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Found 1,543 Skills
Scans other open issues to find ones a given PR may also fix or accidentally break. Outputs adjacent-fix opportunities and contradiction risks with file:line evidence. Use when reviewing a PR to discover bundling opportunities or downstream impact across the issue queue.
Use when the user has an SRT (or transcript text) in one language and wants it translated to another, with punctuation-bounded re-segmentation so cues end at real sentence breaks. Simplified Chinese (zh-CN) and English (en) are first-class targets; other targets follow the same rules. Outputs a target-language SRT or bilingual SRT — no audio, no burn-in. Triggers — "翻译字幕", "翻成中文", "translate this SRT", "中英双语字幕", "把这个 SRT 翻译成 X", "bilingual subtitles".
Multi-language Workers development with Rust, Python, and WebAssembly. Use when building Workers in languages other than JavaScript/TypeScript, or when integrating WASM modules for performance-critical code.
Meta-skill that forges, audits, and refines other skills. Three modes – forge a new skill from a brief, audit recent chat transcripts for new-skill candidates and pain points, or refine an existing skill with additive-only changes. Triggers on "skill-forge a thing that does X", "forge a skill", "skill-forge audit", "skill audit", "refine my skills", "skill-forge refine <name>", or "/skill-forge". Auto-opens a PR against mphinance/alpha-skills (never auto-merges).
Official NVIDIA-authored guidance for navigating PhysicsNeMo — pick the model, datapipe, or example for a SciML/AI4Science task (surrogates, forecasting, downscaling, physics-informed, inverse, generative). Points at existing files via live repo search; never writes code. Do NOT use for installation or environment setup, training-loop or other code authoring/scaffolding, contributor/CI/packaging questions, repo-specific questions in physicsnemo-sym/-cfd/-curator, or general (non-physics) ML/PyTorch.
Luban - Skill Polishing Workshop. Transform a "usable Skill" into a public Skill asset that is "understandable, installable, shareable, verifiable, and continuously evolvable". The methodology consists of five craftsman-like steps: 1. Material Inspection: First challenge whether the premise of this Skill is valid; directly state if the "material" is not worth polishing. 2. Peer Research: Search for similar Skills online to clarify its position in the ecosystem. 3. Dimension Measurement: Evaluate using three metrics - structure, actual testing, and live verification (live verification means reconciling with real running outputs; a green CI can be deceptive). 4. Iterative Refinement: Freeze the original version as a baseline; only retain changes that pass the verification gate, otherwise revert. Try to institutionalize verification methods as tools and rules in the repository. 5. Post-Release Iteration: Release is not the end; maintain a benchmark observation list, and start the next iteration based on real feedback. This tool is used when users want to upgrade, optimize, polish, productize, or release their self-developed Skills. The final deliverables include a structured Skill Polishing Report, directly replaceable rewritten segments, and a shareable "Graduation Certificate" result card that can be screenshot. Trigger phrases include but are not limited to: "Let Luban take a look at this skill", "Polish at Luban's Workshop", "Polish my skill", "Upgrade my skill", "Optimize this skill", "Skill check-up", "Skill audit", "Productize my skill", "How to release this skill", "Benchmark against similar skills", "Why no one installs my skill", "Help me publish my skill to GitHub/ClawHub", "Improve SKILL.md". Even if users only provide a Skill directory, GitHub repository link, or a segment of SKILL.md saying "Help me figure out how to modify it", it should be triggered as long as the context is about making the Skill more usable and shareable. Do NOT use this for creating a new Skill from scratch (use skill-creator), regular code review (use code-review), or rewriting ordinary prompts unrelated to Skill assets.
Discover the user's local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. Use this skill before any other AWS work — deploying to SageMaker, creating resources, calling AWS APIs, or anything that touches an AWS account. Use it especially when the user has not specified a region or profile explicitly, when they say things like "use my AWS account", "deploy to AWS", "use my profile", or when about to make any AWS CLI or SDK call. Never guess the region or account ID — always use this skill to read it from the local configuration first.
Migrate Exa, Tavily, Perplexity, or Firecrawl web-data integrations completely to the appropriate Parallel products while preserving application behavior. Use when replacing these providers' SDKs or REST calls, dependencies, environment variables, request parameters, response parsing, model tools, search-plus-scrape paths, full-content or answer-synthesis paths, tests, and documentation; separating unsupported research-index, crawl, browser, file-parse, monitor, or other non-search capabilities; auditing for leftover provider usage; or finishing and verifying an in-progress provider migration.
Retrieval-augmented generation (RAG) skill for the D&D 5e System Reference Document (SRD). Use when answering questions about D&D 5e core rules, spells, combat, equipment, conditions, monsters, and other SRD content. This skill provides agentic search-based access to the SRD split into page-range markdown files.
World-class alternative data and sentiment analysis for trading - social media, news, on-chain data, positioning. Extract alpha from information others miss. Use when "sentiment, alternative data, social media trading, news trading, twitter signals, on-chain, whale watching, fear greed, positioning, " mentioned.
Debug and auto-fix Vite projects running inside WebContainers: resolve mount/root issues, alias/path errors, missing scripts, and other common dev-time problems so the app boots cleanly.
Use this skill to track institutional investor ownership changes and portfolio flows using 13F filings data. Analyzes hedge funds, mutual funds, and other institutional holders to identify stocks with significant smart money accumulation or distribution. Helps discover stocks before major moves by following where sophisticated investors are deploying capital.