a11oy Restraint

governed · measured · frugal / LIVE
Code-frugality ladder adopted from the open-source Ponytail skill (MIT) — governed (signed receipts + Λ) and measured on our stack. Ponytail's published numbers are cited as theirs; ours as ours.
Less code
median LOC reduction (ours)
Cheaper
median token-cost proxy reduction
Faster
median latency reduction
Joules saved
cumulative · tokens-saved × J/token

Two-arm benchmark

No-skill baseline vs a11oy-restraint over the same five everyday tasks.
TaskRung Baseline LOCRestraint LOC % less code% cheaperx fasterLabel
Loading benchmark…

Frugality → energy less code = fewer tokens = fewer joules

Every restraint decision saves lines → tokens → joules on our sovereign GPU. Joules are priced at the live on-box MEASURED J/token when the GPU probe is live, else our honest SAMPLE constant.
Frugality rate
decisions that cut code
Lines saved
cumulative (MODELED)
Tokens saved
≈ lines × tokens/LOC
Joules saved
J/token —

The 6-rung ladder stop at the first rung that holds

Loading ladder…
Intensities: lite (name the lazier alternative) · full (stdlib/native first, default) · ultra (YAGNI-extremist). Every decision → a signed DSSE receipt + advisory Λ (Conjecture 1, <1.0).

Reproduce flip SAMPLE → MEASURED on your stack

Run the harness with a wired model client; it counts emitted LOC deterministically and records tokens + latency from the API, then writes benchmarks/restraint/results.json. This dashboard flips to MEASURED for the run you actually executed.
Or via promptfoo (Ponytail's methodology):
npx promptfoo@latest eval -c benchmarks/restraint/promptfooconfig.yaml --repeat 10

Ponytail's published numbers CITED · theirs

These are Ponytail's results, cited honestly — never claimed as ours. Our numbers above are measured/sampled on our own stack.