Topic: llm
10,059 skills in this topic.
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async-jobs
Async job processing patterns for background tasks, Celery workflows, task scheduling, retry strategies, and distributed task execution. Use when implementing background job processing, task queues, or scheduled task systems.
yonatangross/orchestkit 143
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visualize-plan
Visualize planned changes before implementation. Use when reviewing plans, comparing before/after architecture, assessing risk, or analyzing execution order and impact.
yonatangross/orchestkit 143
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storybook-mcp-integration
Storybook MCP server integration for component-aware AI development. Covers 6 tools across 3 toolsets (dev, docs, testing): component discovery via list-all-documentation/get-documentation, story previews via preview-stories, and automated testing via run-story-tests. Use when generating components that should reuse existing Storybook components, running component tests via MCP, or previewing stories in chat.
yonatangross/orchestkit 143
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database-patterns
Database design and migration patterns for Alembic migrations, schema design (SQL/NoSQL), and database versioning. Use when creating migrations, designing schemas, normalizing data, managing database versions, or handling schema drift.
yonatangross/orchestkit 143
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product-analytics
A/B test evaluation, cohort retention analysis, funnel metrics, and experiment-driven product decisions. Use when analyzing experiments, measuring feature adoption, diagnosing conversion drop-offs, or evaluating statistical significance of product changes.
yonatangross/orchestkit 143
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testing-unit
Unit testing patterns for isolated business logic tests — AAA pattern, parametrized tests (test.each, @pytest.mark.parametrize), fixture scoping (function/module/session), mocking with MSW/VCR at network level, and test data management with factories (FactoryBoy, faker-js). Use when writing unit tests, setting up mocks, structuring test data, optimizing test speed, choosing fixture scope, or reducing test boilerplate. Covers Vitest, Jest, pytest.
yonatangross/orchestkit 143
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code-review-playbook
Use this skill when conducting or improving code reviews. Provides structured review processes, conventional comments patterns, language-specific checklists, and feedback templates. Use when reviewing PRs or standardizing review practices.
yonatangross/orchestkit 143
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testing-perf
Performance and load testing patterns — k6 load tests, Locust stress tests, pytest execution optimization (xdist parallel, plugins), test type classification, and performance benchmarking. Use when writing load tests, optimizing test execution speed, or setting up pytest infrastructure.
yonatangross/orchestkit 143
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monitoring-observability
Monitoring and observability patterns for Prometheus metrics, Grafana dashboards, Langfuse v4 LLM tracing (as_type, score_current_span, should_export_span, LangfuseMedia), and drift detection. Use when adding logging, metrics, distributed tracing, LLM cost tracking, or quality drift monitoring.
yonatangross/orchestkit 143
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setup
Personalized setup and onboarding wizard. Use when setting up OrchestKit for a new project, configuring plugins, or generating a readiness score and improvement plan.
yonatangross/orchestkit 143
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dream
Nightly memory consolidation — prunes stale entries, merges duplicates, resolves contradictions, rebuilds MEMORY.md index. Use when memory files have accumulated over many sessions and need cleanup. Do NOT use for storing new decisions (use remember) or searching memory (use memory).
yonatangross/orchestkit 143
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skill-evolution
Analyzes skill usage patterns and suggests improvements. Use when reviewing skill performance, applying auto-suggested changes, or rolling back versions.
yonatangross/orchestkit 143
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commit
Creates commits with conventional format and validation. Use when committing changes or generating commit messages.
yonatangross/orchestkit 143
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fix-issue
Fixes GitHub issues with parallel analysis. Use when debugging errors, resolving regressions, fixing bugs, or triaging issues.
yonatangross/orchestkit 143
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demo-producer
Creates polished demo videos for skills, tutorials, and CLI demonstrations. Use when producing video showcases, marketing content, or terminal recordings.
yonatangross/orchestkit 143
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json-render-catalog
json-render component catalog patterns for AI-safe generative UI. Define Zod-typed catalogs that constrain what AI can generate, use @json-render/shadcn for 29 pre-built components, optimize specs for token efficiency with YAML mode. Use when building AI-generated UIs, defining component catalogs, or integrating json-render into React/Vue/Svelte/React Native projects.
yonatangross/orchestkit 143
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design-context-extract
Extract design DNA from existing app screenshots or live URLs using Google Stitch. Produces color palettes, typography specs, spacing tokens, and component patterns as design-tokens.json or Tailwind config. Use when auditing an existing design, creating a design system from a live app, or ensuring new pages match an established visual identity.
yonatangross/orchestkit 143
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testing-patterns
Redirect — testing-patterns was split into 5 focused sub-skills. Use when looking for testing-patterns, writing tests, or test automation. Redirects to testing-unit, testing-e2e, testing-integration, testing-llm, or testing-perf.
yonatangross/orchestkit 143
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presentation-builder
Creates zero-dependency, animation-rich HTML presentations from scratch or by converting PowerPoint files. Use when the user wants to build a presentation, convert a PPT/PPTX to web slides, or create a slide deck for a talk, pitch, or tutorial. Generates single self-contained HTML files with inline CSS/JS.
yonatangross/orchestkit 143
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analytics
Query cross-project usage analytics. Use when reviewing agent, skill, hook, or team performance across OrchestKit projects. Also replay sessions, estimate costs, and view model delegation trends.
yonatangross/orchestkit 143
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github-operations
GitHub CLI operations for issues, PRs, milestones, and Projects v2. Covers gh commands, REST API patterns, and automation scripts. Use when managing GitHub issues, PRs, milestones, or Projects with gh.
yonatangross/orchestkit 143
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ai-ui-generation
AI-assisted UI generation patterns for json-render, v0, Bolt, and Cursor workflows. Covers prompt engineering for component generation, review checklists for AI-generated code, design token injection, refactoring for design system conformance, and CI gates for quality assurance. Use when generating UI components with AI tools, rendering multi-surface MCP visual output, reviewing AI-generated code, or integrating AI output into design systems.
yonatangross/orchestkit 143
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memory-fabric
Knowledge graph memory orchestration - entity extraction, query parsing, deduplication, and cross-reference boosting. Use when designing memory orchestration.
yonatangross/orchestkit 143
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domain-driven-design
Domain-Driven Design tactical patterns for complex business domains. Use when modeling entities, value objects, domain services, repositories, or establishing bounded contexts.
yonatangross/orchestkit 143