By orban
Hierarchical AGENTS.md infrastructure for AI-navigable codebases. Create CLAUDE.md/AGENTS.md files that help AI agents navigate your codebase like senior engineers.
npx claudepluginhub orban/intent-layer --plugin intent-layer> 4 specialized subagents. Markdown files with YAML frontmatter (`description`, `capabilities`) that Claude auto-invokes for analysis tasks.
Find drift between Intent Layer nodes and current code state. Use for quarterly maintenance, post-merge review, or when hook flags accumulate.
Identifies Intent Layer nodes affected by code changes for targeted validation
Analyze a directory and propose AGENTS.md content. Use when setting up new Intent Layer nodes or when a directory is flagged as needing coverage.
Deep validation that an Intent Layer node accurately reflects its codebase. Use after creating/updating nodes or as part of PR review.
End-of-cycle learning capture and triage. Run after completing a feature, fixing a bug, or finishing any significant work. Reviews pending learnings, analyzes conversation for undocumented insights, and integrates into the Intent Layer with appropriate scope (global workflow vs local code).
Quick health check for Intent Layer - validates nodes, checks staleness, reports coverage gaps
Use when maintaining an existing Intent Layer, during quarterly reviews, after significant code changes, when something confused you, or when user asks to audit/update CLAUDE.md or AGENTS.md files.
Get oriented in an unfamiliar codebase using its Intent Layer. Use when joining a new project, exploring a codebase for the first time, or helping someone understand "where do I start?" questions.
Query the Intent Layer to answer questions about the codebase. Use when asking "what owns X?", "where should I put Y?", "what constraints apply to Z?", or navigating an unfamiliar codebase using its AGENTS.md hierarchy.
Set up hierarchical Intent Layer (AGENTS.md files) for codebases. Use when initializing a new project, adding context infrastructure to an existing repo, user asks to set up AGENTS.md, add intent layer, make agents understand the codebase, or scaffolding AI-friendly project documentation.
Interactive review of pending mistake reports from the learning loop. Walk through each report with the user, accepting/rejecting/discarding as appropriate.
Complete developer workflow toolkit. Includes 34 reference skills, 34 specialized agents, and 21 slash commands covering TDD, debugging, code review, architecture, documentation, refactoring, security, testing, git workflows, API design, performance, UI/UX design, plugin development, and incident response. Full SDLC coverage with MCP integrations.
Executes bash commands
Hook triggers when Bash tool is used
Modifies files
Hook triggers on file write and edit operations
Uses power tools
Uses Bash, Write, or Edit tools
Claude + Obsidian knowledge companion. Sets up a persistent, compounding wiki vault. Covers memory management, session notetaking, knowledge organization, and agent context across projects. Based on Andrej Karpathy's LLM Wiki pattern. Optional DragonScale Memory extension adds hierarchical log folds, deterministic page addresses, embedding-based semantic tiling lint, and boundary-first autoresearch topic selection.
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
AI-powered wiki generator for code repositories. Generates comprehensive, Mermaid-rich documentation with dark-mode VitePress sites, onboarding guides, deep research, and source citations. Inspired by OpenDeepWiki and deepwiki-open.
Comprehensive C4 architecture documentation workflow with bottom-up code analysis, component synthesis, container mapping, and context diagram generation
draw.io diagram creation, editing, and review. Use for .drawio XML editing, PNG conversion, layout adjustment, and AWS icon usage.
Uses Bash, Write, or Edit tools