accelerate

Original：🇺🇸 English
Translated

Use when the workflow is too slow, too expensive, or both and needs latency, cost, or token usage optimization.

10installs
Sourcesharpdeveye/maestro
Added on

NPX Install

npx skill4agent add sharpdeveye/maestro accelerate

Tags

Translated version includes tags in frontmatter
agent-workflow-optimizationtoken-reductionlatency-optimizationcost-optimizationllm-performance

SKILL.md Content

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MANDATORY PREPARATION

Invoke {{command_prefix}}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 {{command_prefix}}teach-maestro first. Consult the context-management reference in the agent-workflow skill for window optimization and budget strategies.
Make the workflow faster and cheaper without sacrificing quality. Measure before and after.

Performance Audit

Measure current performance:
text
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 
{{command_prefix}}evaluate
to verify quality didn't degrade, or 
{{command_prefix}}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)