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cost-benchmark

Run a prepared set of test cases to check agent-booster success rates and routing decisions. Optionally compare results with Gemini or Claude models, then save detailed measurements and summaries as JSON records for later verification.

OriginalShow the original description

Run the corpus benchmark — booster locally, optional Gemini/Sonnet/Opus baselines — and persist a verifiable measured-vs-claimed table

Works withOpenAIGemini
See how to install

Good for

  • Checking success rates before a release
  • Verifying routing after adding test cases
  • Checking performance claims against measurements
  • Comparing costs with external model baselines

Files included (1)

  • SKILL.md2.9 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Cost Benchmark

Runs scripts/bench.mjs against the structural+adversarial corpus and writes per-case + summary results to docs/benchmarks/runs/. This is the verification gate that backs every measurable claim in cost-booster-edit / cost-booster-route.

When to use

  • Before publishing a release — verify booster win rate didn't regress.
  • After expanding bench/booster-corpus.json — confirm new cases route correctly.
  • When auditing a "claimed upstream" tag — flip it to "verified" once the bench supports it.
  • On a cost question ("is Sonnet 4.6 cheaper than Opus 4.7 for these tasks?") — re-run with BENCH_ANTHROPIC=1.

Steps

  1. Run the bench from v3/ (where agent-booster resolves):

    ( cd v3 && node ../plugins/ruflo-cost-tracker/scripts/bench.mjs )                  # booster only — free, ~85 ms
    ( cd v3 && BENCH_LLM_BASELINE=1 node ../plugins/ruflo-cost-tracker/scripts/bench.mjs ) # + Gemini 2.0 Flash (cheap)
    ( cd v3 && BENCH_LLM_BASELINE=1 BENCH_ANTHROPIC=1 \
         node ../plugins/ruflo-cost-tracker/scripts/bench.mjs )                          # + Sonnet 4.6 + Opus 4.7
    
  2. Inspect the markdown summary printed to stdout. The gate metric is winRate (Tier 1 cases). Adversarial cases are tracked separately as escalationRate.

  3. Persisted output lands at:

    • docs/benchmarks/runs/latest.json — pointer to the most recent run
    • docs/benchmarks/runs/<ISO-timestamp>.json — historical record
  4. Read it back in subsequent skills (e.g. cost-report step 2 reads latest.json for live tier-spend numbers).

Smoke gates

  • winRate ≥ 0.80 on Tier 1 cases (smoke step 23). Lower the threshold by editing scripts/smoke.sh.
  • escalationRate is reported but ungated — adversarial cases are diagnostic.

Env overrides

Env varDefaultPurpose
BENCH_LLM_BASELINEunset=1 runs the OpenAI-compat baseline
BENCH_LLM_MODELmodels/gemini-2.0-flashOverride the OpenAI-compat model
BENCH_LLM_BASE_URLGemini OpenAI shimOverride endpoint
BENCH_ANTHROPICunset=1 runs Anthropic baseline (Sonnet 4.6 + Opus 4.7)
BENCH_ANTHROPIC_MODELSclaude-sonnet-4-6,claude-opus-4-7Comma-separated Claude IDs
BENCH_OUTtimestamped fileOverride output path
BENCH_QUIET=1unsetSuppress markdown summary

API keys auto-pulled from gcloud secrets (GOOGLE_AI_API_KEY, ANTHROPIC_API_KEY); override with BENCH_LLM_API_KEY / BENCH_ANTHROPIC_API_KEY.

Cross-references

ADR-0002 §"Decision 1" / §"Riskiest assumption" · cost-booster-edit/SKILL.md (verification table consumes this skill's output) · cost-report/SKILL.md step 2 (reads runs/latest.json).

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