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Jamie-BitFlight

brownfield-modernization

by Jamie-BitFlight

0🍴 0📅 Jan 19, 2026

SKILL.md


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?"

Score

Total Score

60/100

Based on repository quality metrics

SKILL.md

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+20
LICENSE

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+10
説明文

100文字以上の説明がある

0/10
人気

GitHub Stars 100以上

0/15
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3ヶ月以内に更新がある

0/10
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10回以上フォークされている

0/5
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オープンIssueが50未満

+5
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プログラミング言語が設定されている

+5
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0/5

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