AIDA

Make Vibe Coding Measurable.

Every vibe coding session generates massive insights — deviations, patterns, quality signals. But you close the terminal, and all of it vanishes. Next session, you start blind again. AIDA captures structured data at every development checkpoint, visualizes it in a live dashboard, and distills deviation patterns into rules that make your AI write better code — every single run.

One line to integrate. Zero workflow changes.

{ "mcpServers": { "aida": { "command": "npx", "args": ["-y", "ai-dev-analytics", "mcp"] } } }

One-Line Setup · Data-Driven Loop · Dashboard · SOP Workflow · Data for Reports · 中文文档

The Insight

Vibe coding is powerful. But it's a black box.

You tell Claude to build a feature. It writes code. You ship it. But you have zero visibility into what actually happened:

• How many tasks did AI complete? How long did each take?

• Where did AI deviate from your project conventions? Why?

• Which deviations keep recurring? What rules would prevent them?

• What's the bug rate? Which phases produce the most bugs?

Without data, you can't improve. You're just vibing — over and over, with the same blind spots.

AIDA makes the invisible visible. It collects structured data from every vibe coding session, renders it in a real-time dashboard, and turns deviation patterns into project rules. Your AI doesn't just code — it learns your project.

🔄 The Data-Driven Loop

This is the core of AIDA — data in, rules out, better code next time.

Vibe Coding Session
        ↓
   AIDA silently collects structured data
   (tasks, deviations, bugs, reviews, files, timeline)
        ↓
   Dashboard visualizes patterns
   "9 deviations → 56% hallucination, 44% rule-missing"
        ↓
   Deviation patterns identified → AI suggests rules → user confirms → sedimented
   .aidevos/rules/ ← your AI's growing knowledge base
        ↓
   AI reads rules next session → same mistakes eliminated
        ↓
   Repeat — each cycle, AI output gets closer to your expectations

Real data from a production project:

Step 1: See why AI deviates — Root cause breakdown reveals whether issues stem from hallucination, missing rules, or insufficient context.

Step 2: See where AI deviates — Category distribution pinpoints the exact areas: UI spacing, layout, component usage, API patterns.

Step 3: Watch rules compound — As rules accumulate (green line), the same deviation patterns stop recurring.

The .aidevos/rules/ directory is your project-specific AI knowledge base. It grows with every run. The more you use AI, the smarter it gets at your project.

📊 The Dashboard

Your entire vibe coding process — structured, visualized, actionable.

Live Demo → Real anonymized project data. No install needed.

AIDA captures every dimension of AI-assisted development and turns it into interactive charts:

Every KPI card is clickable — drill down into task details, deviation root causes, review reports, and file changes.

Run npx ai-dev-analytics dashboard to see your own project's data in seconds.

🔒 100% Local. Zero External Requests.

AIDA writes JSON files to .aidevos/ in your project directory. The codebase contains zero HTTP calls to external services — no telemetry, no cloud sync, no analytics, no tracking. Zero runtime dependencies. Your code and data never leave your machine. Period.

⚡ 30-Second Setup

One line in .mcp.json — that's the entire integration.

{ "mcpServers": { "aida": { "command": "npx", "args": ["-y", "ai-dev-analytics", "mcp"] } } }

No SDK. No wrapper. No code changes. Add this to your project root .mcp.json, and AIDA starts collecting data the next time your AI writes code. It works silently — zero workflow changes.

Tip: For faster startup, run npm install -g ai-dev-analytics and change the command to "aida".

Cursor .cursor/mcp.json:

{
  "mcpServers": {
    "aida": {
      "command": "npx",
      "args": ["-y", "ai-dev-analytics", "mcp"]
    }
  }
}

VS Code Copilot .vscode/mcp.json:

{
  "servers": {
    "aida": {
      "command": "npx",
      "args": ["-y", "ai-dev-analytics", "mcp"]
    }
  }
}

Windsurf ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "aida": {
      "command": "npx",
      "args": ["-y", "ai-dev-analytics", "mcp"]
    }
  }
}

See your data

npx ai-dev-analytics dashboard

Open http://localhost:2375 — real-time updates via SSE, Chinese/English toggle built in.

🤔 Why Data Changes Everything

Without data, every vibe coding session starts from zero. With data, each one builds on the last.

Vibe coding without data is just vibing. Add data, and it becomes a compounding system.

🎯 Use Cases

Vibe Coder — "I want my AI to actually learn my project"

You've been using Claude Code for a week. AIDA's dashboard shows: 23 deviations, concentrated in component-usage and layout categories, root cause mostly rule-missing. AI detects the patterns and suggests rules — you confirm, 6 project rules sedimented. Next week, those categories show zero deviations. Your AI now knows your project conventions.

Tech Lead — "I need to see what AI is actually doing across the team"

Team of 4 uses Claude Code daily. Open the project overview: Developer A has 2 deviations + 5 sedimented rules (AI is learning). Developer B has 15 deviations + 0 rules (AI is not learning). The data tells you exactly where to intervene.

Senior Engineer — "Show me the data for my performance review"

End of H1. Open the dashboard: 150 tasks across 3 features, 89% first-pass review rate, 12 rules sedimented that now benefit the entire team. All structured data — export it, attach it to your review doc. Data beats "I think I did a lot."

Team adopting vibe coding — "How do we go from chaotic to systematic?"

Start collecting data. After 2 weeks, the dashboard shows clear patterns: which types of tasks AI handles well, where it consistently deviates, what rules are needed. You go from "AI sometimes works" to "AI works predictably because we've taught it our conventions."

📁 Data Sedimentation & Performance Reports

AIDA doesn't just visualize — it sediments. Every run accumulates structured data that compounds over time.

Week 1:  47 tasks, 23 deviations, 5 bugs, 6 rules, 4064 lines
Week 4:  180+ tasks, deviation rate dropping, 15 rules, full quality history
Quarter: Complete development record — exportable, analyzable, presentable

What you can do with sedimented data:

All data is structured JSON in .aidevos/. No vendor lock-in. Export it, query it, pipe it into any reporting tool. Run aida report to generate a summary at any time.

⚙️ How It Works

flowchart LR
    A["Your AI Tool\nClaude Code / Cursor"] -->|Vibe Coding| B{"AIDA MCP Server\n10 Tools"}
    B -->|Silent Data Collection| C[".aidevos/run.json"]
    C -->|Visualization| D["Dashboard\nlocalhost:2375"]
    C -->|Pattern Analysis| E[".aidevos/rules/"]
    E -->|AI Reads Rules| A

Your AI tool calls AIDA's MCP tools automatically as it works. You don't invoke them manually. No prompts to write, no scripts to run — just vibe code as usual.

Data Model

All data is local JSON. No database, no cloud.

All structured JSON — ready for export, analysis, or feeding into reports.

🚀 Standardized AI Development Workflow

Beyond data collection, AIDA provides a complete SOP for AI-assisted development — a standardized workflow that turns chaotic vibe coding into a repeatable, measurable process.

aida init    # Select "Full workflow"
aida start   # Create a development run

This enables 14 AI Skills orchestrated as a full development pipeline:

PRD Ingestion → Requirement Analysis → Task Decomposition
        ↓
Code Generation → Self-Review → Bug Fix → Deviation Fix
        ↓
Data Collection → Pattern Analysis → Rule Sedimentation
        ↓
Next Run: AI reads rules → better output → fewer deviations

Every step produces structured data. Every deviation can become a rule. The SOP ensures nothing falls through the cracks — and the data makes the whole process visible and improvable.

aida init              # Interactive project setup
aida start             # Create a new development run
aida status            # Show current run status
aida dashboard         # Launch dashboard (default port 2375)
aida dashboard --port 3000 # Custom port
aida mcp               # Start MCP server (for AI tool config)
aida log <subcommand>  # Write structured data (task, bug, review, etc.)
aida reindex           # Rebuild project-level index
aida report            # Generate performance report
aida rules build       # Generate rule view files from registry
aida rules dedupe      # Find and remove near-duplicate rules
aida rules merge       # Merge rules from parallel branches
aida update            # Update skills to latest version
aida migrate           # Migrate old data to current schema

AIDA uses Model Context Protocol — the standard way for AI tools to interact with external systems. The MCP server runs over stdio with zero dependencies.

What happens when you add the config:

1. Your AI tool discovers AIDA's 10 tools via MCP

2. As the AI works, it naturally calls aida_task_start, aida_log_files, etc.

3. Data flows into run.json silently

4. Deviation patterns emerge → AI suggests rules → user confirms → sedimented

5. AI reads rules next session → output quality improves

No prompts to write. No scripts to run. No workflow to learn.

Roadmap

• Export reports as PDF / HTML (H1/H2 performance reviews)

• Historical trend analysis — deviation reduction curves over time

• Team dashboard with multi-project aggregation

• VS Code extension for inline deviation alerts

• Cross-project rule sharing — team-wide AI knowledge base

Tech Stack

Testing

npm test    # 82 tests across 29 suites

Contributing

Issues, feature requests, and PRs are welcome.

git clone https://github.com/LWTlong/ai-dev-analytics.git
cd ai-dev-analytics
npm install
npm test

License

MIT

Vibe coding without data is just vibing. Add data, and your AI gets smarter every run.

Get Started in 30 Seconds →