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Found 1,923 Skills
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.
Trading expert for Forex/CFD, indices (DAX, NASDAQ, S&P500), MQL5, Pine Script v5, MT5 Python API. Use for: trading strategies, technical analysis, risk management, Expert Advisors, custom indicators, backtesting, copy trading, prop trading rules, Smart Money Concepts, ICT, Ichimoku Kinko Hyo, ŚWISTAK Fibonacci.
Provides file paths to language-specific reference files for the test ANALYSIS skills (assertion-quality, test-anti-patterns, test-gap-analysis, test-smell-detection, test-tagging). Call this skill to discover available extension files (e.g., dotnet.md for .NET/MSTest/xUnit/NUnit/TUnit, python.md for pytest/unittest, typescript.md for Jest/Vitest/Mocha, java.md for JUnit/TestNG, etc.). Do not use directly — invoked by the test-quality-auditor agent and polyglot analysis skills that need framework-specific lookup tables (test markers, assertion APIs, skip annotations, sleep patterns, mystery guest indicators, integration markers, setup/teardown, tag-support capability).
Profile a model running on MAX to find where it spends time and whether the GPU is saturated. Use when the user asks to "profile my model," "where is my model spending time," "why is inference slow," "is my GPU being utilized," "how much GPU am I using," "get a kernel breakdown," "capture an nsys/rocprof/ncu trace of max serve," or wants to measure MAX inference performance. Works for any model MAX can run — built-in architectures and custom ones loaded with --custom-architectures — from a pip or pixi install (max generate, max serve, or a Python script) on NVIDIA or AMD GPUs. Decide cheapest-first: a GPU utilization check, then a kernel breakdown, then a single-kernel deep dive only when one kernel dominates.
Provides guidance for authoring Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers environment context discovery, Airflow 2 vs 3 compatibility, authoring best practices, and local/remote validation processes. Use when creating or extending an Airflow DAG. Don't use when authoring Python code unrelated to Airflow DAGs.
Service metrics, RED metrics (Rate, Errors, Duration), and runtime-specific telemetry for .NET, Java, Node.js, Python, PHP, and Go applications.
MANDATORY for static requests to find, identify, or list untested source files or modules, sources without tests, source-to-test pairing, test-gap worklists, or suggested test locations. Invoke even for a tiny package; do not substitute manual globbing. Uses Roslyn for C#/.NET and tree-sitter for Python, TS/JS, Go, Java, Rust, and Ruby. DO NOT USE FOR: line/branch coverage, CRAP risk, or grading existing tests.
Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.
Execute code and manage compute on Databricks: run Python/Scala/SQL/R via serverless, classic, or interactive clusters, and create/resize/delete clusters and SQL warehouses.
Build Zerobus Ingest clients for near real-time data ingestion into Databricks Delta tables via gRPC. Use when creating producers that write directly to Unity Catalog tables without a message bus, working with the Zerobus Ingest SDK in Python/Java/Go/TypeScript/Rust, generating Protobuf schemas from UC tables, or implementing stream-based ingestion with ACK handling and retry logic.
Create new skills for the lovstudio/skills repo. Fork of the official skill-creator with lovstudio conventions: lovstudio: name prefix, skills/lovstudio-<name>/ directory structure, mandatory README.md per skill, SKILL.md with AskUserQuestion interactive flow, standalone Python CLI scripts, CJK text handling, and auto-update of root README + CLAUDE.md. Use when the user wants to create a new skill, add a skill to this repo, scaffold a skill, or mentions "新建skill", "创建skill", "new skill", "add skill", "生成skill".
Scan an experiment repo and generate a complete paper outline (H1/H2/H3) with user approval checkpoints at each level, then generate body text with evidence annotations, citations, and bilingual output. Python ML repos. 扫描实验仓库，逐级生成论文大纲（H1/H2/H3），每级用户确认后推进， 然后生成带证据标注、引用和双语输出的正文文本。