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Found 144 Skills
This skill helps users get started with existing (brownfield) projects by scanning the codebase, documenting structure and purpose, analyzing architecture and technical stack, identifying design flaws, suggesting improvements for testing and CI/CD pipelines, and generating AI agent constitution files (AGENTS.md) with project-specific context, coding principles, and UI/UX guidelines.
Create a living specification (Spec) or plan for a feature by analyzing requirements and codebase
Generate architecture diagrams as .excalidraw files from codebase analysis, with optional PNG/SVG export. Use when the user asks to create architecture diagrams, system diagrams, data flow diagrams, parameter threading traces, call chain visualizations, visualize codebase structure, generate excalidraw files, export excalidraw diagrams to PNG or SVG, or convert .excalidraw files to image formats.
Create or update a project constitution with governance rules. Uses discovery-based approach to generate project-specific rules.
Access AI-generated documentation and insights for GitHub repositories via DeepWiki. This skill should be used when exploring unfamiliar codebases, understanding repository architecture, finding implementation patterns, or asking questions about how a GitHub project works. Supports any public GitHub repository.
Generate a persistent .nexus-map/ knowledge base that lets any AI session instantly understand a codebase's architecture, systems, dependencies, and change hotspots. Use when starting work on an unfamiliar repository, onboarding with AI-assisted context, preparing for a major refactoring initiative, or enabling reliable cold-start AI sessions across a team. Produces INDEX.md, systems.md, concept_model.json, git_forensics.md and more. Requires shell execution and Python 3.10+. For ad-hoc file queries or instant impact analysis during active development, use nexus-query instead.
Manages persistent Knowledge Graph for specifications. Caches agent discoveries and codebase analysis to remember findings across sessions. Validates task dependencies, stores patterns, components, and APIs to avoid redundant exploration. Use when: you need to cache analysis results, remember findings, reuse previous discoveries, look up what we found, spec-to-tasks needs to persist codebase analysis, task-implementation needs to validate contracts, or any command needs to query existing patterns/components/APIs.
Process large codebases (>100 files) using the Recursive Language Model pattern. Orchestrates parallel sub-agents to map-reduce across files without context rot. Use when: analyzing large repositories; auditing security or auth across many files; finding patterns across 50+ files; processing large log files or data dumps
Systematic refactoring of codebase components through a structured 3-phase process. Use when asked to refactor, restructure, or improve specific components, modules, or areas of code. Produces research documentation, change proposals with code samples, and test plans. Triggers on requests like "refactor the authentication module", "restructure the data layer", "improve the API handlers", or "clean up the payment service".
Generate OpenAPI documentation from source code. Analyzes repository to automatically discover API endpoints and create swagger.json and interactive HTML documentation. Use when generating API docs, creating OpenAPI specs, documenting REST APIs, or analyzing API endpoints.
This skill should be used when agents need to search codebases for text patterns or structural code patterns. Provides fast search using ripgrep for text and ast-grep for syntax-aware code search.
This skill should be used when user asks to "generate UML", "create sequence diagram", "生成时序图", "生成类图", "generate PlantUML", or discusses generating UML diagrams for new interfaces or API design.