A reusable set of low-token AI agent instruction documents ("docs") for AI-enabled IDEs.

Docs are markdown files containing distilled technical, style, architectural, and philosophical knowledge. They are designed to be:

• Low token count - Optimized for efficient AI agent consumption
• Comprehensive - Complete coverage of essential information
• Reusable - Shareable across projects and teams

Inspired by The Matrix (1999), where Neo says "I know kung fu" after having knowledge uploaded directly into his brain. In that scene, Neo instantly gains complete knowledge and skills without needing to learn through practice - the knowledge is simply "there" when needed. Similarly, agent-docs creates comprehensive documentation "programs" that can be referenced by AI agents, giving them instant, complete knowledge about libraries, frameworks, and tools. Just as Neo could access kung fu knowledge instantly, AI agents can reference these docs to immediately understand APIs, patterns, best practices, and architectural decisions without needing to search or learn incrementally. The docs serve as the "knowledge upload" that makes the AI agent instantly capable with any technology.

1. Clone the repository (optional, for local development of agent-docs itself):

   git clone https://github.com/starch-uk/agent-docs.git
   cd agent-docs
   pnpm install

2. Add to your project:

   You can add agent-docs to your project in several ways:

   • As an npm dependency (recommended): Add agent-docs to your package.json and install with your package manager (e.g. pnpm install, npm install, or yarn add).

     When installed this way, a postinstall script runs in the consuming project:

     • If your project does not already have a docs/ directory, the script will copy this package's docs/ directory to your project's root directory
     • If your project already has a docs/ directory, the script does nothing, and your existing docs layout is left unchanged.

   • As a git submodule:
     git submodule add https://github.com/starch-uk/agent-docs.git

   • As a manual copy (if you want explicit control):

     • Copy the docs/ directory from the agent-docs package to your project root

3. Configure your IDE agent:

   Update your IDE agent rules (e.g., Cursor's .cursor/rules/ or similar) to reference the linked docs using @filename syntax or relative paths:

   Reference documentation from ./docs/ when needed:
   - @PMD.md for PMD rules and configuration
   - @XPATH31.md for XPath 3.1 syntax
   - @ESLINT.md for ESLint configuration
   

The docs/ directory contains generated documentation files. Each doc follows a structured format optimized for AI agent consumption:

• A4DRULES.md - Agentforce Rules Reference
• A4DWORKFLOWS.md - Agentforce Workflows reference
• APEXANNOTATIONS.md - Apex annotations reference
• APEXDOC.md - ApexDoc documentation tool reference
• AURACOMPONENTS.md - Aura Component reference
• CML.md - Constraint Modeling Language (CML) reference for Salesforce Revenue Cloud Product Configurator
• CODEANALYZER.md - Salesforce Code Analyzer configuration (includes CLI Commands, CPD Engine, Flow Scanner Engine, Regex Engine, RetireJS Engine, and MCP tools)
• CONTEXTDEFINITIONS.md - Salesforce Context Definitions reference for Dynamic Revenue Orchestrator (DRO)
• ESLINT.md - ESLint configuration and rules reference
• ESLINTJSDOC.md - ESLint JSDoc plugin reference
• FIELDSERVICE.md - Salesforce Field Service reference
• GRAPHBINARY.md - Graph Binary format reference
• GRAPHENGINE.md - Graph Engine reference
• GRAPHML.md - GraphML format reference
• GRAPHSON.md - GraphSON format reference
• GREMLIN.md - Gremlin query language reference
• GRYO.md - Gryo binary format reference
• HUSKY.md - Husky git hooks tool reference
• JEST.md - Jest testing framework reference
• JORJE.md - Jorje Apex parser reference
• JSDOC.md - JSDoc documentation generator reference
• LIGHTNINGBASECOMPONENTS.md - Lightning Base Components reference
• LWCHTMLTEMPLATES.md - LWC HTML Templates reference
• PMD.md - PMD static analysis tool reference (includes Apex AST reference and suppressing warnings)
• PNPM.md - pnpm package manager reference
• PRETTIER.md - Prettier code formatter reference
• PRETTIERAPEX.md - Prettier Apex plugin reference
• REVENUETRANSACTIONMANAGEMENT.md - Salesforce Revenue Cloud Transaction Management reference
• TINKERPOP.md - Apache TinkerPop graph computing framework reference
• VITEST.md - Vitest testing framework reference
• XPATH31.md - XPath 3.1 query language reference

To create new documentation files:

1. Create a new markdown file in the docs/ directory following the naming convention:

   • Uppercase, no spaces, no dots
   • Version numbers become part of the name (e.g., "XPath 3.1" → XPATH31.md)
   • Remove redundant words like "plugin" when they appear between other words (e.g., "prettier-plugin-apex" → PRETTIERAPEX.md)

2. Follow the structured format optimized for AI agent consumption:

   • Overview section with brief context
   • Core Concepts / Key Features (bulleted lists)
   • Configuration / Setup (tables when possible)
   • Usage / Examples (minimal, essential code only)
   • API Reference (condensed, often in tables)
   • Patterns & Best Practices
   • Important Notes (critical gotchas, limitations)

3. Apply optimization guidelines:

   • Keep token count low - be terse but precise
   • Use bullet points over prose for scannability
   • Prefer tables for structured data (config options, APIs, properties)
   • Include code snippets only when essential
   • Cross-reference related docs using [Name](FILENAME.md) format
   • Include a document in a prompt or rule with @FILENAME.md

This repository includes AI Agent Guidance files in .cursor/plans/ that provide structured instructions and workflows for AI coding assistants (like Cursor's Agent). These plan files document processes, best practices, and step-by-step workflows for various tasks, making them accessible to AI agents through the .cursor/plans/ directory structure.

When AI agents need guidance on how to perform specific tasks or follow certain workflows, they can reference these plan files to understand the expected process and provide accurate assistance. Key plan files include:

• VERSIONING.md - Semantic versioning rules and workflows for documentation files
• OPTIMISE.md - Strategies and best practices for maintaining low token counts and efficient documentation formats

These plans are used by AI-powered IDEs to optimize docs and maintain consistency across the documentation set.

See CONTRIBUTING.md for guidelines on contributing to this project.

This project is licensed under the MIT License - see LICENSE.md for details.

For security concerns, please see SECURITY.md.

• Issues: GitHub Issues
• Repository: https://github.com/starch-uk/agent-docs