/evaluate
Use when the user wants a quality review, interaction audit, or to test the workflow against realistic scenarios.
$ npx -y skills add sharpdeveye/maestro --skill evaluate --agent claude-codeHow 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.mdname: 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
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
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.
Repo: sharpdeveye/maestro
Other skills on maestro.
- /accelerate
Use when the workflow is too slow, too expensive, or both and needs latency, cost, or token usage optimization.
Open skill - /adapt-workflow
Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.
Open skill - /agent-workflow
Use when any Maestro command is invoked — provides foundational workflow design principles across prompt engineering, context management, tool orchestration, agent architecture, feedback loops, knowledge systems, and guardrails.
Open skill - /amplify
Use when the workflow works but needs to handle more complex cases or produce higher-quality output through better tools, context, prompts, or models.
Open skill - /calibrate
Use when workflow components are inconsistent, naming conventions vary, or a new team member's work needs alignment to project standards.
Open skill - /capture
Capture a session summary — what was done, what decisions were made, and what to do next.
Open skill

