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 --env-file ../.env --repeat 10
npx promptfoo@latest view

--env-file ../.env is required because promptfoo reads .env from the current directory (benchmarks/), not the repo root where the file lives.

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; cost re-verified at 30 runs, 2026-06-17)

Code (lines)

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

Cost (USD, 5 tasks; 30 runs, 2026-06-17)

arm	Haiku	Sonnet	Opus
baseline (no skill)	0.030	0.137	0.137
caveman	0.014	0.046	0.072
ponytail	0.011	0.035	0.079

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 42-75% less, and runs 3-6x faster, on every Claude model. Cost re-verified at 30 reps, with OpenAI and Gemini arms, in results/2026-06-17-cost-verification.md.

Read this number honestly (updated 2026-06-18). The gap above is single-shot, against a bare model that answers with several options plus commentary, so it counts prose, not just code, and overstates the win. #126 was right about that. The agentic benchmark re-runs the comparison as a real Claude Code session on a real public repo: ponytail cuts 60-94% on features with an over-build trap (custom component vs native input), is a wash on already-minimal code, never writes more, and stays 100% safe while the bare "one-liner" prompt drops a guard. That is the honest, defensible number. See results/2026-06-18-agentic.md.

Independent benchmarks

Run by other people, not by us, on their own harnesses and machines. Linked for transparency: the numbers are theirs, may shift between runs, and are corroboration rather than official figures. Only plugin-installed runs are listed, since pasting SKILL.md into a prompt is a rough approximation of full and skews the result.

Source	Method	Headline	Date
KuldeepB19	Installed plugin, 24 tasks, no-skill vs Lite/Full/Ultra, 5 runs each (480 builds), Opus 4.8, graded by executing the code	~44% less code (53% fewer statements), no correctness or security regression; trims everyday bad-input handling on 5/24 tasks	2026-06-24
RicardoCostaGit	Multi-turn agentic runs via the Cursor SDK, isolated git worktrees, rule file toggled per run	Leaner output but higher process cost (more tool calls/tokens) on large completion-forced tasks; savings land on blocked/snowball-prone tasks	2026-06-16

Both land on the same split as the honesty note above: ponytail reliably writes less code, and whether that saves money depends on the workload (big win on over-build and blocked tasks, can cost more on large completion-forced agentic runs).

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 ≥ 22.22.0 (promptfoo's engine constraint; see Reproduce).

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 (one prompt, one completion), not real multi-turn agent sessions. In a session the ruleset re-injects and the ladder deliberates every turn across many turns, so per-session cost can come out higher or lower than these numbers. Prompt caching offsets some of the re-injection, but a measured agentic A/B (#121) found ponytail can also raise tool calls and cost on completion-forced tasks. Treat these as generation numbers, not a session-cost promise.
• These are everyday tasks. For production-grade specs, where an unconstrained agent bloats much harder, see the writeups in results/.