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/

agent-booster

resolves:

bash

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).

cost-booster-edit

NPX Install

Tags

SKILL.md Content