Intent Layer
0The Intent Layer provides a structured way for AI agents to navigate codebases by detecting project states and routing workflows accordingly. It's designed for developers working with complex codebases.
000Updated 1mo ago
Development
Quick InstallView Source
$ npx skills add https://github.com/crafter-station/skills --skill intent-layerOverview
About This Skill
Intent Layer enables AI agents to understand and navigate complex codebases through structured AGENTS.md files, allowing agents to detect project states and route workflows intelligently. Built on The Intent Layer framework by Tyler Brandt, it bridges the gap between agent capabilities and codebase complexity. The skill works with Claude Code, Cursor, Copilot, and 10+ other agents. By establishing clear context and intent markers, Intent Layer helps agents operate like senior engineers familiar with your project structure. This is essential for teams using AI agents in large, multi-faceted codebases where context and intelligent routing determine agent effectiveness.
How to Use
Install via npx and add to your skills.
Use Cases
Detect the state of a project through scripts
Route projects based on completeness
Manage complex codebases with hierarchical structures
Tags
Setup & Installation
Quick Install
Terminal
$ npx skills add https://github.com/crafter-station/skills --skill intent-layerAlternative Install (Git Clone)
git clone https://github.com/crafter-station/skillsRequirements
- Claude Code or compatible AI agent
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Analyze the [PROJECT_NAME] codebase and identify the current state of the [MODULE_NAME] module. Determine the next logical step in the workflow based on the detected state and suggest the most efficient path forward. Return the analysis in a structured format with: 1) Current state assessment, 2) Detected dependencies or blockers, 3) Recommended next action, and 4) Required inputs or outputs for the next step.
Example Output
## Intent Layer Analysis for `user-auth` Module ### Current State Assessment The `user-auth` module is in a **partially implemented** state. Core authentication logic (login, logout, session management) exists, but OAuth integration is missing. The module depends on a `database` service that is currently offline. ### Detected Dependencies/Blockers - **Missing OAuth2 Provider**: No integration with GitHub, Google, or custom OAuth2 flows. - **Database Service**: `auth-db` container is in a `CrashLoopBackOff` state (logs indicate connection pool exhaustion). - **Configuration Drift**: Environment variables for JWT secret and token expiry are hardcoded in `config.js` instead of using secrets management. ### Recommended Next Action **Priority: High** Implement OAuth2 integration using the `passport` library. Start with GitHub OAuth as a proof of concept, then abstract to a pluggable provider system. This will require: 1. Installing `passport`, `passport-github2`, and `passport-jwt` packages. 2. Updating `routes/auth.js` to include OAuth routes. 3. Refactoring `config.js` to pull secrets from environment variables. ### Required Inputs/Outputs **Inputs Needed:** - GitHub OAuth app credentials (Client ID, Client Secret) - Updated `auth-db` service configuration (connection pool size, retry logic) **Outputs Expected:** - OAuth login endpoints (`/auth/github`, `/auth/github/callback`) - Refactored `config.js` with secrets management - Updated `auth-db` service in `Running` state --- *Next step: Would you like me to generate the code changes for the OAuth integration?*
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