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Found 172 Skills
Use after 2 consecutive failed attempts at solving a problem - STOP guessing and research documentation, codebase, and online resources before resuming
Load PROACTIVELY when starting work on an unfamiliar codebase or setting up a new project. Use when user says "help me understand this codebase", "onboard me", "what does this project do", "set up my environment", or "map the architecture". Covers codebase structure analysis, architecture mapping, dependency auditing, convention and pattern detection, developer environment setup, and documentation of findings for rapid productive contribution.
Automated brownfield codebase analysis. Detects project type, frameworks, dependencies, architecture patterns, and generates comprehensive project profile. Essential for Conductor integration and onboarding existing projects.
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.
Analyze codebases from the bottom up and generate a hierarchical README.md document tree. Start analysis from leaf directories, generate README.md files for each directory containing one-sentence descriptions of files, classes, and functions, and summarize layer by layer upwards to form a complete codebase documentation system. Supports state persistence and resumable analysis, suitable for scenarios such as understanding new projects, generating technical documentation, and analyzing code structures. Use this skill when you need to understand codebase structures, analyze function implementations, or generate code documentation.
Universal AI context generator that compiles codebase maps, wiki knowledge bases, and MCP tools to save thousands of tokens per AI conversation.
Analyzes project bounded contexts, extracts business rules and domain knowledge, writes ai-context/features/<context>.md files, and produces a teach-report.md with documentation coverage metrics. Trigger: /codebase-teach, teach codebase, extract domain knowledge, update feature docs.
Search and analyze a codebase with the `codemap` CLI: semantic search, symbol lookup, dependency tracing, file summaries, importance ranking, coupling metrics, and cycle detection. Use when the user wants architecture-aware code discovery rather than plain text search.
Use when you need a fast, reliable architecture or impact view in a large unfamiliar repo, especially under time pressure or tight context budgets where manual grep or folder inference would be risky.
Design and review Rails applications using layered architecture principles from "Layered Design for Ruby on Rails Applications". Use when analyzing Rails codebases, reviewing PRs for architecture violations, planning feature implementations, or implementing patterns like authorization, view components, or AI integration. Triggers on "layered design", "architecture layers", "abstraction", "specification test", "layer violation", "extract service", "fat controller", "god object".
Load codebase context by analyzing structure and README. Use when starting a new session.
Construir a documentação técnica em docs/ e o inventário npm/Composer em .specsfy/PACKAGES.md. Use ao documentar sistemas ou após implementações.