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Found 1,573 Skills
Build FastAPI services with JWT auth, structlog, and Prometheus metrics. Use when creating or modifying a Python HTTP server, adding authentication, structured logging, or instrumentation to a FastAPI app.
Adding or changing routes in `apps/api`. One source of truth (`defineApiEndpoint` + a Zod schema) becomes an HTTP endpoint, an OpenAPI operation, an MCP tool, and a TS SDK method — descriptions and contracts must be written with all four readers in mind.
ClickHouse queries, Goose migrations, chdb test schema, Weaviate collections/migrations, or telemetry storage paths.
Use Ibis for database-agnostic data access in Python. Use when writing data queries, connecting to databases (DuckDB, PostgreSQL, SQLite), or building portable data pipelines that should work across backends.
Build Temporal workflow applications in Go. Use when creating or modifying Temporal workflows, activities, workers, clients, signals, queries, updates, retry policies, saga patterns, or writing Temporal tests.
Analyze Go function lengths within a workspace and generate statistics (p50, p90, p99). Use when you need to audit function complexity, identify long functions, or generate code metrics for Go projects. Only analyzes files matching **/*.go within the current workspace, excluding dependencies.
Crea documentos técnicos organizados en /docs (specs, planes, ADRs, referencias). Usa cuando el usuario diga "crear documento", "escribir spec", "documentar esto", "creame una spec", "escribime documentación", "hacer documentación", o quiera agregar documentación al proyecto.
Run a Bayesian A/B test on conversion data using PyMC. Use when the user wants to compare two variants (landing pages, emails, pricing, UI changes) and decide which to ship using posterior probabilities and expected loss instead of p-values. Covers Beta-Binomial model, ROPE, expected loss, sample-size guidance, and ArviZ diagnostics.
Fit Bayesian regression models with PyMC using the Hogg approach — start simple, diagnose problems, upgrade the likelihood. Use when the user needs regression with proper uncertainty quantification, heteroscedastic errors, outlier robustness, or model comparison. Covers Normal, Student-t, and GLM likelihoods with ArviZ diagnostics and LOO-CV.
Validate code quality, test coverage, performance, and security. Use when verifying implemented features meet all standards and requirements before marking complete.
Provides test design patterns, coverage strategies, and best practices for comprehensive test suite development
Feline interactions, buffs, and relationship building