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name: "Project Evaluation" description: "Comprehensive project status evaluation using hive-mind coordination, GOAP planning, neural analysis, and AgentDB memory. Use when assessing architecture health, planning refactoring, or generating status reports."

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

Copy# 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

Copy// 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

Copy// 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

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

Phase 4: Results Storage

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

Evaluation Metrics

Output Format

Copy{
  "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