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AI-AGENT-SKILLS
AI-AGENT-SKILLS contient 37 skills collectées depuis vignesh2027, avec une couverture métier par dépôt et des pages de détail sur le site.
Skills dans ce dépôt
Build UIs that work for all users including keyboard navigation, screen readers, and WCAG 2.2
Design multi-agent systems with robust tool interfaces, state management, and failure handling
Build ML systems with disciplined training, evaluation, deployment, and safety practices
Design APIs that are stable, ergonomic, and evolvable
Design systems at the right scale with explicit trade-off documentation
Design services that are reliable, observable, secure, and maintainable
Design CI/CD pipelines with fast feedback, quality gates, and reliable deployments
Conduct staff-engineer-level code reviews across correctness, security, performance, readability, and architecture
Remove accidental complexity without changing behavior — apply Chesterton's Fence
Manage what goes into the AI agent context window for maximum quality and minimum waste
Build data pipelines with contracts, lineage, quality gates, and observability
Schema design, migrations, indexing, and query patterns for maintainable and performant databases
Systematic root-cause analysis — stop guessing, start isolating
Deploy with rollback plans, staged rollouts, and feature flags
Write documentation that stays accurate and decisions that stay recorded
Challenge assumptions and hidden dependencies before they become bugs
Build UIs that are fast, accessible, maintainable, and tested
Branching strategy, commit discipline, PR hygiene, and semantic versioning
Sharpen a vague idea into a buildable, scoped proposal
Structured incident response: detect, communicate, mitigate, resolve, and prevent recurrence
Ship working vertical slices instead of big-bang implementations
Manage infrastructure with version-controlled, reviewed, and tested IaC
Systematically evaluate LLM outputs, prevent regressions, and measure quality
Design service boundaries, contracts, and failure modes for distributed systems
Build iOS/Android apps that are performant, offline-capable, accessible, and battery-efficient
Design logging, metrics, tracing, and alerting that make systems debuggable in production
Profile before optimizing; optimize with evidence, not intuition
Decompose work into atomic, independently verifiable tasks
Write prompts that are reliable, testable, versioned, and safe
Design, evaluate, and operate retrieval-augmented generation systems
Extract, validate, and document requirements from raw input
Identify and mitigate technical and delivery risk before it becomes an incident
Apply security controls, threat modeling, and hardening to code and infrastructure
Ground implementation in authoritative sources — specs, RFC, official docs — not assumptions
Write a specification before writing any implementation code
Write tests before implementation to drive design and prove correctness
Discover and compose skills from this framework to guide your work