mcpField
A
accelerate

accelerate

github.com/sharpdeveyeAgent workflows592338 installsUpdated 26w ago
Repository

About accelerate

accelerate is a skill for improving agent workflow speed and cost while checking that quality is maintained. It uses baseline and follow-up measurements to guide prompt, model, caching, parallelization, and context changes.

Key features

  1. 01Records latency, cost, token, and error baselines
  2. 02Reduces token use and optimizes context
  3. 03Guides model routing and caching
  4. 04Applies parallel processing and streaming
  5. 05Compares results and checks quality impact

Use cases

  1. 01Measuring workflow performance before optimization
  2. 02Reducing request cost or latency
  3. 03Verifying that speed changes preserve quality

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/sharpdeveye/maestro --skill accelerate

After installing, type/acceleratein Claude Code to confirm it loaded.

Details

From the project README

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.

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)

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