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Found 5,550 Skills
Explore and query any dataset annotated with a Frictionless Data Package descriptor (datapackage.json). Use this skill whenever a user wants to discover what tables or resources a dataset contains, look up column names and descriptions, surface usage warnings embedded in metadata, or understand how to load data from Parquet files, DuckDB or SQLite databases, or CSV files described by a datapackage.json. Also use when the user has a datapackage.json and wants to know what's in it, how to query it efficiently, or how to connect its metadata to actual data files. Pairs well with dataset-specific skills (like `pudl`) that layer domain knowledge on top.
Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check deployment status, verify serving endpoints, or clean up resources by undeploying models and deleting endpoints. Use when asked to deploy models on Agent Platform, list available Model Garden models, check if a model is deployable, query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for public Vertex AI deployments (use the `vertex-deploy` skill) or for running model evaluations (use the `agent-platform-eval` skill).
React and Next.js performance optimization patterns adapted from Vercel Engineering's React Best Practices (https://github.com/vercel-labs/agent-skills). Organizes 70+ rules across 8 priority categories — waterfalls, bundle size, server-side, client fetching, re-render, rendering, JS micro-perf, advanced. Use when writing, reviewing, or refactoring React/Next.js code for performance.
54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.
Pre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
Use to deploy the vss-behavior-analytics service standalone (entrypoint, config-source, optional calibration). Not for the full warehouse deploy.
Build deterministic forecast scripts with Earth2Studio (model, data source, IO, inference). Do NOT use for ensemble, diagnostics, data-only fetch, or install.
The canonical rulebook of UI craft and feel. Every element must earn its place and do its one job well; the surface must be tight, clean, calm, and genuinely pleasant to use. The single source of truth that landing-audit grades against and that shipit checks touched UI files against before commit. Use when building or editing ANY user-facing UI, page, screen, hero, or component, or when the user mentions design, layout, hierarchy, typography, color, spacing, contrast, CTAs, or micro-interactions.
Interview the user through their OWN investment screening checklist - you ASK the questions one at a time and the USER answers and clears each gate; the tool never answers, never invents questions, and never checks a gate off. The questions are James's own and live in his Obsidian vault (the source of truth); ask exactly what is there. A decision-support thinking tool, not financial advice. Complements /munger. Use when the user invokes /investment-checklist, says "run this through my investment checklist", "screen this idea/stock/ticker", or pastes a thesis/ticker to be screened.
Download NVIDIA Jetson Linux BSP artifacts (BSP tarball, sample rootfs, public_sources, x-tools, guides) for the active target. Use for Auto Setup; not for extraction or profile edits.
Audit whether a repo's docs actually ANSWER the questions a reader has — by spawning fresh, cheap (Haiku) agents that cold-read ONLY the docs and measuring how fast they reach the answer, whether they hit dead-ends, whether they fall back to source code, and whether they cite docs that contradict each other. Use after a doc reorg, when docs "feel scattered," or when the same confusion keeps recurring. Surfaces findability gaps (a corpus can be COMPLETE — every doc indexed — yet not FINDABLE) plus a prioritized fix list. Works on any repo's docs, not just this one.
Route a full Blazor work request across the appropriate specialist lane(s). Use when the request spans more than one concern (authoring, data, auth, review) or when lane selection itself is uncertain. Triggers on full-request phrasing: "implement this feature", "review and refactor this page", "build this form end-to-end". Distinct from blazor-component-architect (user-level, external, single-lane authoring guidance that may be invoked as a specialist resource).