Code Quality

Audits an existing test file or suite in any language for anti-patterns and quality issues — produces a severity-ranked report (Critical/Warning/Info). INVOKE whenever asked to audit or review tests, find what's wrong with a suite, judge whether tests are any good, or check for: tests that pass but verify nothing, missing assertions, swallowed exceptions, self-comparing / tautological assertions, coverage-touching tests, broad exceptions, flaky or order-dependent tests (Thread.Sleep, DateTime.Now, shared state), duplicated tests, or magic values — in .NET, Python/pytest, TS/Jest, Java, Go, Ruby or C++. DO NOT USE FOR: writing new tests (use code-testing-agent, or writing-mstest-tests for MSTest); running tests (use run-tests); migration; assertion-diversity metrics (use assertion-quality); coverage/CRAP metrics (use coverage-analysis); the testsmells.org academic catalog (use test-smell-detection); fixing or modernizing MSTest tests, assertions, attributes, or lifecycle (use writing-ms

Use when the user asks for founder product video or a task matching the examples below. Generate a 65-second founder-style product video from a product URL + user-supplied imagery. Output is a 16:9 1080p MP4 — 4 × 15s SeeDance acts of a talking founder + 5s branded end card + background music. The user's actual product screenshots are composited onto product reveal shots after Seedance generation, so readable UI and brand text are real, not AI-imagined. Triggers — "founder video", "product video", "60s pitch video", "make a video of [founder] for [URL]", "talking founder explainer". Requires Pika MCP. Uses a supplied brand kit folder (`brand.json` or an exported build-a-brand kit with `brand.md`, tokens, logo assets); if no kit exists, run build-a-brand first.

Guide for using Microsoft MarkItDown - a Python utility for converting files to Markdown. Use when converting PDF, Word, PowerPoint, Excel, images, audio, HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs, Jupyter notebooks, RSS feeds, or Wikipedia pages to Markdown format. Also use for document processing pipelines, LLM preprocessing, or text extraction tasks.

When the user wants to use GitHub for SEO, parasite SEO, GEO, open source marketing, README optimization, or curated Awesome lists. Also use when the user mentions "GitHub," "GitHub SEO," "GitHub parasite SEO," "GitHub GEO," "awesome list," "GitHub README," "profile README," "pinned repositories," "Trending," "Explore," "repository name," "About section," "GitHub description," "GitHub topics," "Website field," "GitHub Pages," "github.io," "user site," "project site," "GitHub gist," "curated list," or "navigation list." Not for Medium or other non-GitHub platforms—use parasite-seo or medium-posts. For OSS business model, use open-source-strategy.

Generate Drizzle ORM schemas for Cloudflare D1 databases with correct D1-specific patterns. Produces schema files, migration commands, type exports, and DATABASE_SCHEMA.md documentation. Handles D1 quirks: foreign keys always enforced, no native BOOLEAN/DATETIME types, 100 bound parameter limit, JSON stored as TEXT. Use when creating a new database, adding tables, or scaffolding a D1 data layer.

Use when the user asks for podcast or a task matching the examples below. Two-host podcast video for any URL or free-form topic — 1 minute, 4 acts × ~15s, native multi-shot dialogue, optional voice cloning for Host A. Use when the user asks to "make a podcast", "podcast about [thing]", "podcast review of [url]", "two-host explainer", "interview-style clip", "two people talking on camera", "I/me and X talk about Y", or "interview with [persona] about [topic]". Native audio is the deliverable; captions are skipped by default because podcast dialogue mistranscribes domain terms.