OpenClaw/clawhub-security-signals huggingface dataset:
Skill: nextaltair/rr-renamer (version 1.0.0)

--- name: rr-renamer description: Use RenameRegex (RR.exe) as a generic Windows CLI bulk renamer from OpenClaw. Use when you need regex search/replace renames with preview (/p pretend), recursive renames (/r), files/dirs selection, and a safe preview→apply workflow. This skill provides a thin wrapper and logging; it does NOT define any specific normalization rules. disable-model-invocation: true --- # RR (RenameRegex) generic runner ## Assumptions - `RR.exe` (RenameRegex) is available on PATH **from the environment that runs the command**. - User stated it is placed in `~/.local/bin`. - Prefer running via **Windows PowerShell 7** (`pwsh.exe`) so Windows path semantics are authoritative. ## Safety defaults - Always run **pretend preview first** (`/p`). - Only run apply after **explicit user approval** in the chat. - Never use `/f` (force overwrite) unless the user explicitly asks. - Log every invocation. ## How to use Use the bundled script to run RR.exe inside a chosen root folder. - Preview: - provide RR arguments, script adds `/p` automatically - Apply: - provide RR arguments, script runs without `/p` See: `scripts/rr_run.ps1` ### Regex argument quoting (PowerShell) When calling from PowerShell, pass regex arguments like `\s+` as a *single backslash* string. Recommended: use **single quotes** to avoid accidental escaping: - `@('*', '\s+', '_', '/r', '/e')`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: c0ldsmi1e/cold-outreach-marketing (version 1.0.0)

--- description: "Helps define ICP, draft cold emails and DMs, and plan outreach sequences for startup sales. Use when someone needs help with sales outreach, cold emails, or finding early customers." user_invocable: true command: cold-outreach --- You are a B2B sales and cold outreach advisor for early-stage startup founders. Help them set up an effective outreach system to find their first customers. **Start by asking these discovery questions (ask all at once, then wait for answers):** 1. What does your product do and what problem does it solve? 2. Who do you think your ideal customer is? (company size, role, industry) 3. What's your price point or business model? 4. Have you done any outreach before? If so, what worked and what didn't? 5. What channels are you considering? (cold email, LinkedIn DMs, Twitter DMs, other) 6. Do you have any existing leads or a way to find prospects? **After receiving answers, provide a tailored outreach plan with these sections:** ### Ideal Customer Profile (ICP) Help them sharpen their ICP: - Target company characteristics (size, industry, tech stack, signals) - Target buyer persona (role, pain points, goals) - Early Customer Profile considerations (who is most likely to buy NOW) ### Prospect Finding Strategy Where and how to find prospects: - LinkedIn Sales Navigator search strategies - Signal-based prospecting (job changes, funding rounds, tech adoption, complaints on social media) - Tools and data sources for building lists ### Cold Email Sequence Draft a 3-email sequence tailored to their product: - Email 1: Initial outreach (problem-focused, personalized) - Email 2: Follow-up with social proof or value add - Email 3: Breakup email with soft CTA Include subject line options and personalization tips. ### Cold DM Templates If applicable, provide LinkedIn or Twitter DM templates. ### Outreach Operations - Recommended sending volume and cadence - Email deliverability basics (domain warm-up, SPF/DKIM) - Tech stack recommendations for their stage - How to track and iterate ### Action Plan Numbered checklist to get their first outreach campaign running within a week. ### Further Reading - The Ultimate Framework to build your ICP: https://www.mrrunlocked.com/p/framework-ideal-customer-profile - Before there is Ideal, there is Early (Customer Profile): https://knowledge.gtmstrategist.com/p/before-there-is-ideal-there-is-early - How to do startup sales with no experience: https://posthog.com/founders/startup-sales-strategy - The Cold Outbound Handbook: https://www.za-zu.com/handbook/intro - Cold email setup guide: https://coldstartblueprint.com/p/cold-email-setup - 4 Must-Have Email Templates in SaaS Sales: https://www.mrrunlocked.com/p/the-4-must-have-sales-email-templates - The GTM Outreach Playbook: https://knowledge.gtmstrategist.com/p/the-gtm-outreach-playbook-from-triggers-to-demos


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: simoncai519/creating-financial-models (version 1.0.0)

--- name: creating-financial-models description: Use when user wants to build financial models, DCF analysis, valuation, sensitivity analysis, e-commerce business planning, investment decisions, or project finance assessments. version: "1.0.0" --- # Creating Financial Models This skill provides a comprehensive suite for building financial models, including Discounted Cash Flow (DCF) analysis, sensitivity testing, Monte Carlo simulations, and scenario planning. It is ideal for valuation, investment decisions, e‑commerce business planning, and any situation requiring rigorous financial analysis. ## Overview - **Core workflow**: Gather inputs → run DCF model → perform sensitivity analysis → optional Monte Carlo simulation → scenario comparison. - **Outputs**: Excel workbook with full model, valuation summary, charts, and a PDF report. ## Core Workflow 1. **Collect data** - Historical financial statements (3‑5 years). - Revenue growth assumptions, margin forecasts, capex, working‑capital needs. - Discount rate components (risk‑free rate, beta, market premium) to compute WACC. 2. **Run DCF model** (`scripts/dcf_model.py`) - Projects free cash flow, computes terminal value, discounts to present value. - Generates enterprise and equity valuations. 3. **Sensitivity analysis** (built‑in in the script) - Varies key inputs (growth rate, WACC) and produces tornado charts. 4. **Monte Carlo simulation** (optional) - Executes thousands of iterations with probabilistic inputs, returns confidence intervals. 5. **Scenario planning** - Define best/base/worst cases, assign probabilities, compare outcomes. 6. **Deliver output** - `output/model.xlsx` – full model. - `output/summary.pdf` – executive summary with charts. ## Usage Examples - `Create a DCF valuation for Acme Corp using the attached financials.` - `Run a sensitivity analysis on the WACC and growth rate for the startup model.` - `Perform a Monte Carlo simulation with 5,000 iterations on the acquisition model.` - `Generate best, base, and worst case scenarios for the new e‑commerce platform rollout.` ## References - See `references/methodology.md` for detailed methodology, assumptions, and best‑practice guidelines. ## Scripts - `scripts/dcf_model.py` – Core DCF engine (Python). Use `python scripts/dcf_model.py <input.json> <output.xlsx>`. --- --- **Created by [Simon Cai](https://github.com/simoncai519) · More e-commerce skills: [github.com/simoncai519/open-accio-skill](https://github.com/simoncai519/open-accio-skill)**


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: guoqiao/dl (version 0.2.3)

--- name: dl description: Download Video/Music from YouTube/Bilibili/X/etc. author: guoqiao metadata: {"openclaw":{"always":false,"emoji":"🦞","homepage":"https://clawhub.ai/guoqiao/dl","os":["darwin","linux","win32"],"requires":{"bins":["uv"]}}} triggers: - "/dl <url>" - "Download this video ..." - "Download this music ..." --- # Media Downloader Smartly download media (Video/Music) from URLs (YouTube, Bilibili, X, etc.) to the appropriate local folders. - **Video:** Save into `~/Movies/` or `~/Videos/`. - **Music:** Save into `~/Music/`. - **Playlists:** Saves items into a subdirectory (e.g., `~/Music/<playlist_name>/`). Designed to work with a local Media Server (e.g., Universal Media Server, Jellyfin) for instant playback on TV/devices. ## Agent Procedure When the user provides a URL or asks to download media, **you MUST follow this exact sequence:** 1. **Acknowledge:** - Immediately reply to the user: "Downloading with dl skill..." 2. **Execute:** - Run the script: ```bash uv run --script ${baseDir}/dl.py "<url>" ``` 3. **Capture Path:** - Read the script output, a path will be printed to stdout at the end, points to either a single file or a folder contains the playlist items. 4. **Upload (Telegram Only):** - If the user is on Telegram (check context or session) AND the file is audio (mp3/m4a): - Use the `message` tool to send the file to the user: ```json { "action": "send", "filePath": "<filepath>", "caption": "Here is your music." } ``` ## Usage Run `dl.py` as a uv script: ```bash # save into default dirs ~/Music or ~/Movies or ~/Videos uv run --script ${baseDir}/dl.py <url> # specify your own output dir uv run --script ${baseDir}/dl.py <url> -o <out_dir> ``` The script will print output path, either a file or a folder. A optional cookies file could be provided to make yt-dlp more reliable, with which ever of these detected first: - `${baseDir}/.cookies.txt` - `$DL_COOKIES_FILE` - `$COOKIES_FILE` - `~/.cookies.txt` ## Setup (User) This skill will be much more useful if you setup a media server on same machine to share the downloaded media in your LAN: 1. Install a DLNA/UPnP Media Server (Universal Media Server, miniDLNA, Jellyfin). 2. Share `~/Music` and `~/Movies` (or `~/Videos`) folders. 3. Downloaded media will appear automatically on your TV, with apps support DLNA/UPnP, such as VLC. See [example script](https://github.com/guoqiao/skills/blob/main/dl/ums/ums_install.sh) to setup Universal Media Server on Mac.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: junjieyu95/glancely (version 0.3.0)

--- name: glancely description: > Personal tracker framework. Scaffold new habit/workout/mood/reminder trackers from natural language. Dashboard, cron, all in one skill. version: 0.3.0 metadata: openclaw: requires: bins: [python3] anyBins: [pip, pip3] os: [macos, linux] envVars: - name: GLANCE_HOME required: false description: Custom data directory (default ~/.glancely) --- ## What glancely is A personal tracker framework that puts all your habits in one dashboard. You describe what you want to track, and glancely scaffolds it — migrations, cron, dashboard panel — in one command. ## Routing user intent When a user asks for something, follow this dispatch table: | User says | Action | |------------------------|--------| | "log mood" / "how I feel" | Check `~/.glancely/components/mood/`. If exists → read SKILL.md → run log.py. If not → read `examples/mood/SKILL.md` for reference → scaffold first, then log. | | "remind me" / "add reminder" | Check `~/.glancely/components/reminder/`. Same fallback to `examples/reminder/SKILL.md`. | | "what's my MIT" / "most important task" | Check `~/.glancely/components/mit/`. Fallback to `examples/mit/SKILL.md`. | | "log diary" / "track time" | Check `~/.glancely/components/diary_logger/`. Fallback to `examples/diary_logger/SKILL.md`. | | "build dashboard" / "show dashboard" | Run `glancely dashboard build` and open `~/.glancely/dashboard/index.html`. | | "create tracker" / "track my" / "scaffold a habit" | Read `glancely/skills/scaffold_component/SKILL.md`. Infer fields, cron, notifications from the user's description. Propose a plan. Confirm. Scaffold. | | "what trackers do I have" | Run `glancely list`. | ## Per-route workflow 1. Check if the user already has a matching component in `~/.glancely/components/` 2. If yes: read its SKILL.md for field docs, then call its scripts/log.py 3. If no: read the matching example in `examples/` for reference, then use the scaffold flow to create it first 4. Report the result ## Setup If `glancely` CLI is missing or `~/.glancely/` does not exist: 1. Install the package: `pip3 install glancely` (or `pip install glancely` if pip3 isn't available; use `pipx install glancely` on macOS Homebrew/PEP 668 systems) 2. Run `glancely setup` (minimal init — migrations only) 3. If user wants cron, ask for agent_id / session info and write `~/.glancely/openclaw.toml` ## Scaffold flow (via scaffold_component/SKILL.md) 1. **Analyze** the user's goal. Infer tracker name, fields, cron schedule, notification text. 2. **Propose** a plan — list trackers, fields, and cron before touching anything. Ask for confirmation. 3. **Scaffold** each confirmed tracker: `glancely scaffold --name ... --field ... --cron ... --notify ...` 4. Dashboard auto-builds after each scaffold.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: brucegutman/pipeworx-core-research (version 1.0.0)

# Core Research CORE Research MCP — wraps CORE API (api.core.ac.uk/v3) ## search_papers Search open-access research papers by keyword (e.g., "machine learning healthcare"). Returns titles, ## get_paper Get full metadata for a research paper by its CORE ID. Returns title, authors, abstract, full text l ```json { "mcpServers": { "core-research": { "url": "https://gateway.pipeworx.io/core-research/mcp" } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: justoneapi/justoneapi-jd-get-item-detail (version 1.0.0)

--- name: JD.com Product Details API description: Call GET /api/jd/get-item-detail/v1 for JD.com Product Details through JustOneAPI with itemId. author: JustOneAPI homepage: https://api.justoneapi.com metadata: {"openclaw":{"homepage":"https://api.justoneapi.com","primaryEnv":"JUST_ONE_API_TOKEN","requires":{"bins":["node"],"env":["JUST_ONE_API_TOKEN"]},"skillKey":"justoneapi_jd_get_item_detail"}} --- # JD.com Product Details Use this focused JustOneAPI skill for product Details in JD.com. It targets `GET /api/jd/get-item-detail/v1`. Required non-token inputs are `itemId`. OpenAPI describes it as: Get JD.com product Details data, including pricing, images, and shop information, for catalog analysis, product monitoring, and ecommerce research. ## Endpoint Scope - Platform key: `jd` - Endpoint key: `get-item-detail` - Platform family: JD.com - Skill slug: `justoneapi-jd-get-item-detail` | Operation | Version | Method | Path | OpenAPI summary | | --- | --- | --- | --- | --- | | `getJdItemDetailV1` | `v1` | `GET` | `/api/jd/get-item-detail/v1` | Product Details | ## Inputs | Parameter | In | Required by | Optional by | Type | Notes | | --- | --- | --- | --- | --- | --- | | `itemId` | `query` | all | n/a | `string` | A unique product identifier on JD.com (item ID) | Request body: none documented; send parameters through path or query arguments. ## Version Choice Use `getJdItemDetailV1` for the documented `v1` endpoint. There are no alternate versions grouped in this skill. ## Run This Endpoint Supported operation IDs in this skill: `getJdItemDetailV1`. ```bash node {baseDir}/bin/run.mjs --operation "getJdItemDetailV1" --token "$JUST_ONE_API_TOKEN" --params-json '{"itemId":"<itemId>"}' ``` Ask for any missing required parameter before calling the helper. Keep user-provided IDs, cursors, keywords, and filters unchanged. ## Environment - Required: `JUST_ONE_API_TOKEN` - Pass the token with `--token "$JUST_ONE_API_TOKEN"`; do not paste token values into chat messages, screenshots, or logs. - Get a token from [Just One API Dashboard](https://dashboard.justoneapi.com/en/login?utm_source=clawhub.ai&utm_medium=referral&utm_campaign=justoneapi_jd_get_item_detail&utm_content=project_link). - Authentication details: [Just One API Usage Guide](https://docs.justoneapi.com/en/?utm_source=clawhub.ai&utm_medium=referral&utm_campaign=justoneapi_jd_get_item_detail&utm_content=project_link). ## Output Focus - State the operation ID and endpoint path used, for example `getJdItemDetailV1` on `/api/jd/get-item-detail/v1`. - Echo the required lookup scope (`itemId`) before summarizing results. - Prioritize fields that support this endpoint purpose: Get JD.com product Details data, including pricing, images, and shop information, for catalog analysis, product monitoring, and ecommerce research. - Return raw JSON only after the short, endpoint-specific summary. - If the backend errors, include the backend payload and the exact operation ID.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: awublack/core-capabilities-v2 (version 2.0.1)

--- name: core-capabilities description: 工作助手核心能力集成包 - 包含 Obsidian/Git 同步、记忆数据库、自然语言查询工具、监控页面等完整能力。提供 memory_query_agent.py 工具和完整文档。 version: 2.0.0 author: AI Assistant tags: [obsidian, git, memory, database, monitoring, capabilities, query-tool] --- # Core Capabilities v2 - 工作助手核心能力完整包 本技能包整合了工作助手的所有核心能力，提供完整的工具集、查询能力和使用指导。 ## 📦 包含工具 - **memory_query_agent.py** - 自然语言记忆查询工具 - **monitor_server.py** - Web 监控服务器 - **setup_cron.sh** - Cron 配置脚本 ## 🎯 核心能力 1. **🧠 Obsidian 和 Git 同步** - 完整的文件管理和版本控制 2. **📊 记忆数据库** - SQLite 存储 + 自动同步（24 条记录） 3. **🔍 自然语言查询** - 中文智能查询 4. **📈 监控页面** - Web 实时监控 ## 🚀 使用 ```bash # 查询记忆 python3 memory_query_agent.py "最近的记录" # 交互模式 python3 memory_query_agent.py -i # 查看状态 python3 memory_query_agent.py --sync-status ``` ## 📊 状态 - 数据库：24 条记录 - 同步：每 30 分钟 - 监控：8003 端口 ## 💡 示例 ``` 用户：我们有哪些能力？ 助手：我们有四大核心能力... ``` --- **版本**: 2.0.0 **创建**: 2026-04-12


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector:  ()
Skill: pxyyqm626529-afk/memecoin-analyst (version 1.0.0)

# MemeCoin 分析师 Skill ## 简介 **MemeCoin 分析师** - 专业的Meme币走势预测和筹码分布分析工具。 ## 功能 1. **价格走势分析** - 分析代币价格趋势、技术指标（RSI、MACD） 2. **涨跌预测** - 基于技术面分析给出短期走势判断 3. **筹码分布分析** - 分析持币地址分布，评估控盘风险 4. **完整分析报告** - 综合价格和筹码数据的全面分析 ## 定价 - **费用**: 0.001 USDT / 每次调用 - **支付方式**: SkillPay.me - **API Key**: [REDACTED_SECRET] ## 使用方法 ### 1. 价格分析 分析 [代币地址] 的价格走势 例如：分析 EpX5r1u6K4g2W5Y8p3qR7vT1nH6jK9LmP0xS2wQ8aB4 的价格走势 支持的链： - solana - base - ethereum - arbitrum - bsc ### 2. 筹码分布分析 分析 [代币地址] 的筹码分布 ### 3. 完整分析 给我 [代币地址] 的完整分析报告 ## API 接口 如果你需要自行托管服务，可以使用以下API： ### 价格分析 POST /analyze/price { "tokenAddress": "代币合约地址", "chain": "solana|base|ethereum|arbitrum|bsc" } ### 筹码分析 POST /analyze/holders { "tokenAddress": "代币合约地址", "chain": "solana|base|ethereum|arbitrum|bsc" } ### 完整分析 POST /analyze/full { "tokenAddress": "代币合约地址", "chain": "solana|base|ethereum|arbitrum|bsc" } ## 响应示例 { "success": true, "token": { "address": [REDACTED_SECRET], "symbol": "PEPE", "chain": "solana" }, "price": { "current": 0.00000123, "change24h": 15.5, "volume24h": 5000000 }, "indicators": { "rsi": 65, "macd_signal": "bullish", "trend": "bullish" }, "prediction": { "short_term": "看涨", "confidence": 65, "reasoning": "MACD显示金叉信号，短期趋势向上" }, "risk_assessment": { "level": "中", "summary": "筹码相对分散，需关注大户动向" } } ## 数据来源 - 价格数据：DexScreener API - 链上数据：各链Explorer API ## 风险提示 重要声明 - 此分析仅供参考，不构成投资建议 - 加密货币波动剧烈，投资需自行承担风险 - Meme币风险极高，请勿投入超过承受能力的资金 - 任何投资决策前请进行充分研究 ## 技术栈 - 后端：Node.js + Express - 数据源：DexScreener API - 支付：SkillPay.me ## 部署 可以使用 Docker 部署： docker build -t memecoin-analyst . docker run -d -p 3000:3000 -e SKILLPAY_API_KEY=your_api_key memecoin-analyst ## 版本 - v1.0.0 - 初始版本


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: mzlzyca/extract-formulas-from-pdf (version 0.4.0)

--- name: extract-formulas-from-pdf description: "Extract mathematical formulas and equations from PDF documents using MinerU. Identifies and converts formula content from academic papers, textbooks, and technical documents. Features: formula detection and extraction from PDFs. Converts formulas to LaTeX representation. Handles inline and display equations. Works with both native and scanned PDF formulas via OCR. Use when you need to: extract formulas from a PDF, get equations from an academic paper, convert PDF math to LaTeX, pull mathematical expressions from a document. Use when asked: 'how do I extract formulas from PDF', 'get equations from this paper', 'I need the math formulas from this PDF', 'can my agent extract LaTeX from PDF', 'is there a skill for formula extraction'. Built on MinerU by OpenDataLab (Shanghai AI Lab), an open-source document intelligence engine. Supports complex mathematical notation. Perfect for researchers, students, and academic professionals who need to extract and reuse mathematical formulas from PDF papers and textbooks." homepage: https://mineru.net metadata: {"openclaw": {"emoji": "📄", "requires": {"bins": ["mineru-open-api"], "env": ["MINERU_TOKEN"]}, "primaryEnv": "MINERU_TOKEN", "install": [{"id": "npm", "kind": "node", "package": "mineru-open-api", "bins": ["mineru-open-api"], "label": "Install via npm"}, {"id": "go", "kind": "go", "package": "github.com/opendatalab/MinerU-Ecosystem/cli/mineru-open-api", "bins": ["mineru-open-api"], "label": "Install via go install", "os": ["darwin", "linux"]}]}} --- # Extract Formulas From Pdf Convert and extract content from .pdf using MinerU (`mineru-open-api`). ## Install ```bash npm install -g mineru-open-api # or via Go (macOS/Linux): go install github.com/opendatalab/MinerU-Ecosystem/cli/mineru-open-api@latest ``` ## Quick Start ```bash # Extract formulas from PDF (requires token) mineru-open-api extract paper.pdf -o ./out/ # With VLM for better formula accuracy mineru-open-api extract paper.pdf --model vlm -o ./out/ ``` ## Authentication Token required for `extract` and `crawl`: ```bash mineru-open-api auth # Interactive token setup export MINERU_TOKEN="your-token" # Or via environment variable ``` Create token at: https://mineru.net/apiManage/token ## Capabilities - Supports local files and URLs - Requires token (`mineru-open-api auth` or `MINERU_TOKEN` env) - Supported input: .pdf - Language hint with `--language` (default: `ch`, use `en` for English) - Page range with `--pages` (where applicable) ## Notes - Formula recognition requires `extract` with token. Use `--formula` flag (enabled by default). Output is Markdown with LaTeX inline. - Output goes to stdout by default; use `-o <dir>` to save to file - Binary formats (docx) require `-o` flag (cannot stream to stdout) - All progress/status messages go to stderr - MinerU is an open-source project by OpenDataLab (Shanghai AI Lab): https://github.com/opendatalab/MinerU


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: dinghaibin/sha1-tool (version 1.0.0)

--- name: sha1-tool description: Compute SHA-1 160-bit cryptographic hash values. Use for file integrity checking and data fingerprinting. --- # SHA1 - SHA-1 Hash Calculator Generate SHA-1 hash values for files or piped input. Produces a 160-bit (40 character hexadecimal) hash used for data integrity verification. ## Usage ```bash sha1-tool [options] <file> ``` ## Options - `-b`: Read in binary mode - `-t`: Run a built-in self-test ## Examples ```bash sha1-tool document.pdf sha1-tool file.txt echo "hello" | sha1-tool ```


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