Loading...
Loading...
Found 1,645 Skills
Transform a user-provided photo or image into a strange vintage Soviet or Eastern European children's book illustration with grotesque humorous cartoon energy, shaky ink, faded watercolor, dirty paper texture, awkward anatomy, nervous absurd expressions, sparse composition, and an absurd handwritten English rhyme. Use this skill when the user asks to turn a photo into an unsettling old children's book illustration, 1980s Eastern European illustration, weird Soviet cartoon book art, grotesque watercolor storybook art, or clumsy absurd illustrated caption style. Do NOT trigger for polished fantasy art, cute children's illustration, modern vector art, realistic portraits, anime, clean editorial illustration, or generic vintage filters.
Spatial data gridding and interpolation with a machine-learning style API. Process geographic and Cartesian point data onto regular grids. Use when Claude needs to: (1) Grid scattered spatial data onto regular grids, (2) Interpolate point data using splines, linear, or cubic methods, (3) Process geographic coordinates with projections, (4) Reduce large datasets using block averaging, (5) Remove polynomial trends from spatial data, (6) Cross-validate gridding parameters, (7) Create processing pipelines with Chain, (8) Grid vector data like GPS velocities.
Batch identify candidate stocks with mature breakout patterns, healthy volume-price structures, and good catalyst alignment, and output priorities, trigger conditions, and failure boundaries. Suitable for scenarios such as short-to-medium-term stock selection, pre-market candidate pool sorting, and screening leading candidate stocks in sector rotation.
Use when creating, validating, or documenting Nemo Gym pivot datasets from rollout, trajectory, chat-completion, Responses API, or tool-call artifacts. Covers Gym Responses-style row conversion, pivot selection, single-step tool-use configs, agent_ref alignment, verifier knobs, expected-action row contracts, and train/eval usage.
Use when reviewing, scoring, or auditing third-party SaaS / vendor relationships — running a vendor scorecard, tracking SLA compliance, classifying third-party risk, preparing a tier-1 vendor review, or auditing the SaaS portfolio. Triggers on "vendor SLA", "vendor scorecard", "third-party risk", "TPRM", "vendor review", "SaaS audit", "supplier performance", "vendor health check", "renewal review". Forks context so large vendor catalogs (50-500 line items) and SLA logs don't pollute the parent thread. Ships 3 stdlib-only Python tools (vendor scorer with industry tuning, SLA compliance tracker with credit-claim flags, vendor risk classifier across 4 risk vectors), 3 reference docs each citing 7+ authoritative sources (Gartner / Shared Assessments / NIST / ISO 27036 / breach post-mortems), and a 5-vendor catalog template. Distinct from c-level-advisor/general-counsel-advisor (contract law, not operational management), business-growth/contract-and-proposal-writer (outbound proposals, not inbound vendor scoring), and sibling procurement-optimizer (spend categorization, not vendor performance).
Build and maintain a Karpathy-style LLM knowledge base — a self-compiling Obsidian markdown wiki where an Agent ingests raw sources, compiles cross-linked concept/entity/summary pages, answers queries against the corpus, lints the graph for health, and audits in-context human feedback filed from Obsidian or the local web viewer. Use when (1) scaffolding a new knowledge base for any research topic, (2) ingesting articles/papers/PDFs/web pages into raw/, (3) compiling or restructuring wiki articles from existing raw material, (4) answering questions against the wiki and filing durable answers back, (5) running lint passes for dead links / orphan pages / coverage gaps / audit shape, (6) processing human feedback from the audit/ directory and applying corrections. Not for general note-taking, daily journals, or non-wiki Obsidian use.
Prioritized TypeScript React code review guidelines. Focuses on type safety, React conventions, performance, security, architecture, accessibility, error handling, and testing. First scan the code to identify issues, then obtain solutions and review comment templates from the references/ directory.
Self-hosted, open-source alternative to Google NotebookLM for AI-powered research and document analysis. Use when organizing research materials into notebooks, ingesting diverse content sources (PDFs, videos, audio, web pages, Office documents), generating AI-powered notes and summaries, creating multi-speaker podcasts from research, chatting with documents using context-aware AI, searching across materials with full-text and vector search, or running custom content transformations. Supports 16+ AI providers including OpenAI, Anthropic, Google, Ollama, Groq, and Mistral with complete data privacy through self-hosting.
Owns Python code style for this stack: ruff for lint + format, numpydoc for docstrings. Two responsibilities — (1) place the project's `ruff.toml` from the bundled template once the stack and workspace are in place, and (2) run ruff against any Python files Claude has just generated or edited. Stops at "the touched files pass `ruff check`." TRIGGER when (any of these): (1) a Python file was just created or edited via Write / Edit / MultiEdit — invoke this skill before declaring the task done so ruff is run on the touched files; (2) a fresh ML workspace was just scaffolded by `organize-ml-workspace` and the project has no `ruff.toml` at its root yet — drop the bundled template; (3) the user asks about lint, format, docstring style, or reaches for `black` / `isort` / `flake8` / `pydocstyle` (redirect to ruff — the stack's canonical linter, owned by `data-science-python-stack` Tier 1). SKIP when: the project is non-Python; the only edits in this turn are to Markdown / TOML / JSON / YAML; the file lives in a third-party vendored directory the user doesn't own. HOW TO USE: run ruff manually on the files you just touched — do not configure a PostToolUse hook for this. **Read the "Stop conditions" block and emit the Pre-flight checklist as visible text in your response — both are mandatory before running ruff.**
Fetch official brand/product/tool logos (Stripe, GitHub, Notion, AWS, Figma, etc.) as clean SVGs from SVGL (svgl.app) — as saved .svg files, inline markup, or installed React components. Check this whenever logos or SVGs come up, e.g. adding brand marks to integration/partner rows, footers, pricing tables, or slides; replacing a blurry logo with a vector; getting light/dark variants; or finding an official logo. Prefer it over hand-writing SVG markup or grabbing random files. Skip for generic UI icons, illustrations, charts, favicons from an existing logo, or designing a brand-new custom logo.
Morningstar Screener via API JSON publica: descarga masiva de 53 universes (102K+ listings, 39 paises, NYSE/Nasdaq/BCBA/etc) con 33 campos (precio, market cap, ratios, retornos 1d/1w/1m/3m/6m/12m/36m/60m/120m, deuda, dividend yield, sector, industria). Sin API key, sin auth.
Operate SkipCalls AI phone receptionists through MCP. Use when the user mentions SkipCalls, AI receptionist/answering service/front desk, missed or inbound calls, call transcripts, scheduling a one-time outbound call, greetings, tasks, calendars, transfers, SMS behavior, business profile Q&A, or MCP connector setup.