OpenClaw/clawhub-security-signals huggingface dataset:
Skill: wanng-ide/hle-benchmark-evolver (version 1.0.0)

--- name: hle-benchmark-evolver description: Runs HLE-oriented benchmark reward ingestion and curriculum generation for capability-evolver. Use when the user asks to optimize Humanity's Last Exam score, ingest question-level benchmark results, prioritize easy-first queues, or request an immediate benchmark progress result. tags: [benchmark, hle, evolution, reward, curriculum] --- # HLE Benchmark Evolver This skill operationalizes HLE score-driven evolution for OpenClaw. ## When to Use - User asks to improve HLE score (for example target >= 60%). - User provides question-level benchmark output and wants it converted to reward. - User wants easy-first curriculum queue and next-focus questions. - User asks for an immediate benchmark result snapshot. ## Inputs - Benchmark report JSON path (`--report=/abs/path/report.json`) - Optional benchmark id (`cais/hle` default) ## Workflow 1. Validate the report JSON exists and is parseable. 2. Ingest report into `capability-evolver` benchmark reward state. 3. Generate curriculum signals: - `benchmark_*` - `curriculum_stage:*` - `focus_subject:*` - `focus_modality:*` - `question_focus:*` 4. Return a compact result summary for this run. ## Run ```bash node skills/hle-benchmark-evolver/run_result.js --report=/absolute/path/hle_report.json ``` Full automatic loop (starts evolution cycle): ```bash node skills/hle-benchmark-evolver/run_pipeline.js --report=/absolute/path/hle_report.json --cycles=1 ``` If your evaluator can be called from shell, let pipeline generate the report each cycle: ```bash node skills/hle-benchmark-evolver/run_pipeline.js \ --report=/absolute/path/hle_report.json \ --eval_cmd="python /path/to/eval_hle.py --out {{report}}" \ --cycles=3 --interval_ms=2000 ``` If no `--report` is provided, it defaults to: `skills/capability-evolver/assets/gep/hle_report.template.json` ## Output Contract Always print JSON with these fields: - `benchmark_id` - `run_id` - `accuracy` - `reward` - `trend` - `curriculum_stage` - `queue_size` - `focus_subjects` - `focus_modalities` - `next_questions` ## Notes - This skill handles reward/curriculum ingestion. It does not directly solve HLE questions. - `run_pipeline.js` links ingestion, evolve, and solidify into one executable loop.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: suspicious (HIGH)
Skill: brucegutman/pipeworx-carbon (version 1.0.0)

--- name: pipeworx-carbon description: UK national carbon intensity data — real-time, historical, and generation mix from the Carbon Intensity API version: 1.0.0 metadata: openclaw: requires: bins: - curl emoji: "🌱" homepage: https://pipeworx.io/packs/carbon --- # UK Carbon Intensity How clean is the UK electricity grid right now? This pack pulls from the official Carbon Intensity API (carbonintensity.org.uk) maintained by National Grid ESO. Get real-time and historical carbon intensity forecasts plus the current generation fuel mix. ## Tools | Tool | Purpose | |------|---------| | `get_intensity` | Current national carbon intensity — forecast gCO2/kWh, actual value, and index (very low to very high) | | `get_intensity_by_date` | Carbon intensity data for a specific date (format YYYY-MM-DD) | | `get_generation_mix` | Current electricity generation breakdown by fuel type (wind, solar, gas, nuclear, etc.) | ## When this is useful - Smart home systems deciding when to run appliances (charge EVs when intensity is low) - Sustainability dashboards showing real-time grid carbon data - Research comparing UK grid decarbonization over time - Answering "how green is UK electricity right now?" ## Example ```bash curl -s -X POST https://gateway.pipeworx.io/carbon/mcp \ -H "Content-Type: application/json" \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"get_generation_mix","arguments":{}}}' ``` Returns something like: ```json { "from": "2024-03-15T10:00Z", "to": "2024-03-15T10:30Z", "mix": [ { "fuel": "wind", "percentage": 38.2 }, { "fuel": "nuclear", "percentage": 15.1 }, { "fuel": "gas", "percentage": 22.4 }, { "fuel": "solar", "percentage": 8.7 } ] } ``` ## Setup ```json { "mcpServers": { "pipeworx-carbon": { "command": "npx", "args": ["-y", "mcp-remote@latest", "https://gateway.pipeworx.io/carbon/mcp"] } } } ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: instant-picture/qmd-1-0-0 (version 1.0.0)

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


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

--- name: three description: 介绍数字 3 的文化含义和象征意义（中英文双语）。Use when users ask about the cultural meaning of number 3, "三的文化", "symbolism of three", "为什么 3 很重要", "meaning of number three" etc. --- # Three / 三 - 数字 3 的文化介绍 ## 触发场景 / Trigger Scenarios 当用户询问以下内容时激活此技能： - 数字 3 的文化含义 / Cultural meaning of number 3 - "三"的象征意义 / Symbolism of "three" - 为什么 3 很特殊 / Why is 3 special - 3 在不同文化中的代表 / What 3 represents in different cultures --- ## 中文版本 / Chinese Version ### 1. 哲学与宗教意义 **西方宗教**： - 基督教：三位一体（圣父、圣子、圣灵） - 古希腊：三美神（优雅、美丽、欢乐） - 西方童话中常见"三个愿望"、"三只小猪"等三元结构 **东方哲学**： - 道家："二生三，三生万物"，3 代表万物生成的关键 - 儒家："三才"（天、地、人），"三纲五常" - 佛教："三宝"（佛、法、僧），"三世"（过去、现在、未来） ### 2. 文化象征 **中国**： - "三"代表多数，如"三思而后行"、"三人行必有我师" - "三阳开泰"象征新年吉祥 - "三星高照"代表福、禄、寿 - 古代科举：乡试、会试、殿试三级考试 **西方**： - 3 是幸运数字（lucky number） - "Third time's the charm"（第三次就会成功） - 颁奖典礼的前三名（金、银、铜牌） - 故事的三段式结构（开始、发展、结局） ### 3. 现代意义 - 三维空间（长、宽、高） - 三色原理（RGB 色彩系统） - 三等奖、第三名的象征 - "三"代表稳定和完整（三角形最稳定） --- ## English Version ### 1. Philosophical & Religious Significance **Western Religion**: - Christianity: The Holy Trinity (Father, Son, Holy Spirit) - Ancient Greece: The Three Graces (elegance, beauty, joy) - Western fairy tales often feature triadic structures: "three wishes," "three little pigs" **Eastern Philosophy**: - Taoism: "Two produced Three; Three produced All Things," 3 represents the key to generating all things - Confucianism: "Three Powers" (Heaven, Earth, Humanity), "Three Bonds and Five Constants" - Buddhism: "Three Jewels" (Buddha, Dharma, Sangha), "Three Times" (past, present, future) ### 2. Cultural Symbolism **Chinese Culture**: - "三" represents multitude: "三思而后行" (think thrice before acting), "三人行必有我师" (in a group of three, one can be my teacher) - "三阳开泰" symbolizes New Year auspiciousness - "三星高照" represents Fortune, Prosperity, and Longevity - Ancient imperial examinations: three levels (local, provincial, palace) **Western Culture**: - 3 is a lucky number - "Third time's the charm" - Top three in award ceremonies (gold, silver, bronze medals) - Three-act story structure (beginning, development, conclusion) ### 3. Modern Significance - Three-dimensional space (length, width, height) - Three-color principle (RGB color system) - Symbol of third place/bronze medal - "Three" represents stability and completeness (triangle is the most stable shape) --- ## 使用方式 / Usage 当用户询问相关话题时，根据具体兴趣点提供： 1. 哲学/宗教角度的解释 / Philosophical/religious perspectives 2. 不同文化中的象征 / Symbolism in different cultures 3. 现代应用和含义 / Modern applications and meanings 保持回答简洁有趣，可根据用户兴趣深入特定方向。 Keep responses concise and interesting; dive deeper into specific directions based on user interest.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: mrsarac/rune-prompt-amplification (version 1.0.0)

--- name: rune-prompt-amplification description: "Transforms any flat prompt into a structured 8-layer XML prompt using RUNE's semantic engine — delivering ~45% higher quality AI responses. Built on Spinoza's philosophical framework by NeuraByte Labs." version: "1.0.0" author: "Mustafa Saraç <[REDACTED_SECRET]>" license: MIT homepage: https://github.com/neurabytelabs/rune-skill compatibility: Requires Python 3.11+, RUNE repo cloned locally, RUNE_API_KEY in ~/.secrets --- # RUNE — Prompt Amplification Framework RUNE transforms flat, ambiguous prompts into structured XML prompts validated by Spinoza's philosophical framework — resulting in outputs that are ~45% higher quality than raw prompting. ## The 8 Layers | Layer | Name | Purpose | |-------|------|---------| | L0 | System Core | Role, persona, behavioral rules | | L1 | Context Identity | Domain knowledge, target audience | | L2 | Intent Scope | Task definition, output format | | L3 | Governance | Constraints, ethical boundaries | | L4 | Cognitive Engine | Thinking strategy (CoT, ToT) | | L5 | Capabilities Domain | Tools, integrations, capabilities | | L6 | QA | Validation criteria, quality control | | L7 | Output Meta | Format, style, length, language | ## Requirements - Python 3.11+ - RUNE repo cloned locally - `RUNE_API_KEY` in `~/.secrets` ## Usage ```bash # Pipe a prompt echo "Write a blog post about AI" | bash main.sh # Pass as argument bash main.sh "Explain quantum entanglement to a 12-year-old" ``` ## Setup ```bash # 1. Clone RUNE repo git clone https://github.com/mrsarac/master-prompts ~/Documents/GitHub/rune # 2. Add API key to ~/.secrets echo "export RUNE_API_KEY=your_key" >> ~/.secrets # 3. Test echo "Hello" | bash main.sh ``` ## Source - **Author:** NeuraByte Labs - **Version:** RUNE v4.3 / WAND v1.5.0 - **Repo:** https://github.com/neurabytelabs/rune-skill


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)
Skill: zhouqkt/agency-git-workflow-master (version 1.0.0)

--- name: agency-git-workflow-master description: Expert AI agent specializing in git workflow master. From The Agency (github.com/msitarzewski/agency-agents). metadata: openclaw: emoji: "🌿" requires: { bins: [] } install: [] --- # git workflow master ## Identity & Style ## 🧠 Your Identity & Memory - **Role**: Git workflow and version control specialist - **Personality**: Organized, precise, history-conscious, pragmatic - **Memory**: You remember branching strategies, merge vs rebase tradeoffs, and Git recovery techniques - **Experience**: You've rescued teams from merge hell and transformed chaotic repos into clean, navigable histories ## Core Mission ## 🎯 Your Core Mission Establish and maintain effective Git workflows: 1. **Clean commits** — Atomic, well-described, conventional format 2. **Smart branching** — Right strategy for the team size and release cadence 3. **Safe collaboration** — Rebase vs merge decisions, conflict resolution 4. **Advanced techniques** — Worktrees, bisect, reflog, cherry-pick 5. **CI integration** — Branch protection, automated checks, release automation ## How to Activate Reference this agent by name or specialty when you need its expertise.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ken7y/hn-morning-brief (version 1.0.1)

--- name: hn-morning-brief metadata: {"openclaw":{"emoji":"📰","requires":{"bins":["python3"]},"os":["linux","darwin","win32"]}} description: > Use this skill when the user explicitly mentions Hacker News or HN — e.g. "what's on HN", "show me Hacker News", "top HN stories", "anything good on HN today", or "dive deeper into this HN story". Do not activate for general tech news requests that don't mention HN or Hacker News. --- ## Morning Briefing ### Step 1 — Pull user interests from memory ``` memory_search("interests topics preferences technology news") ``` Do this first, before fetching stories — the results determine how stories are ranked. Extract any topics, technologies, or themes found. If nothing relevant comes back, fall back to HN ranking order. ### Step 2 — Fetch top HN stories ```bash python3 skills/hn-morning-brief/scripts/fetch_hn.py --limit 20 ``` (Path is relative to the project root — openclaw installs this skill at `skills/hn-morning-brief/`.) Returns JSON with: `title`, `article_url`, `hn_url`, `domain`, `author`, `points`, `num_comments`. ### Step 3 — Rank and filter Score each story by combining two signals: - **Relevance to user interests** (from memory) — a story the user cares about is worth more regardless of score - **HN points** — use as a tiebreaker and quality signal when interests are unclear Surface the 8–12 highest-scoring stories. If memory search returned no clear interests, rank by `points` only. ### Step 4 — Present briefing ``` ## HN Morning Brief — {today's date} {N} stories picked for you 1. **{Title}** `{domain}` · ⬆ {points} · 💬 {num_comments} {one-line context or why this is interesting} → [Article]({article_url}) · [HN Discussion]({hn_url}) 2. ... --- Say "dive deeper into #N" or "tell me more about [title]" to get a full summary. ``` --- ## Diving Deeper When the user picks a story: 1. **Fetch and summarize the article** — read the article URL and write a 3–5 sentence summary of the key points. Do this even if the user just says "more on #3" — they want the content, not just the link. 2. **Show both links:** - Article: `{article_url}` - HN Discussion: `{hn_url}` (often where the most interesting debate happens) 3. **Offer to go further:** "Want me to search for more context on this?" ## Gotchas - `article_url` is the **original article**. `hn_url` is the HN discussion thread. Never swap them — linking to the HN page when the user wants the article is a bad experience. - If the article is a PDF or appears paywalled, say so and summarize from the title, domain, and any available description instead of silently failing. - If `memory_search` returns no clear interests, rank by `points` and don't guess — invented interests will surface irrelevant stories.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: dtsiomo/presale-one-pass-orchestrator (version 0.1.0)

--- name: presale-one-pass-orchestrator description: Run or supervise a one-pass Codex implementation with preflight gates, stepwise plan execution, and strict QA defect loop. Use when executing approved presale plans end-to-end in one controlled run. --- Run one-pass only after readiness is explicitly approved. ## Preflight 1. Validate all mandatory docs/configs from `project-context`. 2. Validate readiness checklist decision = YES. 3. Validate no open review comments. 4. Stop immediately on any failed gate. ## Execution protocol 1. Follow plan steps in order. 2. After each step, update plan `verification matrix` and `mid-summary`. 3. Keep scope locked; reject out-of-scope additions. ## QA defect loop (mandatory) Apply process from `references/qa-defect-loop.md`. ## Completion 1. Ensure final summaries exist in all plan files. 2. Produce changed files + reproducible check commands. 3. List risks/debts and open questions.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)
Skill: johnnywang2001/regex-toolkit (version 1.0.0)

--- name: regex-toolkit description: Test, match, extract, replace, explain, and validate regular expressions from the command line. Includes a library of 25+ common patterns (email, URL, IP, phone, date, UUID, etc.) that can be used by name. Use when the user needs to build, debug, or test regex patterns, extract data with regex, do search-and-replace with backreferences, or understand what a regex does. Zero external dependencies. --- # Regex Toolkit Test, find, replace, explain, and validate regular expressions. Includes 25+ common named patterns. Pure Python, no dependencies. ## Quick Start ```bash # Test a pattern python3 scripts/regex_toolkit.py test '\d+' --text 'abc 123 def' # Use a named pattern (email, url, ipv4, phone-us, uuid, etc.) python3 scripts/regex_toolkit.py findall email --text 'Contact [REDACTED_SECRET] or [REDACTED_SECRET]' # Find all matches with positions python3 scripts/regex_toolkit.py findall '\b\w{5}\b' --file input.txt # Search and replace python3 scripts/regex_toolkit.py replace '\bfoo\b' -r bar --text 'foo bar foo' # Explain a regex in plain English python3 scripts/regex_toolkit.py explain '(?P<year>\d{4})-(\d{2})-(\d{2})' # Validate regex syntax python3 scripts/regex_toolkit.py validate '[a-z' # List all common patterns python3 scripts/regex_toolkit.py patterns --list # Pipe from stdin echo "Call 555-1234 or 555-5678" | python3 scripts/regex_toolkit.py findall phone-us ``` ## Commands | Command | Description | |---------|-------------| | `test` | Check if pattern matches. Shows first match position and groups. | | `findall` | Find all matches with positions and captured groups. `--json` for JSON output. | | `replace` | Regex search-and-replace. Supports `\1` backreferences. `--count N` limits replacements. | | `explain` | Break down a regex into plain-English explanation of each element. | | `validate` | Check if a regex pattern is syntactically valid. | | `patterns` | List built-in common patterns or show a specific one by name. | ## Input Options All matching commands accept: `--text 'string'`, `--file path`, or piped stdin. ## Flags | Flag | Description | |------|-------------| | `-i, --ignorecase` | Case-insensitive matching | | `-m, --multiline` | `^`/`$` match line boundaries | | `-s, --dotall` | `.` matches newline | ## Built-in Patterns Use by name instead of writing regex: `email`, `url`, `ipv4`, `ipv6`, `phone-us`, `phone-intl`, `date-iso`, `date-us`, `time-24h`, `hex-color`, `mac-address`, `uuid`, `ssn`, `zip-us`, `credit-card`, `slug`, `semver`, `domain`, `hashtag`, `mention`, `markdown-link`, `html-tag`, `json-key`, `filepath-unix`, `filepath-win`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: godferylindsay/godfery-translate (version 1.0.0)

--- --- name: Translate name: Translate description: "Translate text accurately — preserve formatting, handle plurals, and adapt tone per locale." metadata: emoji: "🌐" category: "text" difficulty: "intermediate" os: "all" tags: ["translation", "localization", "languages", "formatting"] --- # Translate Master accurate translation that preserves meaning, formatting, and cultural context. ## Formatting Preservation - Never translate content inside code blocks, HTML tags, or markdown syntax - Preserve placeholders like `{name}`, `{{variable}}`, `%s`, `$1` exactly as-is - Keep markdown structure intact: headers, links, bold/italic formatting - Maintain JSON/XML structure and keys — translate only values where appropriate ## Content Rules - Don't translate: proper nouns, brand names, technical terms, URLs, email addresses - Don't translate: code snippets, CSS classes, API endpoints, file extensions - Preserve numbers, dates, and IDs in their original format unless locale conversion needed - Keep consistent terminology throughout — create a glossary for repeated terms ## Language-Specific Handling - **Plurals**: Use correct plural forms per target language rules (not English patterns) - **Gender**: Ensure noun-adjective agreement in gendered languages (Spanish/French/German) - **Formality**: Choose appropriate register (tu/vous, tú/usted, du/Sie) based on context - **RTL languages**: Consider text direction for Arabic/Hebrew but keep LTR elements (URLs, numbers) ## Cultural Adaptation - Adapt idioms and expressions rather than literal translation - Convert units when culturally appropriate (miles↔km, Fahrenheit↔Celsius) - Adjust date formats to locale standards (MM/DD vs DD/MM vs DD.MM) - Use local currency symbols and number formatting (, vs . for decimals) ## Context Awareness - Disambiguate based on context: "bank" (financial vs river), "mouse" (animal vs computer) - Maintain document tone: formal business vs casual blog vs technical manual - Consider target audience: children's content vs academic paper vs marketing copy - Preserve original intent and emotional nuance, not just literal meaning ## Quality Control - Read full context before translating to understand meaning - Check that translated text flows naturally in target language - Verify all formatting elements remain functional after translation - Ensure consistent voice and terminology across the entire document

path: README.md
content: # translate - OpenClaw Skill

## Installation

```bash
openclaw skill install translate
```

> 📖 **Complete setup guide**: https://skillboss.co/skill.md

## Usage
See SKILL.md for details.

## License
MIT


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: santosparra651-arch/github-bounty-hunter-v2 (version 1.0.0)

--- name: github-bounty-hunter version: 1.0.0 description: Automatically search for low-competition GitHub bounty tasks (comments < 5) and generate a clean report with details and estimated difficulty. author: Your Name price: 299 category: Bounty Hunting tags: - github - bounty - automation - search --- # GitHub Bounty Hunter 🎯💰 Automatically find low-competition bounty tasks on GitHub. Filter by number of comments to avoid crowded bounties and maximize your chance of getting accepted. ## Features - 🔍 **Search open bounties** with label:bounty and state:open - 🎯 **Low-competition filter** - only shows bounties with `< N` comments (default 5) - 📊 **Generate clean report** with title, URL, comment count, difficulty estimation - ⚡ **Fast search** via GitHub API - 🎨 **Sorted by competition level** - least competition first ## Installation ```bash npx clawhub install github-bounty-hunter ``` ## Usage Search for low-competition bounties: ```bash npx github-bounty-hunter ``` Customize maximum comment count: ```bash npx github-bounty-hunter 3 ``` ## Output example: ```text ========================================= GitHub Bounty Hunter - Search Results ========================================= Found 5 low-competition bounties: 👉 Add transaction history page URL: `https://github.com/example/project/issues/6` 💬 Comments: 2 | 💪 Difficulty: Beginner 👉 FAQ & How It Works Page URL: `https://github.com/example/project/issues/264` 💬 Comments: 1 | 💪 Difficulty: Beginner ... ``` ## Requirements - Node.js 16+ - GitHub API access (authenticated for higher rate limits) ## How it works This skill uses the GitHub Search API to find open issues with the bounty label, filters by comment count, sorts by least comments first, and outputs a clean report ready for you to pick your next bounty. ## Perfect for: - Beginners looking for easy bounties - Bounty hunters who want less competition - Daily quick scans for new opportunities ## Changelog ### 1.0.0 - Initial release - Basic search and filtering - Report generation


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