Advanced Modular Library Design
Design modular TypeScript libraries using HexDI principles: compile-time dependency validation, feature-first organization, and clean API boundaries.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
181 skills found
Design modular TypeScript libraries using HexDI principles: compile-time dependency validation, feature-first organization, and clean API boundaries.
Apply reality-first coding standards: intentional naming, focused functions, guard clauses, and deterministic side effects, with no speculative features.
A testing fixture for validating AI agent skill configurations and detecting rule violations.
Interactive debugging workflow for Ruby test suites using the debug gem, featuring step execution, system state inspection, and root cause analysis.
Architects enterprise AI agents from structured specs, generating production-ready code, data flow diagrams, and platform-specific logic for ServiceNow, Salesforce, and Snowflake.
Guide for implementing a new AI coding agent analyzer in Splitrail to track token usage, costs, and performance metrics.
Expert advisor for implementing Anthropic's structured outputs. Choose between JSON mode and strict tool use for guaranteed schema compliance and validated agentic workflows.
A guide for building high-quality MCP (Model Context Protocol) servers in Python or TypeScript to integrate external APIs and services into LLM workflows.
Official Sunhat toolkit for end-to-end TRON smart contract lifecycle: development, compilation, cross-framework testing, and deployment.
A framework for applying Test-Driven Development to process documentation, ensuring agent reliability by using pressure scenarios to identify and patch rationalization loopholes.
Implement professional-grade test automation strategy, manage test pyramids, detect anti-patterns, and integrate with CI/CD for resilient, fast, and high-quality software testing.
Techniques for writing effective fuzzing harnesses across languages. Use when creating new fuzz targets or improving existing harness code.