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Found 92 Skills
Write and audit Python code comments using antirez's 9-type taxonomy. Two modes - write (add/improve comments in code) and audit (classify and assess existing comments with structured report). Use when users request comment improvements, docstring additions, comment quality reviews, or documentation audits. Applies systematic comment classification with Python-specific mapping (docstrings, inline comments, type hints).
Hamilton Helmer's 7 Powers framework applied to a business. Spawns a team of specialist agents — Power Cartographer, Lifecycle Timer, Counter-Positioning Scout, and Moat Devil's Advocate — who each apply a distinct lens from Helmer's taxonomy. The lead synthesizes into a Power Inventory (what you have), Power Pipeline (what's achievable given your stage), and the honest Helmer Verdict. Use when the user says "helmer this", "apply 7 powers", "what power does this company have", "is this a moat", "diagnose my competitive position", or proposes a business and wants strategic analysis. Works standalone or after /thiel (which confirms you need a monopoly) or /munger (which asks if the economics are durable).
Analyze a user's Plannotator plan archive to extract denial patterns, feedback taxonomy, evolution over time, and actionable prompt improvements — then produce a polished HTML dashboard report. Falls back to Claude Code ExitPlanMode denial reasons when Plannotator data is unavailable.
Systematic web application QA testing with structured issue taxonomy, health scoring, and regression tracking. Use this skill when the user asks for QA testing, systematic testing, smoke testing, regression testing, web app testing, browser testing, or says "QA this", "test the app", "smoke test", "run QA", "systematic test", "check the site", "regression test", "full QA", "/qa-systematic". Supports full, quick, and regression modes.
Product analytics instrumentation and strategy covering event taxonomy design, tracking plans, user behavior analysis, activation/retention metrics, and marketing attribution. PostHog-first with multi-platform support (Pendo, Amplitude, Mixpanel, Heap).
Analyze user/customer feedback and produce a User Feedback Analysis Pack (source inventory, normalized feedback table, taxonomy/codebook, themes + evidence, recommendations, and feedback loop). Use for voice of customer, feature request analysis, support ticket synthesis, churn reason synthesis, and survey open-ends.
Apply the formal standard for React component engineering focusing on accessibility, composition, and styling. Use for building professional, composable React artifacts. Use proactively when creating or reviewing React components. Examples: - user: "/component-create Button trigger" → build accessible button with asChild and keyboard map - user: "/component-review src/components/Input.tsx" → audit for accessibility and composition compliance - user: "Build a responsive slider" → select taxonomy type and implement with data attributes - user: "Review my layout component" → check for monolithic patterns vs composition
Generate a custom trace annotation web app for open coding during LLM error analysis. Use when the user wants to review LLM traces, annotate failures with freeform comments, and do first-pass qualitative labeling (open coding). Also use when the user mentions "annotate traces", "trace review tool", "open coding tool", "label traces", "build an annotation interface", "review LLM outputs", or wants to manually inspect pipeline traces before building a failure taxonomy. This skill produces a tailored Python web application using FastHTML, TailwindCSS, and HTMX.
Quick pragmatic review of .NET test code for anti-patterns that undermine reliability and diagnostic value. Use when asked to review tests, find test problems, check test quality, or audit tests for common mistakes. Catches assertion gaps, flakiness indicators, over-mocking, naming issues, and structural problems with actionable fixes. Use for periodic test code reviews and PR feedback. For a deep formal audit based on academic test smell taxonomy, use exp-test-smell-detection instead. Works with MSTest, xUnit, NUnit, and TUnit.
Comprehensive testing doctrine for software and AI systems — covers positive patterns, anti-patterns, gates for coding agents writing tests, CI discipline, and an LLM/agent evaluation primer. Use when authoring or reviewing tests, adding mocks, deciding test placement, generating tests via agents, debugging flaky CI, designing eval suites for LLM features, or rebuilding a brittle test suite. Contains 12 positive patterns (selector hierarchy, table-driven, builders, real-system gates), 25 anti-patterns across Brittleness, Flakiness, Mock-misuse, Process, and AI-specific families, 7 mandatory gates for agents writing tests, flaky-test taxonomy with quarantine workflow, contract / property / mutation testing patterns, and an oracle-ladder primer for LLM-as-judge and agent eval. Language-agnostic — pseudo-code only. Don't use for general code review, library-specific debugging unrelated to tests, non-testing CI pipeline design, or production observability.
Access protein metadata, function, taxonomy, and sequences across UniProtKB, UniParc, and UniRef. Use when searching for proteins, mapping identifiers, or retrieving functional annotations and publications. Don't use for sequence alignment, protein folding, or sequence similarity search (use specialized skills for those tasks).
Deep formal test smell audit based on academic research taxonomy (testsmells.org). Detects 19 categorized smell types — conditional logic, mystery guests, sensitive equality, eager tests, and more — with calibrated severity and research-backed remediation. Use for comprehensive test suite health assessments. For a quick pragmatic review, use test-anti-patterns instead. DO NOT USE FOR: writing new tests (use writing-mstest-tests), evaluating assertion quality specifically (use assertion-quality), or finding test duplication and boilerplate (use exp-test-maintainability).