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Skill

/evaluate

Use when the user wants a quality review, interaction audit, or to test the workflow against realistic scenarios.

From plugin
maestro
41125 skills
Install
$ npx -y skills add sharpdeveye/maestro --skill evaluate --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/evaluate

Context preview

The summary Claude sees to decide when to auto-load this skill.

Use when the user wants a quality review, interaction audit, or to test the workflow against realistic scenarios.

SKILL.md

evaluate.SKILL.md
name: evaluate
description: "Use when the user wants a quality review, interaction audit, or to test the workflow against realistic scenarios."
argument-hint: "[workflow or scenario]"
category: analysis
version: 2.0.0
user-invocable: true

MANDATORY PREPARATION

Invoke /agent-workflow — it contains workflow principles, anti-patterns, and the **Context Gathering Protocol**. Follow the protocol before proceeding — if no workflow context exists yet, you MUST run /teach-maestro first. Consult the feedback-loops reference in the agent-workflow skill for evaluation patterns, golden test sets, and regression detection.

---

Evaluate the workflow's actual interaction quality by testing it against scenarios that represent real usage.

Evaluation Dimensions

**1. Task Completion**

  • Does the workflow actually accomplish what it's supposed to?
  • Does it handle the complete task or only the happy path?
  • Are edge cases addressed or silently dropped?

**2. Output Quality**

  • Is the output accurate, complete, and well-formatted?
  • Does it match the defined output schema (if any)?
  • Would a domain expert approve the output?

**3. Error Behavior**

  • What happens when input is malformed?
  • What happens when a tool fails?
  • What happens when the model is uncertain?
  • Is the error message useful or generic?

**4. User Experience**

  • Is the interaction natural and intuitive?
  • Are confirmations requested for destructive operations?
  • Is the response time acceptable?
  • Does the workflow communicate its limitations?

**5. Consistency**

  • Does the same input produce consistent output quality?
  • Are there random failures that aren't reproducible?
  • Does quality degrade over long conversations?

Scenario Testing

Create and run test scenarios:

| Scenario | Input | Expected | Actual | Grade | |----------|-------|----------|--------|-------| | Happy path | Normal input | Correct output | ? | A-F | | Edge case | Unusual input | Graceful handling | ? | A-F | | Error case | Bad input | Helpful error | ? | A-F | | Stress case | Large/complex input | Reasonable handling | ? | A-F | | Adversarial | Tricky/malicious input | Safe response | ? | A-F |

Evaluation Report

Produce a structured report with:

1. Overall quality grade (A-F) 2. Per-dimension scores with evidence 3. Specific scenario results 4. Priority improvements with recommended Maestro commands

Evaluation Checklist

  • [ ] All 5 dimensions tested with concrete scenarios
  • [ ] At least one edge case and one adversarial case tested
  • [ ] Results documented in the scenario table
  • [ ] Overall grade assigned with justification
  • [ ] Improvement actions reference specific Maestro commands

Recommended Next Step

After evaluation, run `/fortify` to address error behavior gaps, `/refine` for output quality improvements, or `/iterate` to set up continuous quality monitoring.

**NEVER**:

  • Evaluate theoretically — run actual scenarios
  • Give an A grade unless the workflow handles all scenario types well
  • Skip adversarial testing for user-facing workflows
  • Evaluate only the happy path
Read more
Ships withmaestro

Workflow fluency for AI coding agents. 1 core skill · 25 commands · 7 domain references · memory layer · audit trail — works across Cursor, Claude Code, Gemini CLI, Copilot, and 6 more.

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TypeScript
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3mo ago
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4mo ago
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Repo: sharpdeveye/maestro