A CLI tool for systematic deep analysis of markdown documents and codebases using a two-tier AI architecture: GPT-5.2-Pro for reasoning and GPT-5.2 for file discovery.

🤖 Note: This project was "vibe engineered" with Amp and Claude Opus 4.5 and others as part of my ongoing effort to demonstrate that AI-assisted development can produce high-quality software when paired with rigorous design documentation, comprehensive tests, and careful human review.

• Two-Tier Architecture: GPT-5.2-Pro focuses on reasoning while GPT-5.2 handles file discovery
• Three High-Level Tools: find_files, summarize_files, read_files with cost controls
• Session Continuity: Continue conversations with --continue <session-id>
• Cost Tracking: Separate usage reporting for researcher and scout models

• Go 1.25.1 or later
• OpenAI API Key with access to GPT-5.2-Pro and GPT-5.2

# Build the CLI
go build -o dist/deep-analysis .

# Or with task
task build

Set your OpenAI API key:

export OPENAI_API_KEY="your-api-key-here"

# Analyze a markdown document (results appended in place)
./dist/deep-analysis notes.md

# Write output to a different file
./dist/deep-analysis notes.md --output annotated.md

# Analyze a project in a different directory
./dist/deep-analysis --cwd /path/to/project task.md

Each run generates a session ID logged to stderr:

INFO Saved session session=f1736654e6d5a7c1b58d14ac response_id=resp_xxx

To continue a conversation:

1. Add your follow-up question to the document
2. Run with --continue:

./dist/deep-analysis notes.md --continue f1736654e6d5a7c1b58d14ac

The AI will see your previous analysis and focus on new questions.

Researcher (GPT-5.2-Pro)   →  Reasoning, analysis, conclusions
        ↓
    find_files / summarize_files / read_files
        ↓
Scout (GPT-5.2)            →  Translates queries to glob/grep
        ↓
File System                →  Actual file access

1. find_files(query, paths) - Discover files matching natural language intent

   • Returns file paths with sizes
   • Scout translates to glob/grep patterns

2. summarize_files(paths, focus) - Get AI-generated summaries (cheap, use liberally)

   • Scout reads and summarizes files
   • Use for triage before full reads

3. read_files(paths) - Read full file contents (expensive, use sparingly)

   • Limited to 10 files or 200KB per call
   • Exceeding limits returns an error with guidance

The researcher follows: find → summarize → read

1. find_files("error handling") → Returns 15 files (180KB total)
2. summarize_files(all paths, "error patterns") → Quick summaries
3. Identify 3 key files from summaries
4. read_files(those 3) → Full content for analysis
5. Write analysis citing specific code

Each run reports usage for both models:

INFO Researcher usage (GPT-5.2-Pro) api_calls=5 input_tokens=12000 output_tokens=3000 cost_usd=$0.7560
INFO Scout usage (GPT-5.2)         api_calls=8 input_tokens=45000 output_tokens=800  cost_usd=$0.0899
INFO Total cost                   usd=$0.8459

task build   # Build to dist/deep-analysis
task test    # Run tests
task lint    # Run linter

.
├── main.go                      # CLI entrypoint
├── internal/
│   ├── agent/
│   │   ├── scout.go            # Scout dispatcher (GPT-5.1)
│   │   ├── manifest.go         # Project file listing
│   │   └── file_search.go      # Legacy file search
│   ├── client/
│   │   ├── deepanalysis.go     # Researcher client (GPT-5-Pro)
│   │   └── session_store.go    # Session persistence
│   ├── fileops/
│   │   └── fileops.go          # File operations (read, grep, glob)
│   └── server/                 # MCP server (optional)
└── plans/
    └── two-tier-analysis.md    # Architecture documentation

MIT