Accelerate

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 context-management reference in the agent-workflow skill for window optimization and budget strategies.

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Make the workflow faster and cheaper without sacrificing quality. Measure before and after.

Performance Audit

Measure current performance:

Current metrics:
  Latency (p50): ___ms
  Latency (p95): ___ms
  Cost per request: $___
  Token usage (avg): ___ input / ___ output
  Error rate: ___%

Acceleration Strategies

Reduce Token Usage

• Shorten system prompts (remove redundant instructions)
• Compress few-shot examples to minimum viable length
• Use structured output schemas instead of verbose text
• Summarize context instead of passing raw documents
• Reduce output length requirements

Model Cascading

• Route simple tasks to cheaper/faster models
• Escalate only complex tasks to capable models
• Use classification to determine complexity

Caching

• Cache responses for identical or near-identical inputs
• Cache tool results with appropriate TTL
• Cache embeddings for frequently-queried documents
• Use semantic caching for similar (not identical) queries

Parallelization

• Run independent tool calls in parallel
• Run independent agent steps in parallel
• Use streaming to start processing before full response

Context Optimization

• Retrieve less, retrieve better (improve retrieval precision)
• Use context compression techniques
• Implement sliding window for long conversations

Acceleration Report

For each optimization:

1. What changed: Specific modification
2. Before: Latency/cost/tokens before
3. After: Latency/cost/tokens after
4. Quality impact: Any quality change (verify with golden tests)
5. Trade-off: What was sacrificed for the improvement

Acceleration Checklist

• [ ] Baseline metrics recorded before any changes
• [ ] Each optimization measured with before/after comparison
• [ ] Quality impact verified (golden tests still pass)
• [ ] Trade-offs documented for each change
• [ ] Cost/latency improvements quantified

Recommended Next Step

After optimization, run /evaluate to verify quality didn't degrade, or /iterate to set up continuous monitoring.

NEVER:

• Optimize without measuring first (you need a baseline)
• Sacrifice quality for speed without explicit user approval
• Cache outputs that depend on real-time data
• Skip the quality check after optimization
• Optimize prematurely (make it correct first, then make it fast)

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.