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Found 126 Skills
Automated brownfield codebase analysis. Detects project type, frameworks, dependencies, architecture patterns, and generates comprehensive project profile. Essential for Conductor integration and onboarding existing projects.
Analyze a codebase to produce an interactive knowledge graph for understanding architecture, components, and relationships
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.
Extract business domain knowledge from a codebase and generate an interactive domain flow graph. Works standalone (lightweight scan) or derives from an existing /understand knowledge graph.
Universal AI context generator that compiles codebase maps, wiki knowledge bases, and MCP tools to save thousands of tokens per AI conversation.
Provides autonomous project pattern learning by analyzing the codebase to discover development conventions, architectural patterns, and coding standards, then generates project rule files in .claude/rules/. Use when user asks to "learn from project", "extract project rules", "analyze codebase conventions", "discover project patterns", or wants to auto-generate Claude Code rules for the current project.
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.
Initialize a new repository with AGENTS.md
Technical due diligence for M&A, investment, or acquisition. Reads a target company's codebase and generates a comprehensive tech DD report with architecture assessment, tech debt quantification, scalability analysis, security posture, team capability inference, build system quality, test coverage, deployment maturity, and open source license risks. Outputs tech-dd-report.md formatted like a real investment memo with risk ratings, remediation costs, and go/no-go recommendation.
[Hyper] Create or refactor a project README.md by carefully reading the codebase. Detects project shape (CLI, library, web app, monorepo, plugin, framework, docs site, service), entry points, scripts, configuration, license, and existing docs, then produces a structured README in the project's primary documentation language. Use when the user wants a new README, a refactor of a stale README, or a section update grounded in the actual code.
Reverse-engineer any codebase into a complete Product Requirements Document (PRD). Analyzes routes, components, state management, API integrations, and user interactions to produce business-readable documentation detailed enough for engineers or AI agents to fully reconstruct every page and endpoint. Works with frontend frameworks (React, Vue, Angular, Svelte, Next.js, Nuxt), backend frameworks (NestJS, Django, Express, FastAPI), and fullstack applications. Trigger when users mention: generate PRD, reverse-engineer requirements, code to documentation, extract product specs from code, document page logic, analyze page fields and interactions, create a functional inventory, write requirements from an existing codebase, document API endpoints, or analyze backend routes.
This skill should be used when analyzing codebases, understanding architecture, or when "analyze", "investigate", "explore code", or "understand architecture" are mentioned.