cost-booster-edit

Apply deterministic code transforms via the agent-booster WASM engine without LLM calls.

70.1k|8.4k|Updated Jun 2, 2025
One-click install
npx skills add https://github.com/ruvnet/claude-flow --skill cost-booster-edit
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: cost-booster-edit
Source: https://github.com/ruvnet/claude-flow/tree/main/plugins/ruflo-cost-tracker/skills/cost-booster-edit
Command: npx skills add https://github.com/ruvnet/claude-flow --skill cost-booster-edit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires agent-booster.

What problem does it solve?

Routine code transforms like converting var to const or removing console statements normally trigger paid LLM calls with hundreds of milliseconds of latency. This Skill executes those structural edits locally through the agent-booster WASM engine at sub-millisecond speed and zero token cost.

Core Features & Use Cases

  • Deterministic Code Transforms: Executes six booster intents (var-to-const, add-types, remove-console, add-error-handling, async-await, add-logging) via AgentBooster.apply() with measured average latency around 1.2 ms.
  • Confidence-Gated Writes: Fails closed when confidence falls below 0.5, refusing to write the file and escalating the edit to a Tier 2/3 LLM route instead.
  • Outcome Persistence: Records latency, confidence, strategy, and applied status to memory under the cost-tracking namespace and feeds the routing learner via hooks.
  • Use Case: In a CI pipeline, bulk-convert hundreds of files from var to const with deterministic, reproducible output and no API billing, while logging each result for cost analysis.

Quick Start

Ask the agent to apply the var-to-const transform to a specific source file using the cost-booster-edit skill and report the confidence score.

Frequently Asked Questions about cost-booster-edit

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I apply code transforms without calling an LLM?

Use the agent-booster WASM engine through AgentBooster.apply() with a code string, edit description, and language. It performs deterministic AST-based merges locally in about 1 millisecond with zero token cost.

What transforms does agent-booster support?

Six intents are supported: var-to-const, add-types, remove-console, add-error-handling, async-await, and add-logging. These cover simple structural edits where an LLM call would otherwise be billed.

When should I not use agent-booster for code edits?

Avoid it when the transform requires reasoning about intent, naming, or cross-file context, since those are Tier 2/3 jobs needing an LLM. Also skip it when confidence falls below the 0.5 threshold; the skill fails closed and escalates instead of writing.

How fast is agent-booster compared to an LLM edit?

Measured benchmarks show average latency around 1.2 ms, roughly 1000x faster than Gemini 2.0 Flash and over 1800x faster than Claude Sonnet 4.6 for the same structural transforms, with matching correctness on the test corpus.

Why does agent-booster return fuzzy_replace with low confidence?

fuzzy_replace is a best-effort strategy used when exact matching fails, typically yielding confidence around 0.64-0.70. For production transforms, prefer cases that route to exact_replace, which scored 0.85 confidence in benchmarks.