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distributed-tracing
This skill provides concrete instructions for implementing distributed tracing with Jaeger and Tempo. It includes deployment guides for Kubernetes and Docker, instrumentation examples for Python, Node.js, and Go, and practical troubleshooting advice. The content covers sampling strategies, context propagation, and trace analysis queries.
at-dispatch-v2
Converts PyTorch ATen kernel dispatch macros from legacy AT_DISPATCH format to the newer AT_DISPATCH_V2 API. Provides clear mapping tables, step-by-step instructions, and handles edge cases like multi-line lambdas with internal commas. Focuses on accurate syntax transformation without compilation testing.
git-advanced-workflows
This skill provides detailed instructions for advanced Git operations like interactive rebase, cherry-picking, bisect, worktrees, and reflog. It includes concrete workflows for cleaning commit history, applying hotfixes, finding bugs, and recovering from mistakes. The documentation offers specific command examples and best practices for real-world scenarios.
repomix
Package entire code repositories into single AI-friendly files using Repomix. Capabilities include pack codebases with customizable include/exclude patterns, generate multiple output formats (XML, Markdown, plain text), preserve file structure and context, optimize for AI consumption with token counting, filter by file types and directories, add custom headers and summaries. Use when packaging codebases for AI analysis, creating repository snapshots for LLM context, analyzing third-party libraries, preparing for security audits, generating documentation context, or evaluating unfamiliar codebases.
flowio
FlowIO parses FCS files (versions 2.0-3.1) used in flow cytometry. It extracts event data as NumPy arrays, reads metadata and channel information, and can convert data to CSV or DataFrame formats. The skill handles problematic files with offset errors and supports creating new FCS files from processed data.
scvi-tools
This skill provides access to scvi-tools, a Python framework for probabilistic modeling of single-cell genomics data. It handles RNA-seq, ATAC-seq, multimodal integration, and spatial transcriptomics through variational autoencoders. The skill includes detailed workflows, code examples, and best practices for data preprocessing, model training, and downstream analysis.
shopify-debugging
Complete debugging and troubleshooting guide for Shopify including Liquid errors, theme preview debugging, API error handling, JavaScript console debugging, network request inspection, cart issues, checkout problems, and common error codes. Use when debugging Liquid syntax errors, troubleshooting theme rendering issues, fixing API errors, debugging JavaScript, investigating cart problems, or resolving webhook failures.
drugbank-database
Access and analyze comprehensive drug information from the DrugBank database including drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. This skill should be used when working with pharmaceutical data, drug discovery research, pharmacology studies, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task requiring detailed drug and drug target information from DrugBank.
moai-lang-go
This skill provides detailed guidance for Go 1.23+ development, covering Fiber, Gin, GORM, and concurrency patterns. It includes practical code examples for building REST APIs, CLI tools, and microservices, along with Docker deployment and integration testing setups.
frontend-patterns
Provides concrete patterns for building frontends with Next.js App Router, Clerk authentication, shadcn/ui components, and PostHog analytics. Includes clear examples for server/client components, protected routes, form validation, and event tracking. Offers accessibility and responsive design checklists.
machine-learning-engineer
This skill provides structured guidance for deploying machine learning models to production. It covers model optimization, serving infrastructure setup, performance tuning, and monitoring. The checklist and workflow help engineers systematically address latency, throughput, and reliability requirements for real-world ML systems.
claude-code-skills
Provides a detailed reference for creating and organizing Claude Code skills, including folder structure, SKILL.md templates, progressive disclosure patterns, and hook implementation. It's essentially a meta-skill for skill developers.
qa-testing-strategy
Provides a risk-based testing framework covering unit, integration, contract, E2E, performance, and security testing. Includes shift-left gates, flake management with SLOs, CI economics with time budgets, and observability-first debugging workflows. Offers concrete templates for runbooks and checklists.
ai-ml-data-science
This skill provides structured workflows for end-to-end data science projects, from problem framing to production deployment. It includes EDA, feature engineering with feature stores, model training with LightGBM/scikit-learn, and SQL transformations via SQLMesh. Focuses on MLOps practices like drift monitoring and automated retraining.
xlsx
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
skill-installer
This skill installs Codex skills from GitHub repositories, supporting both a curated list and custom repo paths. It handles public and private repos with fallback methods, validates installations, and provides clear status feedback. The tool is essential for managing the Codex skill ecosystem.
deploy
This skill bundles three related tasks: setting up Vercel/Netlify deployment, integrating Google/Vercel Analytics, and performing a pre-flight environment health check. It provides clear, step-by-step guides for each task, triggered by specific Japanese or English phrases.
spring-boot-event-driven-patterns
A comprehensive and practical guide for implementing Event-Driven Architecture in Spring Boot, covering core patterns with clear examples and modern dependencies.
artifacts-builder
A toolset for creating complex Claude.ai HTML artifacts using React, TypeScript, Tailwind CSS, and shadcn/ui. It provides scripts to initialize projects and bundle them into single HTML files suitable for sharing as artifacts. It includes 40+ pre-configured UI components and handles Node version compatibility.
validate-report-file
Validates agent-generated report files for required sections and formatting. Checks for 5 specific sections, validates header metadata and status consistency, and returns structured JSON results. Useful for automated quality gates in report generation pipelines.
project-health-check
Provides a structured framework for evaluating individual project health across six dimensions (schedule, cost, scope, quality, risk, stakeholder). Includes specific metrics, assessment questions, and a weighted scoring system. Generates detailed reports with root cause analysis and prioritized recommendations. Has clear anti-rationalization rules and escalation criteria for critical issues.
continuity-ledger
Creates external ledger files to preserve session state across Claude's /clear operations. Uses markdown files in .ring/ledgers/ to track goals, decisions, progress, and open questions. Enables resuming complex multi-phase work after clearing context without losing understanding.
gwas-database
This skill provides structured access to the NHGRI-EBI GWAS Catalog for querying SNP-trait associations. It includes detailed API examples, Python code snippets for data retrieval, and clear guidance on search patterns for genetic variants, diseases, and genes. The documentation covers practical workflows for genetic epidemiology research.
AgentDB Memory Patterns
Provides memory management for AI agents using AgentDB's vector storage. Offers session memory, long-term pattern storage, and integrates with ReasoningBank for context synthesis. Includes CLI tools for database management, learning plugins, and performance benchmarks showing significant speed improvements over traditional approaches.