constant. ## Tips - **Sufficient duration**: Cutting the test short means incomplete data for fitting - **Moderate input**: Use an input level that produces a measurable response without saturating - **Noise is normal**: Real sensors have measurement noise; don't over-smooth the data - **One good test**: A single well-executed step test often provides sufficient data


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: yang1002378395-cmyk/ai-intelligent-3d-model-generation (version 1.0.0)

--- name: ai-intelligent-3d-model-generation version: 1.0.2 description: AI 3D模型生成，文生3D + 图片转3D。 metadata: openclaw: emoji: "🤖" requires: bins: [] --- # AI 智能3D模型生成 ## 描述 AI 3D模型生成，文生3D + 图片转3D。 ## 功能 - 文生3D（文本生成模型） - 图片转3D（图片生成模型） - 模型优化（自动优化） - 材质生成（智能材质） - 批量生成（批量处理） ## 定价 - 基础版：¥299/月（100 个模型） - 专业版：¥1499/月（1000 个模型） - 企业版：¥4999/月（无限模型） ## 适用场景 - 游戏开发 - VR/AR - 建筑设计 - 产品设计 ## 技术栈 - Python + FastAPI - 3D生成模型 - 模型优化 - 渲染引擎 ## 安装 ```bash git clone https://github.com/openclaw-skills/ai-intelligent-3d-model-generation cd ai-intelligent-3d-model-generation pip install -r requirements.txt python app.py ``` --- 创建：2026-03-13 作者：OpenClaw Skills Team


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: fanyanggod/tiktok-ad-library-creative-center-ads-examples-top-ads (version 1.0.2)

--- name: tiktok-ad-library-creative-center-ads-examples-top-ads description: Research TikTok ad creatives, examples, and top-performing ad patterns using PipiAds TikTok ad discovery tools. metadata: openclaw: requires: env: - PIPIADS_API_KEY bins: - npm install: command: npm args: - install - -g - "pipiads-mcp-server@1.0.3" primaryEnv: PIPIADS_API_KEY emoji: "📊" homepage: https://www.pipiads.com mcpServers: pipiads: command: pipiads-mcp-server env: PIPIADS_API_KEY: "{{PIPIADS_API_KEY}}" --- # TikTok Ad Library & TikTok Creative Center & TikTok Ads Examples & TikTok Top Ads Use this skill for TikTok-first creative research, ad inspiration, and top-ad example discovery. ## Setup 1. Visit the official website at [https://pipiads.com/](https://pipiads.com/) to view real-time dashboard data and product UI. 2. Visit [https://www.pipispy.com/](https://www.pipispy.com/) to get your API key and manage billing/recharge. 3. Set the environment variable: `PIPIADS_API_KEY` ## When to Use - Find TikTok ad examples, top ads, and winning hooks - Explore TikTok creatives by keyword, region, and engagement - Analyze advertiser patterns behind strong TikTok campaigns - Research products and landing pages attached to TikTok ads ## When NOT to Use - The user wants Facebook monitoring workflows - The user wants Facebook ad library research - The user wants TikTok Shop store analytics without ad creative discovery ## Core Tools - `search_ads` — Search ads and narrow to TikTok - `get_ad_detail` — Inspect a top TikTok ad - `search_products` — Find products promoted by TikTok ads - `get_product_detail` — Inspect product and landing page detail - `search_advertisers` — Find advertisers behind TikTok creatives - `get_advertiser_detail` — Review advertiser detail - `search_stores` — Find stores connected to ad activity - `get_store_detail` — Inspect a store profile ## Recommended Workflow 1. Start with `search_ads` using TikTok filters plus keyword, region, and time range. 2. Review strong examples with `get_ad_detail`. 3. If the user wants the promoted product or store, follow with product or store detail tools. 4. Summarize hooks, UGC patterns, visual styles, and offer angles. ## Cost Awareness Each API call consumes credits from your PipiAds account: - **List/Search**: 1 credit per result returned - **Detail**: 1 credit per request (free if queried within 3 days) Ask for niche, region, and time range before broad top-ad searches.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: economics-inflation-tracker (version 1.0.1)

# Economics Inflation Tracker Tracks Consumer Price Index (CPI), Producer Price Index (PPI), and overall inflation rates across different economies to monitor purchasing power. ## Features - **CPI/PPI Data**: Real-time updates on inflation indices - **Core Inflation**: View inflation excluding volatile components (food, energy) - **Forecasts**: Access projected inflation targets ## Pricing - **Price**: 0.001 USDT per API call - **Payment**: Integrated via SkillPay.me ## Use Cases - Monitoring cost of living - Adjusting business pricing strategies - Analyzing central bank policies ## Example Input ```json { "region": "Eurozone", "index": "CPI" } ``` ## Example Output ```json { "success": true, "region": "Eurozone", "index": "CPI", "rate": "2.4%", "message": "Inflation data updated for Eurozone." } ``` ## Integration This skill is integrated with SkillPay.me for automatic micropayments. Each call costs 0.001 USDT.


Security scan verdicts — ClawScan: malicious | static analysis: clean | VirusTotal: suspicious | SkillSpector:  ()
Skill: balkanblbn/code-quality-guard (version 1.0.0)

--- name: code-quality-guard description: Professional pre-deployment code review and quality enforcement. Ensures imports are valid, tags are closed, and logic follows best practices before announcing a build is live. --- # Code Quality Guard Ship cleaner code, faster. Never let a missing import break your production again. ## Checklist 1. **Import Sweep**: Check every component used against the import block. 2. **Tag Verification**: Ensure all JSX/HTML tags are balanced. 3. **Environment Audit**: Verify required env vars and ports. 4. **Log Review**: Scan for debug prints and secrets. ## Usage Run as a pre-build hook to catch "ReferenceErrors" before the human sees them. ## Installation ```bash clawhub install code-quality-guard ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: ezhencacao-dotcom/bid-reader (version 1.0.0)

# bid-reader Skill ## Overview A lightweight skill to extract readable text from bid and tender documents in PDF, Word (`.docx`), and Excel (`.xlsx`/`.xls`) formats. It can be invoked from the OpenClaw UI or other agents to quickly pull the full textual content of a file for analysis, search, or summarisation. ## Usage ``` bid-read <file-path> ``` - `<file-path>` should be an absolute or workspace‑relative path to a document. - The skill prints the extracted plain‑text to stdout, which OpenClaw captures and returns to the caller. ## Example ```bash bid-read /home/zhenxing/投标文件/招投标项目1/13.上海联通/投标文件.pdf ``` The command returns the full text of the PDF, ready for further processing (e.g., keyword search, summarisation). ## Installation Copy the skill folder into your workspace under `skills/bid-reader`. Install required Python packages: ```bash pip install -r $(pwd)/skills/bid-reader/requirements.txt ``` The skill is then available as an agent command. ## Implementation Details - **PDF**: Uses `pdfplumber` to extract text page‑by‑page. - **Word**: Uses `python-docx` to read paragraphs. - **Excel**: Uses `pandas` (with `openpyxl`/`xlrd`) to read all sheets and concatenate cell values. ## Limitations - Only `.pdf`, `.docx`, `.xlsx`, and `.xls` are supported. Other formats will be ignored. - Large files may take a few seconds to process. - Tables are flattened into whitespace‑separated rows; complex formatting is not preserved. ## Future Enhancements - Add OCR fallback for scanned PDFs (e.g., via `pytesseract`). - Support selective page or sheet extraction. - Provide a JSON output mode with structural metadata.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: cinience/aliyun-adb-mysql (version 1.0.0)

--- name: aliyun-adb-mysql description: Use when managing Alibaba Cloud AnalyticDB for MySQL (ADB) via OpenAPI/SDK, including the user needs AnalyticDB resource lifecycle and configuration operations, status checks, or troubleshooting ADB API and cluster workflow issues. version: 1.0.0 --- Category: service # AnalyticDB for MySQL Use Alibaba Cloud OpenAPI (RPC) with official SDKs or OpenAPI Explorer to manage resources for AnalyticDB for MySQL. ## Workflow 1) Confirm region, resource identifiers, and desired action. 2) Discover API list and required parameters (see references). 3) Call API with SDK or OpenAPI Explorer. 4) Verify results with describe/list APIs. ## AccessKey priority (must follow) 1) Environment variables: `ALICLOUD_ACCESS_KEY_ID` / `ALICLOUD_ACCESS_KEY_SECRET` / `ALICLOUD_REGION_ID` Region policy: `ALICLOUD_REGION_ID` is an optional default. If unset, decide the most reasonable region for the task; if unclear, ask the user. 2) Shared config file: `~/.alibabacloud/credentials` ## API discovery - Product code: `adb` - Default API version: `2021-12-01` - Use OpenAPI metadata endpoints to list APIs and get schemas (see references). ## High-frequency operation patterns 1) Inventory/list: prefer `List*` / `Describe*` APIs to get current resources. 2) Change/configure: prefer `Create*` / `Update*` / `Modify*` / `Set*` APIs for mutations. 3) Status/troubleshoot: prefer `Get*` / `Query*` / `Describe*Status` APIs for diagnosis. ## Minimal executable quickstart Use metadata-first discovery before calling business APIs: ```bash python scripts/list_openapi_meta_apis.py ``` Optional overrides: ```bash python scripts/list_openapi_meta_apis.py --product-code <ProductCode> --version <Version> ``` The script writes API inventory artifacts under the skill output directory. ## Output policy If you need to save responses or generated artifacts, write them under: `output/aliyun-adb-mysql/` ## Validation ```bash mkdir -p output/aliyun-adb-mysql for f in skills/database/analyticdb/aliyun-adb-mysql/scripts/*.py; do python3 -m py_compile "$f" done echo "py_compile_ok" > output/aliyun-adb-mysql/validate.txt ``` Pass criteria: command exits 0 and `output/aliyun-adb-mysql/validate.txt` is generated. ## Output And Evidence - Save artifacts, command outputs, and API response summaries under `output/aliyun-adb-mysql/`. - Include key parameters (region/resource id/time range) in evidence files for reproducibility. ## Prerequisites - Configure least-privilege Alibaba Cloud credentials before execution. - Prefer environment variables: `ALICLOUD_ACCESS_KEY_ID`, `ALICLOUD_ACCESS_KEY_SECRET`, optional `ALICLOUD_REGION_ID`. - If region is unclear, ask the user before running mutating operations. ## References - Sources: `references/sources.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mayuran1096/exec-tool (version 1.0.0)

# Exec Tool (ClawHub Skill) ## Overview Exec Tool is a controlled system command execution skill designed for OpenClaw environments. It allows secure execution of predefined CLI commands from within ClawHub workflows. This skill is intended for automation, debugging, and controlled system operations inside VPS environments. --- ## Problem it solves When working in server environments (such as Hostinger VPS deployments), users often need to trigger system-level commands from automation layers like Telegram bots or OpenClaw agents. This skill bridges that gap safely. --- ## Core functionality - Executes only allowed system commands - Currently supports: `clawhub` CLI operations - Prevents arbitrary or unsafe command execution - Returns trimmed output for chat interfaces (e.g., Telegram) --- ## Security model This skill is intentionally restricted: - Only whitelisted commands are executed - Prevents destructive operations (rm, sudo, etc.) - Designed for controlled automation environments --- ## Example usage ### Search skills clawhub search "Google Workspace" ### Install skill clawhub install exec_tool --- ## Integration use case This skill is commonly used in: - Telegram bot automation - OpenClaw workflows - VPS management via chat interfaces - CI/CD command triggers --- ## Notes This skill is part of a controlled execution layer and should not be used for unrestricted shell access.

path: clawdis.json
content: slug: exec_tool
name: Exec Tool
version: 1.0.0
type: skill
entry: tool.py
runtime: python

path: skill.yaml
content: slug: exec_tool
name: Exec Tool
version: 1.0.0
type: skill

entry: tool.py
runtime: python


path: tool.py
content: import subprocess

def exec_command(command: str) -> str:
    """
    Execute a safe shell command and return output
    """

    # 🔒 Basic safety restriction (VERY IMPORTANT)
    allowed_commands = ["clawhub"]

    if not any(command.startswith(cmd) for cmd in allowed_commands):
        return "Command not allowed"

    try:
        result = subprocess.getoutput(command)
        return result[:4000]  # Telegram limit safe
    except Exception as e:
        return str(e)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: sky-lv/skylv-prompt-evaluation (version 1.0.0)

--- name: prompt-evaluation slug: prompt-evaluation version: 1.0.0 description: "Evaluate and benchmark AI prompts for quality, consistency, and performance. Triggers: prompt evaluation, prompt testing, prompt quality, prompt benchmark, prompt optimization." author: SKY-lv license: MIT-0 tags: [evaluation, prompts, quality] keywords: [prompt evaluation, prompt testing, prompt quality, prompt benchmark, prompt optimization, LLM evaluation, prompt engineering, prompt scoring] triggers: prompt-evaluation --- # Prompt Evaluation Evaluate and benchmark AI prompts for quality, consistency, and performance. Score, compare, and optimize your prompts systematically. ## Overview A prompt evaluation framework that helps agents measure prompt quality across multiple dimensions: clarity, specificity, robustness, cost-efficiency, and output consistency. Compare prompt variants and find the optimal version. ## Capabilities ### 1. Quality Scoring ```bash node evaluate.js score --prompt "Summarize the article" --dimensions clarity,specificity,robustness node evaluate.js score --prompt-file ./prompts/ --output scores.json ``` Scores prompts on clarity (0-10), specificity (0-10), robustness (0-10), and cost-efficiency (0-10). ### 2. A/B Comparison ```bash node evaluate.js compare --prompt-a "Summarize" --prompt-b "Write a 3-bullet summary" --trials 50 node evaluate.js compare --config ab-test-config.json ``` Run statistical A/B tests between prompt variants with significance analysis. ### 3. Consistency Check ```bash node evaluate.js consistency --prompt "Translate to French" --runs 100 --variance-threshold 0.15 node evaluate.js consistency --temperature 0.7 --top-p 0.9 ``` Measures output consistency across multiple runs to find the most stable prompts. ### 4. Regression Testing ```bash node evaluate.js regression --baseline v1.0 --current v1.1 --test-suite golden-set.jsonl node evaluate.js regression --fail-on-degradation 5% ``` Detects quality regressions between prompt versions using golden test sets. ### 5. Cost Analysis ```bash node evaluate.js cost --prompt "Long prompt..." --model gpt-4 --estimate-tokens node evaluate.js cost --compare-prompts --output cost-report.csv ``` Estimates token usage and costs for different prompt variants and models. ## Configuration ```json { "evaluation": { "dimensions": ["clarity", "specificity", "robustness", "cost"], "scoringModel": "gpt-4", "abTest": { "trials": 50, "significanceLevel": 0.05 }, "consistency": { "runs": 100, "varianceThreshold": 0.15 }, "regression": { "degradationThreshold": "5%", "goldenSet": "./golden-set.jsonl" } } } ``` ## Use Cases - Prompt Engineering: Systematically improve prompt quality - Quality Assurance: Ensure prompts meet quality standards before production - Cost Optimization: Find prompts that achieve goals with fewer tokens - Version Control: Track prompt quality across versions - Agent Tuning: Optimize agent system prompts for consistency


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