Claude Skills
The open format is called Agent Skills and works in Claude Code, Codex, Cursor and other agents — most people know it as Claude Skills.
Every Agent Skill we could find on GitHub, deduplicated by content. 86 543 files from 2 102 authors, of which 68 261 are unique — the rest is the same skill repackaged into someone else's repository. For each one: what it weighs in tokens, whether it ships runnable scripts, and which MCP servers it needs.
prisma db pull. Reference when using this Prisma feature.
prisma db execute. Reference when using this Prisma feature.
prisma debug. Reference when using this Prisma feature.
prisma dev. Reference when using this Prisma feature.
prisma generate. Reference when using this Prisma feature.
prisma migrate deploy
prisma init. Reference when using this Prisma feature.
prisma migrate dev. Reference when using this Prisma feature.
prisma migrate reset
prisma migrate resolve
prisma migrate status
prisma migrate diff. Reference when using this Prisma feature.
prisma studio. Reference when using this Prisma feature.
prisma validate. Reference when using this Prisma feature.
Client Methods. Reference when using this Prisma feature.
PrismaClient Constructor
Model Queries. Reference when using this Prisma feature.
Raw Queries. Reference when using this Prisma feature.
Query Options. Reference when using this Prisma feature.
Filter Conditions and Operators
Relation Queries. Reference when using this Prisma feature.
Transactions. Reference when using this Prisma feature.
MongoDB Setup. Reference when using this Prisma feature.
MySQL Setup. Reference when using this Prisma feature.
PostgreSQL Setup. Reference when using this Prisma feature.
CockroachDB Setup. Reference when using this Prisma feature.
Prisma Client Setup. Reference when using this Prisma feature.
Prisma Postgres Setup
SQLite Setup. Reference when using this Prisma feature.
SQL Server Setup. Reference when using this Prisma feature.
Prisma Accelerate Users
Driver Adapters. Reference when using this Prisma feature.
Environment Variables
ESM Support. Reference when using this Prisma feature.
Prisma Config. Reference when using this Prisma feature.
Removed Features. Reference when using this Prisma feature.
Schema Changes. Reference when using this Prisma feature.
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
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| Extract clean markdown from any URL, including JavaScript-rendered SPAs. Use this skill whenever the user provides a URL and wants its content, says "scrape", "grab", "fetch", "pull", "get the page", "extract from this URL", or "read this webpage". Handles JS-rendered pages, multiple concurrent URLs, and returns LLM-optimized markdown. Use this instead of WebFetch for any webpage content extraction.
| Web search with full page content extraction. Use this skill whenever the user asks to search the web, find articles, research a topic, look something up, find recent news, discover sources, or says "search for", "find me", "look up", "what are people saying about", or "find articles about". Returns real search results with optional full-page markdown — not just snippets. Provides capabilities beyond Claude's built-in WebSearch.
Delegate complex tasks to Amplifier's multi-agent framework. Use when: (1) research/comparison needing multiple perspectives, (2) multi-file code projects, (3) architecture/design reviews, (4) user asks for deep/thorough work. NOT for: simple Q&A, quick edits, casual chat, anything needing <5s response. CLI: amplifier-openclaw.
> Scans the Teams platform based app code base. Evaluates the code based on the rules described in the skill. Generates a report on the NGC readiness of the app based on the rules. Trigger phrases include "Generate a report for NGC readiness", "NGC readiness report", "Scan for issues related to NGC" etc.
Use when setting up, operating, migrating, repairing, or explaining an OpenClaw deployment on Hugging Face with the ML Claw `mlclaw` CLI. Covers the browser Space gateway, Hugging Face OAuth, local gateway mode, private Storage Buckets, Telegram as an optional connector, Docker context pinning, model selection through the Hugging Face Router, OpenAI API key setup, costs, diagnostics, and state safety.
Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.
Use ML Claw's HF Broker for protected Hugging Face repository and bucket reads, writes, temporary grants, recovery, and revocation. Use instead of asking for or exposing a Hugging Face access token.
> Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. "which model runs on my laptop/machine/device", "recommend a model for", "what LLM should I use for", "compare models for", "what's state of the art for", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks.
Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on Hugging Face, port code onto ZeroGPU, fix a Space that won't build or run, or otherwise work with `hf spaces …`, `@spaces.GPU`, Space README frontmatter, or the `spaces` Python package.
> Multi-agent QA review team for code changes. This skill should be used when the user asks to "review my code", "run QA", "qa-team", "review this branch", "code review", "check my changes", or wants a comprehensive multi-perspective code review of the current branch's changes. Spawns parallel specialist agents (security, database, reliability, compatibility, data integrity, performance, frontend, copy) that independently review the diff and produce a converged report. Also includes two generalist reviewers for convergence validation.
>- The Elephant resolution skill. Ingests flat deficiency reports from concurrent Goldfish reviewers, applies automated fixes for PROPOSE FIX items via UI multiple-choice gates, and presents interactive negotiation/re-sync checkpoints.
>- Interactive ideation and brainstorming skill for the Elephant & Goldfish Model (EGM). Helps turn half-baked concepts into structured hypotheses via clarifying questions, review, and iterative refinement. Produces an RFC summary for human review or more detailed design with /egm-draft.
>- Executes the Antagonistic-Review process from the Elephant-Goldfish Model (EGM) via an automated multi-agent review loop to audit documents against qualitative standards and structural templates, and automatically apply approved fixes. Use when reviewing design docs, READMEs, feature specifications, implementation plans, or verifying document completeness and tone.
>- Drafts a design document section-by-section using the concepts brief from egm-brainstorm (or user notes). This is the second step in the Elephant-Goldfish (EGM) feature flow. Use when you are ready to write a design doc after aligning on requirements + solution.
>- Orchestrates the EGM Goldfish Review Protocol. Used as part of the EGM Initial Zoom Out Review, Comprehension Review, Design Critique, Intermediate Zoom Out Review, and Implementation Readiness.
>- Runs drift detection on all design documents to check for document and code consistency. Produces an actionable report with differences. Use when the user asks to detect drift or check for consistency across all design documents or READMEs in a directory.
Bootstrap directory README.md files across a repository subtree.
| Manager-side team rollup for Copilot Cowork ROI. Aggregates the de-identified stats teammates post (via the Cowork Team Report Member skill) to a shared Teams channel into ONE anonymized HTML dashboard (five tabs, with the how-to-read guide built in), then emails the channel members a summary with the dashboard attached. First run asks for the Teams channel link and remembers it; each run reads the latest 15 days and keeps the latest post per person. Numbers only — no names or files; a Role breaks out only when 3+ share it. Small homogeneous teams; not org-wide. Use when the user asks to "build the team Cowork Team Report", "aggregate my team's Cowork stats", "roll up the channel posts", "manager Cowork Team Report", "email the team dashboard", or set up / refresh the rollup.
| Member step of the team Cowork Team Report rollup. Harvests the signed-in user's own Copilot Cowork session history from OneDrive, lets the user exclude any chat/task, computes impact metrics, and posts a de-identified, TABLE-FORMATTED stats message to your team's dedicated "Cowork report" Teams channel (the channel link is requested on first run and remembered). Tables cover KPIs, time-by-category, value pillars, jobs-to-be-done, business processes, roles, skills, and deliverable types. Person names, file names and prompts are excluded; process/JTBD and customer/account names are kept. Bundles its own pipeline. Runs once or on a biweekly schedule (every other Monday; scheduled runs email the user to review/exclude sessions before posting). Use when the user asks to "post my Cowork Team Report stats", "send my Cowork stats to the team channel", "run the Cowork Team Report member step", or "share my Cowork impact with the team".
| Manager-side team rollup for Copilot Cowork ROI. Aggregates the de-identified stats teammates post (via the Cowork Dashboard Member skill) to a shared Teams channel into ONE anonymized HTML dashboard (five tabs), then emails the channel members a summary with the dashboard and a one-page PDF guide attached. First run asks for the Teams channel link and remembers it; each run reads the latest 15 days and keeps the latest post per person. Numbers only — no names or files; a Role breaks out only when 3+ share it. Small homogeneous teams; not org-wide. Use when the user asks to "build the team Cowork Dashboard", "aggregate my team's Cowork stats", "roll up the channel posts", "manager Cowork Dashboard report", "email the team dashboard", or set up / refresh the rollup.
> Use this skill whenever the user wants to add, create, or write a new eval (evaluation) in the auth0-evals repository. This includes requests like "add an eval for X", "create a new eval", "write a grader for Y", "add a test case for the Z SDK", or "extend the eval suite". Use it whenever the user mentions eval IDs, graders, PROMPT.md, graders.ts, or wants to measure how well an LLM handles an Auth0 SDK integration task. Always use this skill before writing any eval files — it contains critical conventions that are easy to get wrong.
Claude Skills — questions
Answers built from the skills we actually parsed.