brownfield-modernization

name	brownfield-modernization
description	Orchestrate concurrent agents through brownfield modernization phases with checkpoint-based resumable workflows. Use when starting a modernization initiative, resuming from a checkpoint, or coordinating modernization across multiple components.

Brownfield Modernization Skill

Overview

This skill manages the entire lifecycle of a brownfield modernization project by:

1. Initializing checkpoints - Creates a resumable progress document
2. Spawning concurrent subagents - Each specializes in a modernization aspect
3. Aggregating progress - Updates checkpoint with agent results
4. Enabling resumption - Agents can pick up from last checkpoint state

When This Skill Activates

Claude will automatically use this skill when the user:

• Asks to "modernize" or "upgrade" a codebase
• Mentions "brownfield" project work
• Requests concurrent agent orchestration
• Wants to resume from a checkpoint
• Needs systematic codebase analysis and improvement

Core Workflow

1. Checkpoint Detection

# Pseudo-code for checkpoint handling
if exists("MODERNIZATION_CHECKPOINT.md"):
    checkpoint = parse_checkpoint()
    if checkpoint.status == "Complete":
        offer_new_modernization_or_review()
    else:
        resume_from_checkpoint(checkpoint)
else:
    initialize_new_checkpoint()
    start_phase_1()

2. Agent Spawning Pattern

Spawn agents using the Task tool with specific subagent types:

# Concurrent exploration agents (Phase 1)
Task(subagent_type="Explore", prompt="Analyze architecture...")
Task(subagent_type="Explore", prompt="Map dependencies...")
Task(subagent_type="Explore", prompt="Assess technical debt...")

# Implementation agents (Phase 2)
Task(subagent_type="general-purpose", prompt="Set up validation harness...")
Task(subagent_type="general-purpose", prompt="Execute modernization tasks...")

# Documentation agents (Phase 3)
Task(subagent_type="general-purpose", prompt="Generate documentation...")

3. Validation Loop

Every agent output goes through validation:

1. Agent produces output
2. Run validation gates (lint, type-check, test, security)
3. If any gate fails:
   - Feed error back to agent
   - Agent self-corrects
   - Re-run validation
4. If all gates pass:
   - Accept output
   - Update checkpoint
   - Proceed to next task

4. Checkpoint Updates

After each significant action:

## Agent Status

### [Agent Name]
- **Status**: Completed
- **Last Update**: 2026-01-19T15:30:00Z
- **Task Completed**: Analyzed 47 modules, found 12 architectural violations
- **Output Files**: docs/architecture-analysis.md

Phases

Phase 1: Planning & Discovery

Goal: Understand the current state of the codebase

Concurrent Agents:

• Architecture Analyzer
• Dependency Mapper
• Technical Debt Assessor

Deliverables:

• Architecture diagram and analysis
• Dependency upgrade plan
• Technical debt inventory with priorities

Duration: Agents work concurrently, typically completes in one session

Phase 2: Execution

Goal: Implement modernization improvements

Sequential Tasks (with validation gates):

1. Set up validation harness (type checking, linting, testing)
2. Execute highest-priority modernization tasks
3. Validate each change before proceeding

Deliverables:

• Configured validation tools
• Modernized code with passing tests
• Updated dependencies

Duration: Multiple sessions, checkpoint enables resumption

Phase 3: Validation & Documentation

Goal: Ensure quality and document everything

Concurrent Agents:

• Test Coverage Improver
• Documentation Generator

Deliverables:

• Improved test coverage (target: 80%+)
• Per-directory README files
• Updated API documentation

Duration: Agents work concurrently, typically completes in one session

Self-Correction Patterns

Pattern 1: Lint Failure Correction

Agent output: def foo(x): return x+1
Lint error: E225 missing whitespace around operator
Self-correction: def foo(x): return x + 1

Pattern 2: Type Check Correction

Agent output: def process(data: list) -> dict:
Type error: Missing type parameters
Self-correction: def process(data: list[str]) -> dict[str, Any]:

Pattern 3: Test Failure Correction

Agent output: Changed authentication from bcrypt to argon2
Test failure: Expected bcrypt hash format
Analysis: Check git history for intentional bcrypt usage
Self-correction: Preserve bcrypt, update only non-auth code

Pattern 4: Hallucination Detection

Agent claim: "Project uses FastAPI"
Verification: grep -r "fastapi" requirements.txt → Not found
Self-correction: Remove incorrect claim, verify actual framework

Integration with Checklist

This skill works in conjunction with the comprehensive checklist at: docs/AI_ASSISTED_BROWNFIELD_MODERNIZATION_CHECKLIST.md

The checklist provides:

• Detailed validation procedures
• Configuration templates
• Example verification loops
• Tool configuration guides

Resumption Protocol

When resuming from a checkpoint:

1. Read checkpoint file completely
2. Identify current phase and active tasks
3. Check agent statuses:
   • Completed: Skip, use results
   • In Progress: Resume from last known state
   • Blocked: Report blocker, attempt resolution or skip
   • Pending: Start fresh
4. Verify environment hasn't changed (dependencies, configs)
5. Continue orchestration from appropriate point

Error Recovery

Agent Timeout

• Save partial progress to checkpoint
• Mark agent as "Partial"
• Allow manual retry or skip

Validation Gate Persistent Failure

• After 3 self-correction attempts, mark as "Blocked"
• Document the issue in checkpoint
• Escalate to human developer

External Service Failure

• Retry with exponential backoff
• If persistent, document and continue with other tasks

Usage

Users can invoke this skill by:

1. Using the slash command: /modernize
2. Asking Claude directly: "Help me modernize this brownfield codebase"
3. Requesting specific phases: "Run Phase 2 of the modernization"
4. Checking status: "What's the current modernization status?"