---
name: evaluate
description: >
  Kick off Phase 8 (Performance Evaluation). Post-project retrospective analyzing
  agent performance, phase efficiency, cost metrics, and system improvements.
  Delegates to the performance-evaluator agent.
---

# Phase 8: Performance Evaluation

You are beginning the Performance Evaluation phase. This is the project debrief. Your goal is to produce an honest, data-driven assessment of what worked and what didn't.

## Prerequisites

- Project passed Phase 7 (Final Testing)
- Ticket evaluation data available in `docs/evals/tickets/`
- Test report available in `docs/evals/`

## Process

1. **Gather all metrics.** Collect data from:
   - Ticket evaluations (tokens, time, retries, pass/fail)
   - Test reports
   - Any cost logs maintained during development
   - Notes on agent behavior, stalls, or escalations
2. **Delegate to performance-evaluator agent.** The agent produces a comprehensive evaluation covering:
   - Agent scorecards (per agent: tasks, pass rate, retries, token usage)
   - Phase analysis (per phase: estimated vs actual time and tokens)
   - Cost breakdown (by model, phase, agent, ticket)
   - Skill gaps identified
   - Base assumption review
   - Repeated patterns (candidates for automation)
3. **Produce actionable recommendations.** Each recommendation must be:
   - Specific (what exactly to change)
   - Justified (data supporting the change)
   - Scoped (which file/agent/phase/skill to update)
4. **Save** the evaluation to `docs/evals/<project-slug>-eval-<date>.md`
5. **Present findings to user** with a summary and the list of recommended changes.
6. **Ask for approval** on which recommendations to implement in Phase 9.

## Key Questions to Answer

- Which agents performed well? Which need prompt refinement?
- Which phases took longer than expected? Why?
- What was the total project cost? Was the 2x buffer sufficient?
- What skills or tools were missing that would have helped?
- What messages or patterns were repeated? Should they become skills or automations?
- Should any base assumptions change (language defaults, tool choices, framework preferences)?
- How did estimated token usage compare to actual across the board?

## Rules

- Every claim must be backed by data
- Be honest — if the system failed somewhere, say so clearly
- Compare against previous evaluations if they exist
- Recommendations must be actionable by Phase 9 — no vague "do better" suggestions
