benchmarks

Benchmark

Three arms (no skill, caveman, ponytail), three models, five everyday tasks, 10 runs per cell, median reported. Code LOC is counted from fenced code blocks; tokens, cost, and latency come straight from the API.

Reproduce

Claude (Haiku / Sonnet / Opus)

Requires an Anthropic API key and Node.js ≥ 22.22.0 (promptfoo's engine constraint — check with node --version and upgrade if needed):

cp ../.env.example ../.env      # add your ANTHROPIC_API_KEY
npx promptfoo@latest eval -c promptfooconfig.yaml --repeat 10
npx promptfoo@latest view

Local models via Ollama

No API key or promptfoo required. Runs against any model served by Ollama:

ollama pull llama3.2          # or any other model
python benchmarks/benchmark-local.py --model llama3.2 --repeat 3

See benchmarks/results/2026-06-15-llama3.2-local.md for what to expect: the skill works well on instruction-following models (Claude-class) but transfers poorly to small local models where the multi-step decision ladder isn't reliably followed.

Tasks: email validator, JS debounce, CSV sum, React countdown, FastAPI rate-limit (see promptfooconfig.yaml). Single-shot completions, default temperature.

Median results (10 runs, 2026-06-13)

Code (lines)

arm	Haiku	Sonnet	Opus
baseline (no skill)	518	693	256
caveman	116	120	67
ponytail	39	44	51

Cost (USD, 5 tasks)

arm	Haiku	Sonnet	Opus
baseline (no skill)	0.032	0.141	0.135
caveman	0.014	0.045	0.075
ponytail	0.010	0.032	0.071

Latency (seconds, 5 tasks)

arm	Haiku	Sonnet	Opus
baseline (no skill)	37.7	124.1	58.7
caveman	14.9	34.7	23.1
ponytail	9.9	20.1	18.0

Versus baseline, ponytail writes 80-94% less code, costs 47-77% less, and runs 3-6x faster, on every model.

Metrics

File	Metric	Behavior
loc.js	loc	Measurement - always passes, records line count
correctness.js	correct	Gate - fails if generated code doesn't work

correctness.js extracts fenced code blocks and runs per-task checks (spawns Python/Node for email, debounce, CSV; structural regex for React and FastAPI). A broken one-liner that scores great on LOC will fail on correctness.

Note: The React countdown and FastAPI rate-limit checks are keyword/structural only (no runtime execution), so they verify plausible structure rather than full correctness. The email, debounce, and CSV checks execute the code.

Prerequisites

Running the benchmark requires Python 3, pandas, and Node.js (18+).

Notes

• Caveman is a prose-compression skill (it leaves code "normal"), so it lands between baseline and ponytail on code size and wins mainly on prose tokens.
• Cost reflects single-shot calls that re-send the skill every time. In real sessions the skill is injected once and prompt-cached, so the cost gap widens further in ponytail's favor.
• These are everyday tasks. For production-grade specs, where an unconstrained agent bloats much harder, see the writeups in results/.