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Found 2,140 Skills
Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.
Use this skill when implementing structured data markup using JSON-LD and Schema.org vocabulary for rich search results. Triggers on adding schema markup for FAQ, HowTo, Product, Article, Breadcrumb, Organization, LocalBusiness, Event, Recipe, or any Schema.org type. Covers JSON-LD implementation, Google Rich Results eligibility, validation testing, and framework integration (Next.js, Nuxt, Astro).
Use this skill when designing email campaigns, building drip sequences, improving deliverability, or A/B testing email content. Triggers on email campaigns, drip sequences, newsletter, email deliverability, subject lines, email automation, segmentation, open rates, click-through rates, and any task requiring email marketing strategy or execution.
Run a structured 5-day process to prototype, test, and validate product ideas with real users. Use when the user mentions "design sprint", "validate in a week", "rapid prototype", "test with users", or "de-risk before building". Covers mapping, sketching, deciding, prototyping, and testing. For ongoing experimentation, see lean-startup. For customer job analysis, see jobs-to-be-done. Trigger with 'design', 'sprint'.
Use when setting up or optimizing developer workflows in a monorepo, managing mise tasks, git hooks, CI/CD pipelines, database migrations, or release automation. Invoke for development environment setup, build automation, testing workflows, and release coordination.
factory_boy test data generation specialist. Covers Factory, DjangoModelFactory, SQLAlchemyModelFactory, all field declarations (Faker, LazyAttribute, Sequence, SubFactory, RelatedFactory, post_generation, Trait, Maybe, Dict, List), batch creation, pytest integration, and Celery task testing patterns. USE WHEN: user mentions "factory_boy", "test factory", "DjangoModelFactory", "SQLAlchemyModelFactory", asks about "test data generation", "factory traits", "SubFactory", "factory fixtures". DO NOT USE FOR: pytest internals - use `pytest`; Django setup - use `pytest-django`; Hypothesis property testing - use `pytest` with Hypothesis
End-to-end type safety patterns for API development. Covers Zod-to-OpenAPI, ts-rest, Zodios, and contract testing. Use for ensuring type consistency between backend and frontend. USE WHEN: user mentions "type-safe API", "end-to-end types", "Zod to OpenAPI", "ts-rest", "Zodios", "contract testing", asks about "share types between frontend and backend", "type safety across API", "API contract", "Pact testing" DO NOT USE FOR: tRPC (use `trpc` instead); GraphQL (use `graphql` instead); Simple OpenAPI generation (use `openapi-codegen` instead); Non-TypeScript projects
A/B test agent variants measuring quality and total session token cost across simple and complex benchmarks. Use when creating compact agent versions, validating agent changes, comparing internal vs external agents, or deciding between variants for production. Use for "compare agents", "A/B test", "benchmark agents", or "test agent efficiency". Do NOT use for evaluating single agents, testing skills, or optimizing prompts without variant comparison.
Storybook 10 testing patterns with Vitest integration, ESM-only distribution, CSF3 typesafe factories, play() interaction tests, Chromatic TurboSnap visual regression, module automocking, accessibility addon testing, and autodocs generation. Use when writing component stories, setting up visual regression testing, configuring Storybook CI pipelines, or migrating from Storybook 9.
Code quality and deviation gate between /implement and /test. Reads the task document and changed files, validates coding standards, classifies deviations (minor/medium/major), and decides whether implementation is ready for testing. Runs automatically in the auto-chain between implement and test. Also invoke manually after any implementation to catch issues before wasting a test run.
AI model safety scanner built on NVIDIA garak for testing LLMs against 179 security probes across 35 vulnerability families
Expert knowledge for Azure Analysis Services development including troubleshooting. Use when testing server connections, debugging gateway or firewall blocks, or checking connection strings and ports, and other Azure Analysis Services related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines).