cost-booster-edit

Q: How do I install cost-booster-edit?

Run npx skills add https://github.com/ruvnet/ruflo --skill cost-booster-edit in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/cost-booster-edit/ (personal) or .claude/skills/cost-booster-edit/ (project).

by ruvnetAI & Agents

Apply a simple code transform via agent-booster's WASM engine — sub-millisecond, deterministic, $0 (no LLM call). Companion to cost-booster-route.

Installs: 679
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/ruvnet/ruflo --skill cost-booster-edit

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads cost-booster-edit automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Cost Booster Edit

Direct wrapper around agent-booster.apply() (npm agent-booster v0.2.x, exposed via agentic-flow/agent-booster). Use when a transform is already classified as Tier 1 eligible — cost-booster-route recommends whether; this skill executes.

When to use

Bulk transforms across many files (var → const, add-types, remove-console, add-error-handling, async-await, add-logging).
Any simple, structural edit where an LLM would otherwise be called and billed.
Inside CI pipelines where determinism + zero-cost matter more than naturalness.

Do NOT use when the transform requires reasoning about intent, naming, or cross-file context — those are Tier 2/3 jobs.

Steps

Take inputs — intent (one of the 6 booster intents) and file path.
Read the source to a variable, derive the intended edit text from the intent (caller supplies).

Invoke — run from anywhere under v3/ so agent-booster resolves:

node --input-type=module -e '
  import("agent-booster")
    .then(async ({ AgentBooster }) => {
      const booster = new AgentBooster();
      const r = await booster.apply({
        code: process.argv[1],
        edit: process.argv[2],
        language: process.argv[3] || "javascript",
      });
      console.log(JSON.stringify({
        success: r.success, output: r.output, latency: r.latency,
        confidence: r.confidence, strategy: r.strategy,
        tokens: r.tokens,
      }));
    })
    .catch(e => console.log(JSON.stringify({ success: false, error: String(e.message) })));
' -- "$CODE" "$EDIT" "$LANG"

Check confidence — default threshold is 0.5. Below that, fail closed: do NOT write the file; report and escalate to Tier 2/3.
Write back the output field if success && confidence >= 0.5.
Persist outcome — memory_store --namespace cost-tracking --key "booster-edit-..." --value '{"intent":..., "latency":..., "confidence":..., "strategy":..., "applied":true}'. Feed the routing learner via hooks_model-outcome (use the cost-optimize skill's step 8).

Measured benchmark (2026-05-04, this checkout)

5 representative intents run through AgentBooster.apply():

intent	latency (ms)	wall (ms)	confidence	strategy	success
var-to-const	5	5	0.65	fuzzy_replace	true
add-types	1	1	0.64	fuzzy_replace	true
remove-console	0	0	0.70	fuzzy_replace	true
add-error-handling	0	0	0.85	exact_replace	true
async-await	0	0	0.85	exact_replace	true

Avg measured latency ≈ 1.2 ms. All 5 above the default 0.5 confidence threshold. See docs/benchmarks/0002-baseline.md for the LLM-baseline comparison.

What's verified locally

Claim	Status here
100% win rate	Verified — 12/12 on `bench/booster-corpus.json` (see `runs/latest.json`). Booster AND Gemini 2.0 Flash both score 12/12 — this is a structural-correctness corpus, not a hard adversarial one.
Sub-millisecond latency	Verified — avg 0.67 ms, p50 0 ms, p99 6 ms, max 6 ms.
$0 per edit	Verified structurally — no API call, no token billing.
Deterministic AST-based merge	Verified — same inputs reproduce the same `output` and `strategy`.
Confidence ≥ 0.5 ⇒ correct	Verified on this corpus — 12/12 above 0.5 (min 0.551), all correct.
`350×` speedup vs. LLM	Verified — exceeded against every tier: 1000.9× vs Gemini 2.0 Flash, 1838.7× vs Claude Sonnet 4.6, 2634.1× vs Claude Opus 4.7. Run `BENCH_LLM_BASELINE=1 BENCH_ANTHROPIC=1 node scripts/bench.mjs` to refresh.
Cost saved per edit	Measured: $0.000020 vs Gemini, $0.000722 vs Sonnet 4.6, $0.004720 vs Opus 4.7 (the booster side is $0 in all cases).
Win parity with frontier LLMs	Verified — Booster, Gemini 2.0 Flash, Sonnet 4.6, Opus 4.7 all scored 12/12 on this corpus. Booster matches LLM accuracy structurally for deterministic transforms.

To extend: add cases to bench/booster-corpus.json, run ( cd v3 && node ../plugins/ruflo-cost-tracker/scripts/bench.mjs ) (or with BENCH_LLM_BASELINE=1), commit runs/latest.json. Smoke step 23 fails the build if win rate drops below 0.80.

Override the LLM model: BENCH_LLM_MODEL='claude-sonnet-4' (when wired against api.anthropic.com) or BENCH_LLM_MODEL='models/gemini-2.5-flash' for a reasoning-model comparison. Pricing flags: BENCH_LLM_PRICE_IN, BENCH_LLM_PRICE_OUT.

fuzzy_replace is best-effort; for production transforms prefer cases that route to exact_replace (≥0.85 confidence in our sample).

Cross-references

ADR-0002 §"Decision 1" (route classifier) and §"Riskiest assumption" (Bash-shelled invocation) · cost-booster-route (classifier-side companion) · agent-booster npm README (3-mode install, MCP / npm / HTTP).

FAQ

What does the cost-booster-edit skill do?

Apply a simple code transform via agent-booster's WASM engine — sub-millisecond, deterministic, $0 (no LLM call). Companion to cost-booster-route.

How do I install cost-booster-edit?

Run npx skills add https://github.com/ruvnet/ruflo --skill cost-booster-edit in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/cost-booster-edit/ (personal) or .claude/skills/cost-booster-edit/ (project).

Which AI agents can use cost-booster-edit?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is cost-booster-edit safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains cost-booster-edit?

It is published by ruvnet in ruvnet/ruflo. It has 679 recorded installs.

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cost-booster-edit

Install

Install

Describe the task

Review the result

SKILL.md

Cost Booster Edit

When to use

Steps

Measured benchmark (2026-05-04, this checkout)

What's verified locally

Cross-references

FAQ

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