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Found 1,131 Skills
Authors and consumes feature-level domain knowledge files in ai-context/features/. Reference guide for bounded-context business rules, invariants, integration points, and known gotchas.
Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when correctness matters more than speed, when working in unfamiliar code, when stakes are high (production, security-sensitive logic, irreversible operations), or any time a confident output would be cheaper to verify now than to debug later.
Build WCAG 2.1 AA compliant websites with semantic HTML, proper ARIA, focus management, and screen reader support. Includes color contrast (4.5:1 text), keyboard navigation, form labels, and live regions. Use when implementing accessible interfaces, fixing screen reader issues, keyboard navigation, or troubleshooting "focus outline missing", "aria-label required", "insufficient contrast".
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
Type-safe environment variable validation using Zod with a Drizzle-like schema API. Supports server/public fields, feature flags, either-or constraints, and client-side protection.
Create isolated Neon database branches for testing. Schema-only branches with auto-cleanup via TTL, test server orchestration, and environment variable management.
shadcn/ui component patterns including CVA variants, OKLCH theming, cn() utility, and composition. Use when adding shadcn components, building variant systems, or customizing themes.
Find every way users can break your AI before they do. Use when you need to red-team your AI, test for jailbreaks, find prompt injection vulnerabilities, run adversarial testing, do a safety audit before launch, prove your AI is safe for compliance, stress-test guardrails, or verify your AI holds up against adversarial users. Covers automated attack generation, iterative red-teaming with DSPy, and MIPROv2-optimized adversarial testing.
ESM2 protein language model for embeddings and sequence scoring. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) scores, (2) Getting protein embeddings for clustering, (3) Filtering designs by sequence plausibility, (4) Zero-shot variant effect prediction, (5) Analyzing sequence-function relationships. For structure prediction, use chai or boltz. For QC thresholds, use protein-qc.
Evidence-based muscle hypertrophy guidance from Science and Development of Muscle Hypertrophy (2nd ed). Activate when users ask about muscle growth mechanisms, training variables for hypertrophy, rep ranges, volume, frequency, exercise selection, or program design for muscle building.
Material Symbols v3 variable icon font system. Use when adding icons to buttons, navigation, status indicators, or any UI element. Provides 2,500+ icons with fill, weight, grade, and optical size axes. Integrates with project color tokens.
Use when substantive documents (reviews, analyses, synthesis documents) need adversarial review to strengthen arguments, identify weak points, and challenge assumptions before editorial polish (mandatory for Writer → Devil's Advocate pairing protocol)