diagram
Maintain and synchronize Unified Impact Diagrams using the Diagram Driven Development (DDD) methodology to connect technical architecture with user value.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
383 skills found
Maintain and synchronize Unified Impact Diagrams using the Diagram Driven Development (DDD) methodology to connect technical architecture with user value.
AI-powered documentation engine that automatically generates C4 architecture diagrams, technical specs, and codebase analysis from any source code directory.
Development guide for creating and publishing TPMJS tools using the blocks CLI, AI SDK v6, and npm registry patterns.
Comprehensive reference for GrepAI configuration, detailing the .grepai/config.yaml schema, embedder settings, storage backends, and optimization parameters.
Proactive context window management for AI agents via intelligent token monitoring, snapshot creation, and selective state rehydration to maintain continuity during long sessions.
Automates the creation of draft GitHub pull requests using conventional commit standards and strict validation workflows.
Intelligent tool selector for code search. Routes queries between semantic (claudemem) and native tools (Grep/Glob) to optimize efficiency, token usage, and search accuracy.
Automated security auditing for project dependencies. Scans package files (npm, pip, maven, etc.) for vulnerabilities, CVEs, and license issues, offering automated fix suggestions and integration for secure deployment workflows.
Automated static code review for Arduino, ESP32, and RP2040 projects. Identifies memory safety issues, structure improvements, and best practices to enhance firmware quality and reliability.
Enforces disciplined Test-Driven Development (TDD) by requiring a failing test before implementation, ensuring code reliability and preventing premature over-engineering.
Analyze project codebases to generate architecture documentation, coding standards, and development practices for AI onboarding.
Evidence-based debugging for Python, Node.js, and Java applications using runtime execution traces and diagnostic MCP tools.