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Found 2,405 Skills
Pipeline skill for CMS-HCC risk adjustment calculation and coding gap identification. Use when the user asks to apply the ICD-10-to-HCC crosswalk, calculate RAF scores, resolve disease hierarchies programmatically, identify coding gaps from Rx or lab proxies, project member-level risk scores, build a risk adjustment data pipeline, compute HCC coefficients, run hierarchy resolution code, or generate member risk score reports. Triggers include "calculate RAF score", "ICD-10 to HCC crosswalk", "hierarchy resolution code", "coding gap detection", "Rx proxy gap", "lab proxy gap", "risk score projection", "HCC pipeline", "member risk report", "risk adjustment Python", "risk adjustment SQL", "RAF calculation code", "CMS-HCC pipeline".
Dev Container を最短で導入/更新するためのブートストラップ。stack を自動判定（node/python/rust）し、テンプレート適用または安全更新を行う。既存 .devcontainer がある場合はバックアップ後にマージ。Chat オプション: stack (auto|node|python|rust), packageManager (npm|pnpm|yarn), mode (safe|overwrite), includeTools (true|false), addCI (true|false)。
LangChain workflows for `create_agent`, LCEL chains, `bind_tools`, middleware, and structured output with production-safe orchestration. Use when implementing or refactoring LangChain application logic in Python or TypeScript.
Develop and deploy Lakeflow Jobs on Databricks. Use when creating data engineering jobs with notebooks, Python wheels, or SQL tasks. Invoke BEFORE starting implementation.
Pipeline skill for multi-omics data processing and integration. Use when the user asks to map gene IDs between HGNC Ensembl and UniProt, convert between omic data formats, run batch correction with ComBat or ComBat-seq, perform GSEA or over-representation analysis on multi-omic results, run consensus clustering on integrated data, execute MOFA2 in R or mofapy2 in Python, build a multi-omics ETL pipeline, harmonize feature identifiers across omics layers, or run clusterProfiler enrichment. Triggers include "map Ensembl to HGNC", "ID mapping omics", "ComBat code", "run MOFA2", "mofapy2", "GSEA Python", "fgsea R", "consensus clustering", "multi-omics pipeline", "gseapy", "biomaRt", "clusterProfiler", "mixOmics DIABLO".
Pipeline skill for protein structure analysis covering PDB/mmCIF parsing, RMSD superposition, Ramachandran/dihedral analysis, binding pocket detection, contact maps, B-factor flexibility, DSSP secondary structure, and format conversion. Triggers on PDB, protein structure, RMSD, Ramachandran, binding pocket, Biopython, PyMOL, pocket detection, fpocket, DSSP, PDBQT, structural alignment, superposition.
Structure a pygame (pygame-ce) game in Python: the init/event/update/draw loop, delta-time movement, Surface/Rect blitting, keyboard/mouse input, and Sprite/Group management with collision. Use when building or debugging a pygame game — when the user mentions pygame, pygame-ce, the game loop, blit, Surface, Rect, sprite groups, or clock.tick. Targets pygame-ce.
Finds and deletes "Captain Obvious" tests — tests that can never fail or check nothing. It catches assertions the type checker already guarantees (typeof/isinstance/toBeDefined on typed values), assertion-free tests, tautologies (expect(true).toBe(true), assert x == x, len >= 0), arrange-assert echoes (const x = 5; expect(x).toBe(5)), mock-echo tests, unawaited async assertions, dead/swallowed/conditional assertions, overly-broad pytest.raises(Exception), and duplicate test bodies. Use whenever the user wants to clean up a test suite, remove redundant/useless/tautological/AI-generated tests, mentions tests that "never fail" or "test nothing", or says "captain obvious". Works on TypeScript (Jest/Vitest/bun:test) and Python (pytest + mypy). The detection is fully deterministic — always run the bundled scripts, never scan test files one by one yourself.
Best practices for scikit-learn machine learning, model development, evaluation, and deployment in Python
Engineer effective LLM prompts using zero-shot, few-shot, chain-of-thought, and structured output techniques. Use when building LLM applications requiring reliable outputs, implementing RAG systems, creating AI agents, or optimizing prompt quality and cost. Covers OpenAI, Anthropic, and open-source models with multi-language examples (Python/TypeScript).
CLI-based image generation from text prompts using Google Gemini APIs via Python. Use when user needs "generate image", "create image with AI", "gemini image", "text to image", "create sprite", or "generate character art". Supports model selection, batch generation, watermark removal, and background transparency. Do NOT use for web app image features (use nano-banana-builder), video/audio generation, or non-Gemini models.
Apply Clean Architecture + DDD + Hexagonal patterns to backend services. Use when designing APIs, microservices, domain models, aggregates, repositories, bounded contexts, or scalable backend structure. Triggers on DDD, Clean Architecture, Hexagonal, ports and adapters, entities, value objects, domain events, CQRS, event sourcing, repository pattern, use cases, onion architecture, outbox pattern, aggregate root, anti-corruption layer. Language-agnostic (Go, Rust, Python, TypeScript, Java, C#).