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Found 7,600 Skills
JaCoCo Java code coverage tool USE WHEN: user mentions "JaCoCo", "Java coverage", "code coverage", asks about "coverage threshold", "jacoco-maven-plugin", "coverage report", "LINE coverage", "BRANCH coverage" DO NOT USE FOR: JavaScript/TypeScript coverage - use Vitest skill, SonarQube analysis - use `sonarqube` skill, test execution - use testing skills
Google Cloud Platform SDK integration. Cloud Functions, Firestore, Cloud Storage, Pub/Sub, BigQuery, and Cloud Run. Node.js and Python client libraries. USE WHEN: user mentions "GCP", "Google Cloud", "Cloud Functions", "Firestore", "Cloud Storage", "Pub/Sub", "BigQuery", "Cloud Run", "Firebase" DO NOT USE FOR: AWS services - use `aws`; Azure services - use `azure`; Firebase Auth - use auth skills
structlog - structured logging library for Python with native JSON support, context binding, and processor pipeline. Integrates with FastAPI, Django, and standard logging module. USE WHEN: user mentions "structlog", "python structured logging", "context binding", asks about "JSON logging python", "fastapi logging", "django structured logging" DO NOT USE FOR: Standard Python logging - use `python-logging` instead, Node.js logging - use `pino` or `winston`, Java logging - use `slf4j` or `logback` instead
Java logging with SLF4J facade, Logback, and Log4j2 implementations. Covers configuration, log levels, structured logging, async logging, and production best practices for Spring Boot applications. USE WHEN: user mentions "java logging", "spring boot logging", "slf4j setup", asks about "how to log in java", "logback vs log4j2", "java logging best practices" DO NOT USE FOR: Node.js logging - use `nodejs-logging` instead, Python logging - use `python-logging`, Kotlin-specific logging - similar but has nuances
Cloudflare D1 migration workflow: generate with Drizzle, inspect SQL for gotchas, apply to local and remote, fix stuck migrations, handle partial failures. Use when running migrations, fixing migration errors, or setting up D1 schemas.
Git troubleshooting techniques including recovering lost commits, fixing merge conflicts, resolving detached HEAD, and diagnosing repository issues. Use when user encounters git errors or needs to recover from mistakes.
Advanced git operations including complex rebase strategies, interactive staging, commit surgery, and history manipulation. Use when user needs to perform complex git operations like rewriting history or advanced merging.
Complete guide for building MCP servers with FastMCP 3.0 - tools, resources, authentication, providers, middleware, and deployment. Use when creating Python MCP servers or integrating AI models with external tools and data.
Optimize command outputs with RTK (Rust Token Killer) for 70% token reduction
Perform RFM (Recency, Frequency, Monetary) customer segmentation analysis on e-commerce data. Use when you need to analyze customer value, identify VIP customers, or create marketing segments. Automatically cleans data, calculates RFM metrics, applies K-means clustering, and generates visualization reports with Chinese language support.
Write and audit Python code comments using antirez's 9-type taxonomy. Two modes - write (add/improve comments in code) and audit (classify and assess existing comments with structured report). Use when users request comment improvements, docstring additions, comment quality reviews, or documentation audits. Applies systematic comment classification with Python-specific mapping (docstrings, inline comments, type hints).
Guided Shape Up workflow for taking projects from idea to working software. Orchestrates the /shaping and /breadboarding skills through a structured process: Frame, Shape, Breadboard, Slice, Build. Works for both greenfield (0-1) and existing projects. Use when: starting a new project or feature, planning a significant change to an existing codebase, user says "shape this", "let's shape", "shape up", or wants to go from idea to implementation with structured problem/solution separation. Proactively guides each phase and suggests next steps.