Loading...
Loading...
Found 98 Skills
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).
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.
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.
Use when you need systematic extraction of pain points, feature gaps, switching triggers, and opportunities from reviews (B2B review sites, app stores, forums, communities, issue trackers). Includes bias hygiene, taxonomy building, triangulation, and turning insights into experiments.
Design or audit AI-first help centers/knowledge bases/FAQs, including taxonomy, article templates, analytics, and AI support (RAG, chatbot, escalation), using 2025-2026 best practices
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.
Roblox AnalyticsService: custom events, economy tracking, funnels, rate limits, event taxonomy.
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.
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.
Guides structured security log analysis across authentication, network, endpoint, and cloud audit log sources. Auto-invoked when the user shares log data, asks about suspicious events, needs help interpreting Windows Event IDs or Linux auth logs, or is establishing baselines for anomaly detection. Produces log source taxonomy, anomaly identification, baseline recommendations, and correlation findings mapped to MITRE ATT&CK v16 techniques.
Smart contract and secure API contract security analysis — invariant checking, access control, reentrancy, and integer overflow patterns. Implements Checks-Effects-Interactions pattern, formal invariant verification, and OpenSCV vulnerability taxonomy for Solidity/EVM and Rust/Solana contracts.