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sdlc-reports
This skill automates the generation of structured SDLC reports for software teams. It pulls data from project artifacts to create iteration summaries, executive briefings, and metrics dashboards. The templates are detailed and include sections for completed work, blockers, and retrospective items, saving managers hours of manual compilation.
creating-opencode-agents
Use when creating OpenCode agents - provides markdown format with YAML frontmatter, mode/tools/permission configuration, and best practices for specialized AI assistants
moai-platform-supabase
This skill provides detailed guidance on using Supabase for full-stack applications, covering PostgreSQL 16, pgvector for AI embeddings, Row-Level Security for multi-tenancy, real-time subscriptions, Edge Functions, and storage. It includes practical code examples for common patterns and references to modular documentation.
agent-council
A tool that runs multiple AI agents in parallel to collect diverse perspectives on a question, then synthesizes the results. It uses a job-based system with polling capabilities and handles both one-shot and long-running queries. Requires Node.js and works in both terminal and host-agent contexts.
spring-data-neo4j
A comprehensive and practical guide to Spring Data Neo4j integration, offering clear setup instructions, entity mapping patterns, repository implementations, and testing strategies for graph database development.
chunking-strategy
Provides a structured guide to implement document chunking for RAG systems, covering five strategies from basic fixed-size to advanced semantic methods. Includes concrete Python examples, performance metrics, and implementation steps for different document types.
gtars
Gtars is a Rust toolkit for genomic interval analysis with Python bindings. It handles BED files, overlap detection, coverage tracks, and tokenization for ML models. The documentation provides clear examples for common workflows like peak analysis and coverage generation. It integrates with the geniml package for machine learning applications.
integrating-stripe-webhooks
Provides specific solutions for Stripe webhook implementation problems, focusing on raw body parsing across frameworks and fixing TypeScript type issues with subscription data. Includes framework examples for Fastify, Express, and FastAPI, plus common error patterns and debugging steps.
unity-uitoolkit
Provides guidance for Unity UI Toolkit development including UXML structure, USS styling patterns, and C# VisualElement manipulation. Offers concrete code examples for common tasks like editor window setup, class toggling, and data binding. Helps developers follow Unity's recommended UI Toolkit practices.
game-developer
A specialized agent for game development tasks covering Unity, Unreal, Godot, and web frameworks. Provides structured workflows for architecture, graphics, physics, AI, and networking with specific performance targets like 60 FPS and load times under 3 seconds.
geniml
A Python package for applying machine learning to genomic interval data from BED files. It provides tools for learning embeddings of genomic regions (Region2Vec), single cells (scEmbed), and metadata (BEDspace), building consensus peaks, and enabling similarity search. It integrates with common bioinformatics workflows like scanpy for scATAC-seq analysis.
latchbio-integration
Provides guidance for building and deploying bioinformatics workflows on the Latch platform. Covers workflow creation with Python decorators, cloud data management using LatchFile/LatchDir, resource configuration for tasks, and integration of existing Nextflow/Snakemake pipelines. Includes practical examples for RNA-seq analysis and GPU-accelerated workflows.
error-handling
A comprehensive error handling framework for AI assistants that prioritizes user experience by auto-fixing common errors silently and translating technical issues into actionable guidance.
session-recovery
A well-designed skill for maintaining AI assistant context across sessions and IDEs, enabling seamless project continuation with comprehensive memory management and smart summarization.
tcga-bulk-data-preprocessing-with-omicverse
This skill guides users through preprocessing TCGA bulk RNA-seq data using the omicverse Python package. It provides step-by-step instructions for loading sample sheets, expression archives, and clinical data, then performing survival analysis and exporting annotated AnnData files for downstream analysis.
prompt-engineer
A structured Python script that automates prompt optimization through a 6-step workflow. It starts with triage to determine scope (single prompt, ecosystem, greenfield, or problem), then executes specific optimization steps. The skill enforces immediate execution without analysis paralysis.
spring-ai-mcp-server-patterns
Comprehensive implementation guide for building MCP servers with Spring AI, offering enterprise-grade patterns, clear examples, and production-ready configurations for Java developers.
extracting-hecras-results
A well-structured documentation skill that effectively guides users to comprehensive HEC-RAS result extraction resources, providing clear navigation to authoritative sources and practical workflows.
maintenance
Automatically cleans and organizes project files like Plans.md and session logs based on configurable thresholds. Uses LLM evaluation to suggest cleanups and moves old tasks to archives. Helps prevent file bloat in development workflows.
analyzing-financial-statements
This skill calculates key financial ratios and metrics from financial statement data for investment analysis
pyopenms
PyOpenMS provides Python bindings to OpenMS for mass spectrometry data analysis. It handles LC-MS/MS proteomics and metabolomics workflows including file I/O for formats like mzML and mzXML, signal processing, feature detection, peptide identification, and quantitative analysis. The documentation includes clear examples for common tasks.
creating-cursor-rules-skill
Provides detailed guidance for creating effective Cursor IDE rule files. Covers rule anatomy, organization strategies, and best practices with concrete examples. Helps teams document project-specific conventions for AI assistants to follow.
project-awareness
A comprehensive project context awareness skill that provides detailed project state detection, AIWG framework integration, and actionable recommendations for development teams.
gen-env
A comprehensive solution for running multiple isolated project instances locally with excellent documentation covering port allocation, data isolation, browser state separation, and cleanup patterns.