ai-agent-skills

Validates, parses, and sanitizes AI-generated outputs before they reach end users or downstream systems. Structured output enforcement, schema validation, and fallback handling.

Design stable, versioned, self-documenting APIs. Easy to use correctly, hard to use incorrectly. Apply Hyrum's Law from day one.

Automated quality gates from commit to production. Every merge to main is potentially shippable. No manual steps in the deployment path.

Get layered, context-aware explanations of unfamiliar code. Understand what it does, why it was written that way, and how to work with it safely.

Structured code review focusing on correctness, security, and maintainability. Correctness before style. Every reviewer comment must be actionable.

Load minimum necessary context into agent context windows. Prevents token bloat, reduces cost, and improves focus. Only load what the current task needs.

Systematic root cause analysis for production and development bugs. Hypothesis-driven debugging — never guess-and-check.

Document decisions, not just implementations. ADRs for architectural choices, inline docs for non-obvious code, and runbooks for operational knowledge.

Graceful degradation and meaningful error messages. Errors are first-class citizens, not afterthoughts. Every error path is designed, not discovered.

Accessible, performant, responsive UI patterns. Component design, state management discipline, and Core Web Vitals compliance.

Trunk-based development with atomic commits, clean history, and meaningful commit messages. Every commit should be deployable.

Transforms imperative instructions into declarative goals with verifiable success criteria. Enables autonomous looping until verified completion.

Detects and mitigates LLM hallucinations in production pipelines. Validates AI-generated facts, code, and decisions before they reach end users or downstream systems.

Converts vague ideas into concrete, testable specifications with acceptance criteria. No implementation begins without a spec.

Build in verifiable increments. Never implement more than can be tested right now. Ship partial working systems over complete broken ones.

Test real system boundaries, not mocks of mocks. Integration tests verify that components work together, not that they work in isolation.

Converts unstructured meeting notes into structured, assigned, time-bounded action items. Never leave a meeting without knowing who does what by when.

Designs and coordinates multi-agent pipelines where specialized agents collaborate to complete complex tasks. Includes communication protocols, failure handling, and state management.

Structured logging, distributed tracing, and alerting for AI systems and traditional services. You can't fix what you can't see.

Measure first, optimize second. Data-driven performance improvements with before/after benchmarks and production validation.

Zero-downtime deployments with pre-flight checks, staged rollouts, and rollback plans. Never ship to production without a verified rollback strategy.