Agent skill
Project Evaluation
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.
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Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/productivity/project-evaluation
SKILL.md
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
bash
# 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
typescript
// 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
javascript
// 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
typescript
// Train on evaluation patterns
await neuralTrain({
pattern_type: "coordination",
training_data: { metrics: ["architecture", "testing", "performance"] }
});
Phase 4: Results Storage
typescript
// 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
json
{
"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 executionhive-mind-advanced- For collective coordinationagentdb-memory-patterns- For persistencegoap-planning- For action sequencing
Best Practices
- Always retrieve prior session state before evaluation
- Store all findings in AgentDB for cross-session persistence
- Train neural patterns on successful evaluations
- Generate GOAP plans for actionable next steps
- Create skills from recurring evaluation patterns
Created: 2025-12-03 Version: 1.0.0 Category: Project Management
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