Project Evaluation Skill

What This Skill Does

Orchestrates comprehensive project evaluation using all Claude Flow systems:

• Hive-Mind: Collective intelligence coordination
• AgentDB: Persistent memory across sessions
• Neural Training: Pattern learning from evaluations
• GOAP Planning: Action planning for improvements
• Skill Creation: Document learnings as reusable skills

Prerequisites

• Claude Flow v2.0+ (npx claude-flow@alpha)
• Initialized hive-mind (npx claude-flow hive-mind init)
• Project with prior session memory

Quick Start

# 1. Retrieve prior session state
npx claude-flow memory retrieve --namespace hive-mind --key "session/*"

# 2. Initialize evaluation swarm
npx claude-flow swarm init --topology hierarchical --agents 5

# 3. Spawn evaluation agents
# Use Claude Code Task tool:
Task("Architecture Agent", "Evaluate architecture health...", "system-architect")
Task("GOAP Agent", "Generate improvement plan...", "code-goal-planner")

Evaluation Framework

Phase 1: Memory Retrieval

// Retrieve prior session state
const session = await memory.retrieve('session/*/completed', 'hive-mind');
const worldState = await memory.retrieve('goap/world-state/final', 'goap');

Phase 2: Agent Spawning

// Spawn evaluation agents via Task tool
[Parallel]:
  Task("system-architect", "Evaluate architecture against assessment...")
  Task("code-goal-planner", "Generate GOAP plan for Grade A...")
  Task("tester", "Analyze test coverage and quality...")

Phase 3: Neural Analysis

// Train on evaluation patterns
await neuralTrain({
  pattern_type: "coordination",
  training_data: { metrics: ["architecture", "testing", "performance"] }
});

Phase 4: Results Storage

// Store in AgentDB for persistence
await memory.store('evaluation/architecture-grade', results, 'agentdb');
await memory.store('evaluation/goap-plan', plan, 'goap');

Evaluation Metrics

Category	Metrics
Architecture	Grade, critical issues, domain separation
Testing	File count, coverage %, pass rate
Performance	Bundle size, build time, store LOC
Tech Debt	Deprecated code, uncommitted changes

Output Format

{
  "evaluation": {
    "previousGrade": "B-",
    "currentGrade": "B+",
    "criticalIssuesResolved": 3,
    "criticalIssuesRemaining": 0
  },
  "goapPlan": {
    "totalCost": 13,
    "actions": ["commit", "test", "delete", "optimize"],
    "successProbability": "85%"
  },
  "metrics": {
    "storeLOC": 3678,
    "testFiles": 67,
    "uncommittedFiles": 19
  }
}

Integration with Other Skills

• store-migration-workflow - For refactoring execution
• hive-mind-advanced - For collective coordination
• agentdb-memory-patterns - For persistence
• goap-planning - For action sequencing

Best Practices

1. Always retrieve prior session state before evaluation
2. Store all findings in AgentDB for cross-session persistence
3. Train neural patterns on successful evaluations
4. Generate GOAP plans for actionable next steps
5. Create skills from recurring evaluation patterns

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Created: 2025-12-03 Version: 1.0.0 Category: Project Management