Add purpose and feature surface mapping reports for repository

[Copilot]

Overview

This PR adds comprehensive purpose and feature surface mapping documentation to help users and developers understand the repository's architecture, runnable surfaces, and user journeys.

What's Added

/reports/purpose-map.md - Human-Readable Documentation

A comprehensive markdown document that provides:

• Single-sentence MVP purpose: Clearly defines Sherlock as an end-to-end, orchestrator-driven data science platform
• 7 User Journeys mapped with detailed tables showing:
  • Primary files and endpoints
  • Entry commands (curl, Python, npm)
  • Data dependencies (MongoDB, Redis, Kafka)
  • Risk assessment (H/M/L)
• Runnable surfaces documentation:
  • 6 core microservices with startup commands and ports
  • 4 infrastructure dependencies (MongoDB, Redis, Kafka, Nginx)
  • Docker deployment options
  • Utility scripts and tools
• Architecture diagrams showing data flow between components
• Risk assessment: 1 High, 5 Medium, 1 Low risk features
• Assumptions and uncertainties (15% uncertainty level)

/reports/feature-surface.json - Machine-Readable Format

A structured JSON file (621 lines) containing:

• Repository metadata (name, version, license model)
• 7 features with complete specifications:
  • REST API endpoints (method, path, service, port)
  • Entry commands for each feature
  • Data dependencies
  • Risk levels with detailed notes
• Runnable surfaces:
  • Service definitions with entrypoints
  • Infrastructure component details
  • Docker deployment configurations
  • Utility script mappings
• Data flow and storage architecture
• Configuration settings and thresholds
• Risk distribution summary

/reports/README.md - Usage Guide

Documentation explaining:

• Contents of both reports
• Usage examples (Python and bash)
• Quick stats and validation commands
• Key findings summary
• Architecture overview

Example Usage

Quick validation:

# Verify JSON structure
python -m json.tool reports/feature-surface.json > /dev/null

# Extract metrics
python -c "import json; data = json.load(open('reports/feature-surface.json')); \
print(f'Features: {len(data[\"features\"])}'); \
print(f'Services: {len(data[\"runnable_surfaces\"][\"services\"])}')"

Access service information:

import json

with open('reports/feature-surface.json') as f:
    data = json.load(f)

# Get all runnable services
for service in data['runnable_surfaces']['services']:
    print(f"{service['name']}: {service['startup_command']}")

Key Findings

MVP Purpose:

Sherlock is an end-to-end, orchestrator-driven data science platform that enables users to perform exploratory data analysis, data quality validation, feature engineering, and model training through microservices agents coordinated by a master orchestrator with real-time observability.

Architecture:

• Microservices-based (FastAPI agents)
• 6 independent services
• Event-driven with Kafka
• Real-time observability dashboard

Mapped Journeys:

1. Upload & Load Dataset (Medium risk)
2. Exploratory Data Analysis (Medium risk)
3. Data Quality Validation (Low risk)
4. Feature Engineering Pipeline (Medium risk)
5. Model Training & Evaluation (High risk)
6. Workflow Orchestration (Medium risk)
7. Real-time Observability Dashboard (Medium risk)

Documentation Sources

Analysis based on:

• README.md and docs/ directory
• COMPREHENSIVE_SYSTEM_AUDIT_REPORT.md
• mcp-server/config.yaml
• Agent implementation files (eda_agent.py, refinery_agent.py, ml_agent.py, master_orchestrator_api.py)
• dashboard-ui/package.json
• Various guide files (USER_GUIDE.md, REFINERY_AGENT_GUIDE.md, etc.)

Benefits

• Onboarding: New users can quickly understand system architecture and entry points
• Development: Developers can identify components and their interactions
• Operations: Clear service startup commands and dependency mapping
• Planning: Risk assessment helps prioritize maintenance and improvements
• Automation: Machine-readable JSON enables tooling and scripts

Constraints Met

✅ No code modifications
✅ Only writes to /reports directory
✅ Documented assumptions (15% uncertainty, below 20% threshold)
✅ Both human-readable and machine-readable formats provided

Original prompt

You are A0 Purpose & Surface Mapper.

Goal:

1. State the repo’s single-sentence MVP purpose.
2. Enumerate features and real user journeys (max 7) mapped to files/paths.
3. Detect runnable surfaces (apps/services/scripts), entrypoints, and start commands.

Steps:

• Read README, /docs/**, Grimoire.md, "Executive Summary (≤10 bullets).docx", package.json/workspace manifests, /apps/, /services/.
• Produce /reports/purpose-map.md with:
  • One-sentence purpose
  • Table: Journey | Files | Entry cmd | Data deps | Risk (H/M/L)
• Emit machine-readable /reports/feature-surface.json.

Constraints:

• No code edits. Only write to /reports.
• If uncertainty >20%, list assumptions explicitly.

Deliverables:

• /reports/purpose-map.md
• /reports/feature-surface.json

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[DeepExtrema]

good