37 Skills That Power Agents
Reference skills are the knowledge library behind OrchestKit agents -- auto-injected patterns for backend, frontend, databases, security, testing, LLM/RAG, and more.
What Are Reference Skills?
Reference skills are the 37 skills with user-invocable: false in their frontmatter. You never invoke them directly. Instead, they are automatically injected into agent context through three paths:
- Agent frontmatter -- Each agent declares which skills it needs. When the agent spawns, those skills load automatically.
- Command skill composition -- When you run
/ork:implement, itsskills:field pulls in reference skills likeapi-design-frameworkandunit-testing. - Keyword auto-suggest -- The
skill-auto-suggesthook detects keywords in your prompt and injects matching skills.
Every reference skill declares an agent: field in its frontmatter, indicating which agent primarily consumes it. This creates a natural grouping by domain.
Backend Architecture
Primary agent: backend-system-architect
Skills for designing and building server-side systems with Python, FastAPI, and distributed patterns.
| Skill | Description |
|---|---|
api-design-framework | REST, GraphQL, and gRPC API design patterns with OpenAPI templates |
api-versioning | URI, header, and query parameter versioning strategies |
asyncio-advanced | Advanced Python asyncio patterns, task groups, and structured concurrency |
aggregate-patterns | DDD aggregate design, consistency boundaries, and event publishing |
backend-architecture-enforcer | Layer validation and architectural constraint enforcement |
connection-pooling | Database connection pool sizing, health checks, and failover |
distributed-locks | Redis and PostgreSQL advisory lock patterns for concurrency control |
domain-driven-design | Bounded contexts, entities, value objects, and repositories |
error-handling-rfc9457 | RFC 9457 Problem Details for structured error responses |
fastapi-advanced | FastAPI dependency injection, middleware, background tasks |
grpc-python | gRPC service definitions and Python implementations |
idempotency-patterns | Idempotency keys, deduplication, and safe retry patterns |
mcp-server-building | Building MCP servers for Claude Code extensibility |
rate-limiting | Token bucket, sliding window, and tiered rate limiting |
resilience-patterns | Circuit breakers, bulkheads, retries, and fallbacks |
sqlalchemy-2-async | SQLAlchemy 2.0 async engine, sessions, and query patterns |
strawberry-graphql | Strawberry GraphQL with FastAPI integration |
Frontend and UI
Primary agent: frontend-ui-developer
Skills for React, Next.js, and modern frontend development.
| Skill | Description |
|---|---|
biome-linting | Biome linter and formatter configuration |
core-web-vitals | LCP, FID, CLS optimization patterns |
dashboard-patterns | Data dashboard layout, charts, and real-time update patterns |
edge-computing-patterns | Edge runtime, middleware, and CDN optimization |
form-state-patterns | Form state management with React Hook Form and Zod |
i18n-date-patterns | Internationalization, locale-aware dates, and number formatting |
image-optimization | Next.js Image, responsive images, and lazy loading |
lazy-loading-patterns | Code splitting, dynamic imports, and suspense boundaries |
pwa-patterns | Progressive web app: service workers, offline, and push notifications |
radix-primitives | Radix UI unstyled primitive components |
react-server-components-framework | Next.js 16+ App Router, RSC, Cache Components, Server Actions |
recharts-patterns | Recharts data visualization patterns and responsive charts |
render-optimization | React rendering performance, memoization, and virtual lists |
responsive-patterns | Container queries, fluid typography, and responsive layouts |
scroll-driven-animations | CSS scroll-driven animations and intersection observer |
shadcn-patterns | shadcn/ui component patterns and customization |
streaming-api-patterns | Server-Sent Events, WebSocket, and streaming fetch |
tanstack-query-advanced | TanStack Query caching, prefetching, and optimistic updates |
type-safety-validation | TypeScript strict mode, Zod schemas, and type narrowing |
view-transitions | View Transitions API for smooth page and state transitions |
vite-advanced | Vite configuration, plugins, and build optimization |
zustand-patterns | Zustand state management patterns and middleware |
Database
Primary agent: database-engineer
Skills for schema design, migrations, vector search, and database operations.
| Skill | Description |
|---|---|
alembic-migrations | Alembic migration generation, autogenerate, and rollback |
database-schema-designer | Schema design, normalization, indexing strategies |
database-versioning | Database version control and schema evolution |
pgvector-search | PGVector HNSW + BM25 hybrid search with Reciprocal Rank Fusion |
zero-downtime-migration | Online schema changes, shadow tables, and dual-write patterns |
Testing
Primary agent: test-generator
Skills for test strategy, frameworks, and quality enforcement.
| Skill | Description |
|---|---|
a11y-testing | Accessibility testing with axe, screen readers, and ARIA |
browser-automation | Playwright browser automation and scraping patterns |
contract-testing | Consumer-driven contract testing with Pact |
e2e-testing | End-to-end testing strategies with Playwright |
integration-testing | Integration test patterns, fixtures, and database isolation |
llm-testing | Testing LLM applications: determinism, evaluation, mocking |
msw-mocking | Mock Service Worker for API mocking in tests |
property-based-testing | Hypothesis and property-based test generation |
pytest-advanced | Advanced pytest fixtures, parametrize, and plugin patterns |
test-data-management | Test data factories, fixtures, and seeding strategies |
test-standards-enforcer | Test naming conventions, coverage targets, and quality rules |
unit-testing | Unit test AAA pattern, isolation, and coverage targets |
vcr-http-recording | HTTP interaction recording and replay for tests |
webapp-testing | Full-stack web application test strategies |
Security
Primary agent: security-auditor
Skills for application security, authentication, and vulnerability prevention.
| Skill | Description |
|---|---|
auth-patterns | JWT, OAuth2, passkey/WebAuthn, and session management |
input-validation | Input sanitization, type coercion, and injection prevention |
llm-safety-patterns | Prompt injection defense, output filtering, and safety guardrails |
mcp-security-hardening | MCP server security: auth, rate limiting, and sandboxing |
owasp-top-10 | OWASP Top 10 vulnerabilities and mitigations |
security-scanning | SAST, DAST, dependency scanning, and secret detection |
LLM, RAG, and AI
Primary agent: llm-integrator
Skills for building AI-powered applications with retrieval, evaluation, and inference optimization.
| Skill | Description |
|---|---|
fine-tuning-customization | LLM fine-tuning strategies, data preparation, and evaluation |
function-calling | Tool/function calling patterns for Claude and OpenAI models |
high-performance-inference | Inference optimization: batching, quantization, and caching |
llm-evaluation | LLM evaluation frameworks, metrics, and benchmarking |
llm-streaming | Streaming LLM responses, token-by-token rendering |
mcp-advanced-patterns | Advanced MCP patterns: composable tools, streaming, multi-server |
ollama-local | Ollama local model deployment and inference |
prompt-caching | Prompt caching strategies for cost reduction |
prompt-engineering-suite | Prompt design patterns, chain-of-thought, and few-shot |
Data Pipelines and RAG
Primary agent: data-pipeline-engineer
Skills for data processing, retrieval-augmented generation, and caching.
| Skill | Description |
|---|---|
agentic-rag-patterns | Agentic RAG with routing, self-correction, and adaptive retrieval |
background-jobs | Background task queues with Celery, Redis, and async workers |
browser-content-capture | Web content extraction and structured data capture |
caching-strategies | Redis caching: write-through, write-behind, cache-aside |
contextual-retrieval | Contextual retrieval with document chunking and metadata |
devops-deployment | CI/CD pipelines, Docker, and deployment automation |
embeddings | Text embedding models, vector stores, and similarity search |
golden-dataset-curation | Golden dataset creation, validation, and quality scoring |
golden-dataset-management | Dataset versioning, storage, and lifecycle management |
golden-dataset-validation | Dataset quality validation, bias detection, and coverage analysis |
hyde-retrieval | Hypothetical Document Embeddings for improved retrieval |
query-decomposition | Complex query decomposition into sub-queries |
rag-retrieval | RAG pipeline patterns: indexing, retrieval, generation |
reranking-patterns | Cross-encoder reranking and Reciprocal Rank Fusion |
semantic-caching | Semantic similarity caching for LLM responses |
LangGraph Workflows
Primary agent: workflow-architect
Skills for building stateful, multi-step AI workflows with LangGraph.
| Skill | Description |
|---|---|
agent-loops | Agent loop patterns: ReAct, plan-and-execute, reflection |
alternative-agent-frameworks | Comparison of CrewAI, AutoGen, and LangGraph |
langgraph-checkpoints | LangGraph state persistence and checkpoint management |
langgraph-functional | LangGraph functional API for stateless graph definitions |
langgraph-human-in-loop | Human-in-the-loop patterns with interrupts and approvals |
langgraph-parallel | Parallel node execution and fan-out/fan-in patterns |
langgraph-routing | Conditional routing and dynamic edge selection |
langgraph-state | State management: TypedDict, Pydantic, and Annotated operators |
langgraph-streaming | Token and event streaming from LangGraph workflows |
langgraph-subgraphs | Nested subgraphs and modular workflow composition |
langgraph-supervisor | Multi-agent supervisor patterns with delegation |
langgraph-tools | Tool integration and function calling in LangGraph nodes |
multi-agent-orchestration | Multi-agent coordination patterns and protocols |
multi-scenario-orchestration | Complex multi-scenario workflow orchestration |
task-dependency-patterns | Task dependency graphs and parallel execution |
temporal-io | Temporal.io durable execution and workflow patterns |
Event-Driven Architecture
Primary agent: event-driven-architect
| Skill | Description |
|---|---|
cqrs-patterns | Command Query Responsibility Segregation patterns |
event-sourcing | Event sourcing, projections, and event store patterns |
message-queues | RabbitMQ, Kafka, and Redis Streams messaging patterns |
outbox-pattern | Transactional outbox for reliable event publishing |
saga-patterns | Saga orchestration and choreography for distributed transactions |
Observability and Metrics
Primary agents: metrics-architect, monitoring-engineer
| Skill | Description |
|---|---|
cache-cost-tracking | Cache hit/miss tracking and cost attribution |
drift-detection | Configuration and infrastructure drift detection |
langfuse-observability | LangFuse tracing for LLM application observability |
observability-monitoring | OpenTelemetry, Prometheus, Grafana, and alerting |
okr-kpi-patterns | OKR/KPI definition, tracking, and dashboard patterns |
pii-masking-patterns | PII detection and masking in logs and data streams |
performance-testing | Load testing with k6, Locust, and benchmark design |
silent-failure-detection | Detecting silent failures in distributed systems |
Code Quality
Primary agent: code-quality-reviewer
| Skill | Description |
|---|---|
clean-architecture | SOLID, hexagonal architecture, ports and adapters |
code-review-playbook | Code review checklists, feedback patterns, and review culture |
evidence-verification | Evidence-based code verification and claim validation |
project-structure-enforcer | Project structure conventions and directory validation |
quality-gates | Quality gate definitions, metrics thresholds, and CI enforcement |
Video and Demo Production
Primary agent: demo-producer
| Skill | Description |
|---|---|
audio-mixing-patterns | Audio mixing, ducking, and soundtrack composition |
content-type-recipes | Video content type templates: tutorials, demos, showcases |
elevenlabs-narration | ElevenLabs text-to-speech narration generation |
hook-formulas | Video hook formulas for viewer retention |
music-sfx-selection | Music and sound effect selection for video |
narration-scripting | Narration script writing and pacing |
scene-intro-cards | Scene introduction card design and animation |
thumbnail-first-frame | Thumbnail and first-frame optimization |
video-pacing | Video pacing, timing, and rhythm patterns |
video-storyboarding | Video storyboard creation and shot planning |
Additional Domains
Skills spanning accessibility, UX research, product strategy, and more.
| Skill | Agent | Description |
|---|---|---|
focus-management | accessibility-specialist | Keyboard focus management and focus traps |
react-aria-patterns | accessibility-specialist | React Aria accessible component patterns |
wcag-compliance | accessibility-specialist | WCAG 2.2 compliance guidelines and testing |
persona-journey-mapping | ux-researcher | User persona creation and journey mapping |
user-research-methods | ux-researcher | User research methods: interviews, surveys, usability |
business-case-analysis | business-case-builder | ROI analysis, business case templates |
design-system-starter | rapid-ui-designer | Design system bootstrapping and token systems |
motion-animation-patterns | rapid-ui-designer | CSS and JS animation patterns |
market-analysis-patterns | market-intelligence | Market analysis frameworks and competitive research |
product-strategy-frameworks | product-strategist | Product strategy: RICE, MoSCoW, Kano |
prioritization-frameworks | prioritization-analyst | Prioritization methods and scoring models |
requirements-engineering | requirements-translator | Requirements gathering, user stories, acceptance criteria |
system-design-interrogation | system-design-reviewer | System design review questions and evaluation |
platform-upgrade-knowledge | deployment-manager | Platform upgrade procedures and compatibility |
Cross-Cutting Skills
These skills do not declare an agent: field. They serve multiple agents or the core system.
| Skill | Description |
|---|---|
ascii-visualizer | ASCII art diagram generation for architecture visualization |
best-practices | General software engineering best practices |
context-compression | Context window optimization and compression techniques |
context-engineering | Context engineering patterns for LLM applications |
git-workflow | Git branching, atomic commits, and recovery patterns |
github-operations | GitHub CLI operations: issues, milestones, releases |
memory-fabric | Graph-first memory orchestration across tiers |
monorepo-context | Monorepo navigation and context management |
performance-optimization | General performance optimization patterns |
release-management | Release workflows, semantic versioning, changelogs |
run-tests | Test runner orchestration and result parsing |
skill-analyzer | Skill content analysis and optimization recommendations |
stacked-prs | Multi-PR development with dependent pull requests |
web-research-workflow | Web research with Tavily search, extract, and mapping |
How Reference Skills Get to Agents
The connection between a reference skill and its agent is bidirectional:
- Skill side: The skill's
agent:frontmatter field declares which agent owns it (e.g.,agent: database-engineer). - Agent side: The agent's frontmatter
skills:array lists the skills it needs.
When the database-engineer agent spawns, Claude Code reads its frontmatter, finds the skills: array, and injects each listed skill's content into the agent's context window. The agent then has immediate access to all the patterns, examples, and checklists those skills contain.
What's Next
- 23 Command Skills -- The commands that orchestrate these reference skills.
- Composing Skills Into Workflows -- How everything fits together.
- Create Your Own SKILL.md -- Add reference skills for your own domain.
24 Commands You Can Invoke
Every /ork:X command skill in OrchestKit -- grouped by category with descriptions and usage examples.
Composing Skills Into Workflows
How command skills, reference skills, hooks, and agents combine to form intelligent workflows -- and when to use each composition pattern.
Last updated on