automate-taskTurns one automation request into one runnable Python script with inline dependencies, a command line, logging, a dry run and exit codes. Confirms the domain with the user, presents package and design options with a recommendation, maps a parallelism level to worker counts, keeps every technical cho
build-web-serviceLeads the design of a Python HTTP service from a requirements interview to one vertical-slice handoff packet: classifies the service, brings in the contract, persistence and test design skills, and settles identity, deferred work, rate limits, caching and deployment lifecycle. The primary recipe is
data-analysisAnswers a question from data with a result that can be defended: fixes the question, the observation grain, the denominator and the baseline before choosing a method, validates along the deployment boundary without leakage, and reports effect size with uncertainty and what the evidence does not show
data-engineeringMakes data code correct first and cheap second: pins down the output, estimates sizes before running, checks schema and join contracts, chooses the engine, reads the query plan before tuning, and has results returned or published as artifacts instead of printed. Covers synthetic data and tests for d
design-persistenceDesigns how a Python system stores durable records: record meaning and access patterns, invariants and state transitions, where each constraint is enforced, transaction and concurrency ownership, repository boundaries and a migration plan. Chooses the database from the requirements, writes no projec
design-testsDesigns the tests that give evidence for a Python contract: scenarios chosen by ranked risk, each aimed at public behavior and able to fail for a plausible broken implementation. Writes no files: it briefs the test-engineer agent to write and run the tests, strict XFAIL where the implementation is m
enterprise-designTurns business intent into a caller-facing Python contract before implementation: outcomes, a rule table, the architecture boundary, and the exact public types, signatures and error classes, shown to the user as declarations. Keeps partial success, duplicates, expected rejection and dependency failu
tactical-slop-reviewReviews a Python pull request, branch or codebase against the expert's engineering guide and writes one file: PYTHON-REVIEW.md with findings or REFACTOR-PLAN.md with an ordered refactor plan. Facts come from the plugin's MCP tools and pylens subagents, every citation is checked by a tool before it s