@ai-coders/context

The Ultimate MCP for AI Agent Orchestration, Context Engineering, and Spec-Driven Development. Context engineering for AI now is stupidly simple.

Stop letting LLMs run on autopilot. PREVC is a universal process that improves AI output through 5 simple steps: Planning, Review, Execution, Validation, and Confirmation. Context-oriented. Spec-driven. No guesswork.

The Problem

Every AI coding tool invented its own way to organize context:

.cursor/rules/          # Cursor
.claude/                # Claude Code
.windsurf/rules/        # Windsurf
.github/agents/         # Copilot
.cline/                 # Cline
.agent/rules/           # Google Antigravity
.trae/rules/            # Trae AI
AGENTS.md               # Codex

Using multiple tools? Enjoy duplicating your rules, agents, and documentation across 8 different formats. Context fragmentation is real.

The Solution

One .context/ directory. Works everywhere.

.context/
├── docs/           # Your documentation (architecture, patterns, decisions)
├── agents/         # Agent playbooks (code-reviewer, feature-developer, etc.)
├── plans/          # Work plans linked to PREVC workflow
└── skills/         # On-demand expertise (commit-message, pr-review, etc.)

Export to any tool. Write once. Use anywhere. No boilerplate.

Youtube video

Connect with Us

Built by AI Coders Academy — Learn AI-assisted development and become a more productive developer.

AI Coders Academy — Courses and resources for AI-powered coding
YouTube Channel — Tutorials, demos, and best practices
Connect with Vini — Creator of @ai-coders/context

Why PREVC?

LLMs produce better results when they follow a structured process instead of generating code blindly. PREVC ensures:

Specifications before code — AI understands what to build before building it
Context awareness — Each phase has the right documentation and agent
Human checkpoints — Review and validate at each step, not just at the end
Reproducible quality — Same process, consistent results across projects

Usage

npx @ai-coders/context

That's it. The wizard detects what needs to be done.

PT-BR Tutorial https://www.youtube.com/watch?v=5BPrfZAModk

What it does

Creates documentation — Structured docs from your codebase (architecture, data flow, decisions)
Generates agent playbooks — 14 specialized AI agents (code-reviewer, bug-fixer, architect, etc.)
Manages workflows — PREVC process with scale detection, gates, and execution history
Provides skills — On-demand expertise (commit messages, PR reviews, security audits)
Syncs everywhere — Export to Cursor, Claude, Copilot, Windsurf, Cline, Codex, Antigravity, Trae, and more
Tracks execution — Step-level tracking with git integration for workflow phases
Keeps it updated — Detects code changes and suggests documentation updates

Quick Start

Install the MCP
Prompt to the agent:

init the context

This will setup the context and fill it according the the codebase
With the context ready prompt

plan [YOUR TASK HERE] using ai-context

After planned, prompt

start the workflow

That's it!

PREVC Workflow System

A universal 5-phase process designed to improve LLM output quality through structured, spec-driven development:

Phase	Name	Purpose
P	Planning	Define what to build. Gather requirements, write specs, identify scope. No code yet.
R	Review	Validate the approach. Architecture decisions, technical design, risk assessment.
E	Execution	Build it. Implementation follows the approved specs and design.
V	Validation	Verify it works. Tests, QA, code review against original specs.
C	Confirmation	Ship it. Documentation, deployment, stakeholder handoff.

The Problem with Autopilot AI

Most AI coding workflows look like this:

User: "Add authentication"
AI: *generates 500 lines of code*
User: "That's not what I wanted..."

PREVC fixes this:

P: What type of auth? OAuth, JWT, session? What providers?
R: Here's the architecture. Dependencies: X, Y. Risks: Z. Approve?
E: Implementing approved design...
V: All 15 tests pass. Security audit complete.
C: Deployed. Docs updated. Ready for review.

Documentation

User Guide — Complete usage guide

Scale-Adaptive Routing

The system automatically detects project scale and adjusts the workflow:

Scale	Phases	Use Case
QUICK	E → V	Bug fixes, small tweaks
SMALL	P → E → V	Simple features
MEDIUM	P → R → E → V	Regular features
LARGE	P → R → E → V → C	Complex systems, compliance

Requirements for the CLI

Node.js 20+
API key from a supported provider (for AI features)

If you are using throught MCP you don't need to setup an API key from a supported provider, your AI agent will use it's own LLM.

Supported Providers

Provider	Environment Variable
OpenRouter	`OPENROUTER_API_KEY`
OpenAI	`OPENAI_API_KEY`
Anthropic	`ANTHROPIC_API_KEY`
Google	`GOOGLE_API_KEY`

MCP Server Setup

This package includes an MCP (Model Context Protocol) server that provides AI coding assistants with powerful tools to analyze and document your codebase.

Quick Installation (v0.7.0+)

Use the new MCP Install command to automatically configure the MCP server:

npx @ai-coders/context mcp:install

This interactive command:

Detects installed AI tools on your system
Configures ai-context MCP server in each tool
Supports global (home directory) and local (project directory) installation
Merges with existing MCP configurations without overwriting
Includes dry-run mode to preview changes
Works with Claude Code, Cursor, Windsurf, Cline, Continue.dev, and more

Manual Configuration

Alternatively, manually configure for your preferred tool.

Antigravity

1. Access Raw Config

The visual interface only shows official partners, but the manual editing mode allows any local or remote executable.

Open the Agent panel (usually in the sidebar or Ctrl+L).
Click the options menu (three dots ...) or the settings icon.
Select Manage MCP Servers.
At the top of this screen, look for a discreet button or link named "View raw config" or "Edit JSON".

Note: If you cannot find the button in the UI, you can navigate directly through the file explorer and look for .idx/mcp.json or mcp_config.json in your workspace root.

2. Add Custom Server

You will see a JSON file. You must add a new entry inside the "mcpServers" object.

Here is the template to add a server (example using npx for a Node.js server or a local executable):

{
  "mcpServers": {
    "ai-context": {
      "command": "npx",
      "args": ["@ai-coders/context", "mcp"]
    }
  }
}

3. Restart the Connection

After saving the mcp.json file:

Return to the "Manage MCP Servers" panel.
Click the Refresh button or restart the Antigravity environment (Reload Window).
The new server should appear in the list with a status indicator (usually a green light if connected successfully).

Claude Code (CLI)

Add the MCP server using the Claude CLI:

claude mcp add ai-context -- npx @ai-coders/context mcp

Or configure manually in ~/.claude.json:

{
  "mcpServers": {
    "ai-context": {
      "command": "npx",
      "args": ["@ai-coders/context", "mcp"]
    }
  }
}

Claude Desktop

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS or %APPDATA%\Claude\claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "ai-context": {
      "command": "npx",
      "args": ["@ai-coders/context", "mcp"]
    }
  }
}

Cursor AI

Create .cursor/mcp.json in your project root:

{
  "mcpServers": {
    "ai-context": {
      "command": "npx",
      "args": ["@ai-coders/context", "mcp"]
    }
  }
}

Windsurf

Add to your Windsurf MCP config (~/.codeium/windsurf/mcp_config.json):

{
  "mcpServers": {
    "ai-context": {
      "command": "npx",
      "args": ["@ai-coders/context", "mcp"]
    }
  }
}

Zed Editor

Add to your Zed settings (~/.config/zed/settings.json):

{
  "context_servers": {
    "ai-context": {
      "command": {
        "path": "npx",
        "args": ["@ai-coders/context", "mcp"]
      }
    }
  }
}

Cline (VS Code Extension)

Configure in Cline settings (VS Code → Settings → Cline → MCP Servers):

{
  "mcpServers": {
    "ai-context": {
      "command": "npx",
      "args": ["@ai-coders/context", "mcp"]
    }
  }
}

Codex CLI

Add to your Codex CLI config (~/.codex/config.toml):

[mcp_servers.ai-context]
command = "npx"
args = ["--yes", "@ai-coders/context@latest", "mcp"]

Google Antigravity

Add to your Antigravity MCP config (~/.gemini/mcp_config.json):

{
  "mcpServers": {
    "ai-context": {
      "command": "npx",
      "args": ["@ai-coders/context", "mcp"]
    }
  }
}

Trae AI

Add to your Trae AI MCP config (Settings > MCP Servers):

{
  "mcpServers": {
    "ai-context": {
      "command": "npx",
      "args": ["@ai-coders/context", "mcp"]
    }
  }
}

Local Development

For local development, point directly to the built distribution:

{
  "mcpServers": {
    "ai-context-dev": {
      "command": "node",
      "args": ["/path/to/ai-coders-context/dist/index.js", "mcp"]
    }
  }
}

Available MCP Tools

Once configured, your AI assistant will have access to 9 gateway tools with action-based dispatching:

Gateway Tools (Primary Interface)

Gateway	Description	Actions
explore	File and code exploration	`read`, `list`, `analyze`, `search`, `getStructure`
context	Context scaffolding and semantic context	`check`, `init`, `fill`, `fillSingle`, `listToFill`, `getMap`, `buildSemantic`, `scaffoldPlan`
plan	Plan management and execution tracking	`link`, `getLinked`, `getDetails`, `getForPhase`, `updatePhase`, `recordDecision`, `updateStep`, `getStatus`, `syncMarkdown`, `commitPhase`
agent	Agent orchestration and discovery	`discover`, `getInfo`, `orchestrate`, `getSequence`, `getDocs`, `getPhaseDocs`, `listTypes`
skill	Skill management for on-demand expertise	`list`, `getContent`, `getForPhase`, `scaffold`, `export`, `fill`
sync	Import/export synchronization with AI tools	`exportRules`, `exportDocs`, `exportAgents`, `exportContext`, `exportSkills`, `reverseSync`, `importDocs`, `importAgents`, `importSkills`

Dedicated Workflow Tools

Tool	Description
workflow-init	Initialize a PREVC workflow with scale detection, gates, and autonomous mode
workflow-status	Get current workflow status, phases, and execution history
workflow-advance	Advance to the next PREVC phase with gate checking
workflow-manage	Manage handoffs, collaboration, documents, gates, and approvals

Key Features in v0.7.0

Gateway Pattern: Simplified, action-based tools reduce cognitive load
Plan Execution Tracking: Step-level tracking with updateStep, getStatus, syncMarkdown actions
Git Integration: commitPhase action for creating commits on phase completion
Q&A & Pattern Detection: Automatic Q&A generation and functional pattern analysis
Execution History: Comprehensive logging of all workflow actions to .context/workflow/actions.jsonl
Workflow Gates: Phase transition gates based on project scale with approval requirements
Export/Import Tools: Granular control over docs, agents, and skills sync with merge strategies

Skills (On-Demand Expertise)

Skills are task-specific procedures that AI agents activate when needed:

Skill	Description	Phases
`commit-message`	Generate conventional commits	E, C
`pr-review`	Review PRs against standards	R, V
`code-review`	Code quality review	R, V
`test-generation`	Generate test cases	E, V
`documentation`	Generate/update docs	P, C
`refactoring`	Safe refactoring steps	E
`bug-investigation`	Bug investigation flow	E, V
`feature-breakdown`	Break features into tasks	P
`api-design`	Design RESTful APIs	P, R
`security-audit`	Security review checklist	R, V

npx @ai-coders/context skill init           # Initialize skills
npx @ai-coders/context skill fill           # Fill skills with AI (project-specific content)
npx @ai-coders/context skill list           # List available skills
npx @ai-coders/context skill export         # Export to AI tools
npx @ai-coders/context skill create my-skill # Create custom skill

Agent Types

The orchestration system maps tasks to specialized agents:

Agent	Focus
`architect-specialist`	System architecture and patterns
`feature-developer`	New feature implementation
`bug-fixer`	Bug identification and fixes
`test-writer`	Test suites and coverage
`code-reviewer`	Code quality and best practices
`security-auditor`	Security vulnerabilities
`performance-optimizer`	Performance bottlenecks
`documentation-writer`	Technical documentation
`backend-specialist`	Server-side logic and APIs
`frontend-specialist`	User interfaces
`database-specialist`	Database solutions
`devops-specialist`	CI/CD and deployment
`mobile-specialist`	Mobile applications
`refactoring-specialist`	Code structure improvements

Name		Name	Last commit message	Last commit date
Latest commit History 225 Commits
.context		.context
.github/workflows		.github/workflows
docs		docs
prompts		prompts
scripts		scripts
src		src
.env.example		.env.example
.gitignore		.gitignore
.npmignore		.npmignore
AGENTS.md		AGENTS.md
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
example-documentation.ts		example-documentation.ts
jest.config.js		jest.config.js
package-lock.json		package-lock.json
package.json		package.json
tsconfig.json		tsconfig.json

License

vinilana/ai-coders-context

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@ai-coders/context

The Problem

The Solution

Youtube video

Connect with Us

Why PREVC?

Usage

What it does

Quick Start

PREVC Workflow System

The Problem with Autopilot AI

Documentation

Scale-Adaptive Routing

Requirements for the CLI

Supported Providers

MCP Server Setup

Quick Installation (v0.7.0+)

Manual Configuration

Antigravity

1. Access Raw Config

2. Add Custom Server

3. Restart the Connection

Claude Code (CLI)

Claude Desktop

Cursor AI

Windsurf

Zed Editor

Cline (VS Code Extension)

Codex CLI

Google Antigravity

Trae AI

Local Development

Available MCP Tools

Gateway Tools (Primary Interface)

Dedicated Workflow Tools

Key Features in v0.7.0

Skills (On-Demand Expertise)

Agent Types

License

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