Temper

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 agent-architecture reference in the agent-workflow skill for topology patterns and when multi-agent is justified.

---

Pull back from over-engineering. The most common mistake isn't building too little — it's building too much.

Over-Engineering Detection

Signs you've over-engineered:

• Multi-agent for a single-agent problem
• Premature optimization before you have performance data
• Abstraction layers with one implementation
• Configuration for things that never change
• Evaluation loops on non-critical outputs
• Framework before features

The Complexity Test

For each component:

1. Is this solving a problem we actually have? (not "might have")
2. Is this the simplest solution that works?
3. Would removing this break anything? (if not, remove it)
4. Can someone new understand this in 5 minutes? (if not, simplify)

Tempering Strategies

Collapse Unnecessary Agents

OVER-ENGINEERED: User → Classifier → Router → Specialist → Formatter → Checker (6 components)
TEMPERED: User → Single Agent with good prompt (1 component, same quality)

Remove Premature Abstraction

OVER-ENGINEERED: class AgentOrchestrator with 5 strategy interfaces
TEMPERED: async function runWorkflow(input) — direct, readable

Simplify Configuration

OVER-ENGINEERED: config.yaml (200 lines, 47 params, 3 inheritance levels)
TEMPERED: config.yaml (20 lines, essential params only, sensible defaults)

What NOT to Temper

• Error handling — essential, not overhead
• Logging — saves you when things go wrong
• Input validation — prevents cascading failures
• Core guardrails — safety is non-negotiable
• The golden test set — how you know it still works

Recommended Next Step

After tempering, run /evaluate to confirm quality is preserved, or /diagnose for a full health check.

NEVER:

• Temper without measuring output quality before and after
• Remove error handling in the name of simplicity
• Simplify below the level of correctness
• Remove features users actively rely on

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: sharpdeveye
• Source: sharpdeveye/maestro
• License: MIT
• Homepage: https://maestroskills.dev

Install and usage instructions live in the source repository linked above.