AI Skills

Plug-and-play skills and prompts for every AI coding agent.

Claude Code · Claude Desktop · Cursor · OpenAI Codex · Gemini CLI · GitHub Copilot · Windsurf · Antigravity · Cline · Zed · Aider · any agent that reads a file

Website · Prompt Studio · Install · Skills · Prompts · Ecosystem · Contributing

[!TIP] Autonomous by design. Point any agent at this repo and it selects the right skill or prompt for each task on its own: it reads AGENTS.md (and CLAUDE.md in Claude Code), then picks from index.json by each entry's use_when trigger. You install once; you never have to name a skill.

What this is

One place that finds every skill. Two halves make that true:

  • The library: original skills and prompts, written for this repository, every one complete and tested. Below.
  • The ecosystem index: every major skill collection on GitHub, indexed with per-skill links for the official ones. 8,000+ skills reachable through ecosystem.json, one fetch away.

The idea behind the library: a tested method beats improvisation. Hand an agent the way a strong engineer reviews code, and its review improves in one step. Hand a person a prompt built on what actually works, and the answer improves on the first try.

  • skills/ are working methods an agent loads and follows: reviewing code, debugging, writing a postmortem, designing an API. One folder per skill, one SKILL.md inside, in the format Claude agents load natively and every other tool can read.
  • prompts/ are complete prompts with named {variables}, the model settings to run them, and one honest line on what each does well. Build your own in the browser with the Prompt Studio. Where settings mention temperature: that is the model's freedom to improvise, 0 exact and repeatable, 1 creative. No temperature control in your tool? Skip it; the prompt works at the default. In Claude Code every prompt installs as a slash command: /summarize, /tldr, /explain, /plan, /brainstorm, /critique, /improve, /outline, /steps, /pros-cons, plus /goal, /autoresearch, and /reflect (the generated commands/ folder). Cursor and Antigravity read the same prompts as slash commands too; INSTALL.md has the one-line copy step per tool.

Each entry is plain markdown with a short header. That is the design, not a limitation: a method an agent can read is one you can read, edit, version, and carry to your next tool. No runtime, no framework, no format that expires when a product does.

Quick start

Claude Code adds the whole library from one command, organized as one installable plugin per category:

/plugin marketplace add Amey-Thakur/AI-SKILLS

Any other tool reads plain markdown. The one-liners:

Tool One line
Claude Desktop, claude.ai Upload a skill folder in Settings, Capabilities, Skills
Codex, Gemini CLI, Cursor, Copilot, Windsurf, Antigravity, Zed, Aider Reference a skill from your AGENTS.md
Cline curl -s <raw>/skills/code-quality/code-review/SKILL.md > .clinerules/code-review.md
Any API Fetch the raw file; the body is your system prompt

Full per-tool instructions, including scoped Cursor .mdc rules and Copilot instruction files, are in INSTALL.md.

For agents: the whole catalog is machine-readable at index.json (each entry with a description and a raw URL), mirrored in llms.txt, with usage rules in AGENTS.md. Fetch the index, pick by description, pull only what the task needs.

What's inside

One row per category; every entry, with its one-line description, is in CATALOG.md.

Category Skills For example
accessibility 8 accessibility-review, accessible-forms, alt-text-writing
apis 9 api-change-management, api-client-design, api-deprecation
architecture 13 api-gateway-pattern, architecture-decision-records, architecture-diagrams
backend 14 api-error-responses, api-versioning, background-jobs
big-tech-processes 28 architecture-review-board, bar-raiser-interviewing, canary-analysis
big-tech-roles 30 accessibility-specialist-role, backend-engineer-role, cloud-architect-role
business-growth 8 churn-analysis, community-building, developer-marketing
career-communication 10 async-communication, conference-talks, engineering-resume
cloud 12 autoscaling-policies, cloud-cost-optimization, cloud-disaster-recovery
code-quality 37 api-surface-minimalism, assertion-density, boolean-parameters
css-styling 10 css-animations, css-architecture, css-cascade
data-engineering 12 batch-vs-streaming, change-data-capture, data-lineage
data-science 18 cohort-analysis, correlation-causation, data-cleaning
databases 12 backup-restore, database-migrations, database-normalization
debugging 32 alerting-design, binary-search-debugging, browser-devtools
devops 14 artifact-versioning, blue-green-deployments, capacity-planning
distributed-systems 12 backpressure, clock-skew, consensus-basics
documentation 10 api-reference-docs, changelog-writing, code-documentation
email 8 clear-emails, difficult-emails, email-etiquette
embedded-iot 8 embedded-debugging, embedded-memory-constraints, firmware-ota-updates
frontend 10 design-systems, error-boundaries-ui, form-handling
game-development 8 entity-component-system, game-asset-pipeline, game-input-handling
git-collaboration 10 branch-strategy, code-owners, commit-messages
gpu-ai-infrastructure 20 ai-datacenter-networking, checkpointing-large-training, cuda-kernel-basics
javascript-typescript 14 js-async-patterns, js-error-handling, js-event-loop
jvm-dotnet 10 csharp-linq, dotnet-async, dotnet-dependency-injection
llm-engineering 22 agent-memory, agentic-loops, coding-agent-workflow
machine-learning 12 cross-validation, drift-monitoring, experiment-tracking
mobile 10 app-store-readiness, deep-linking, mobile-input-ux
multi-agent-teams 25 agent-arch-board, agent-code-review-loop, agent-competitive-analysis-team
performance 28 algorithmic-optimization, async-io-patterns, batching-and-debouncing
product-management 10 ab-test-design, customer-interviews, feature-sunsetting
python 14 pytest-mastery, python-asyncio, python-cli-tools
research 15 autonomous-research, decision-journals, deep-research
scripting-automation 10 automation-guardrails, bash-robustness, cli-ux-design
security 44 api-security, audit-logging, authn-design
systems-languages 10 c-memory-safety, cpp-raii, ffi-boundaries
testing 41 api-testing, approval-testing, assertion-libraries
ui-ux 10 empty-and-error-states, information-architecture, interaction-design
writing 10 audience-adaptation, clear-writing, concise-writing
prompts 134 ad-copy, add-code-comments, adjust-tone

Principles

  1. Portable. Plain markdown + minimal YAML. If a tool dies, the content survives.
  2. Honest. Each entry says what it is for and where it does not apply. Nothing here claims to replace judgment.
  3. Complete. An entry ships when it is usable end to end, not before.
  4. Small. One method per skill, one job per prompt. Composition beats bloat.

FAQ

Why not just prompt harder? "Review this well" leaves the agent to guess what good means. A skill hands it the priorities, the verification steps, and the reporting format a strong engineer would use. The output changes on the next run.

Does this work with my tool? If the tool reads a markdown file, yes. The per-tool steps in INSTALL.md are conveniences, not requirements.

How is this different from a curated list? It is both halves. The library is the things themselves: every entry here, complete, in one format. The ecosystem index is the list: every major collection elsewhere, linked and machine-readable. One fetch covers both.

Can I use these commercially? Yes, under MIT. Attribution is appreciated and never required.

Contributing

New skills and prompts are welcome when they clear the bar in CONTRIBUTING.md: complete, self-contained, honest, distinct, portable. By taking part you agree to the Code of Conduct. Found something that could cause harm? Follow the security policy, not a public issue.

License and author

Released under the MIT License. Use these skills and prompts commercially or privately; attribution is appreciated and never required.

Built by Amey Thakur, who also builds NotebookLab, the offline-first AI knowledge workspace these methods grew out of.