agentsclimarketplace

Model bakeoff

Skill Neeeophytee/ai-cost-cutter-skills/skills/model-bakeoff

10 skills that cut your AI bill with proof instead of vibes. MIT, works in Claude Code, Codex, Cursor. Every skill's check is executed by CI.

Install
npx -y skills add Neeeophytee/ai-cost-cutter-skills --skill model-bakeoff

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

3 things to look at

  • 24 days oldThe repository was created 24 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 16 stars16 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Choose a model with evidence by running the few prompts that actually matter across candidate models inside a free tier's caps, picking by a stated criterion. Use when the user asks "which model should I use", wants to evaluate a newly launched model, or is choosing between models for a specific task.

SKILL.md

3.6 KB, 865 tokens by cl100k_base, as published. Nobody here has run it

Pick a model with evidence, not vibes

A "GPT-killer at a tenth of the price" launches roughly every other week, and the user's realistic options are (a) ignore it and maybe overpay forever, or (b) burn a day benchmarking. There's a third option: a one-afternoon bake-off on a free tier, sized so it provably fits the cap before it starts. GitHub Models (as of 2026-06) exposes 45+ models, frontier ones included, behind an existing GitHub login, capped around 50 requests/day on top models: too tight for production, exactly enough for a careful comparison.

Steps

  1. Collect the prompts that actually matter: 3-7 real examples of the user's task, with a known-good answer or a concrete scoring rule each. Not synthetic puzzles; the actual tickets, extractions, or drafts this model will face.
  2. Pick 2-4 candidate models. Include the incumbent (the model currently doing the job) so the bake-off can conclude "keep what you have"; that is a valid and cheap outcome.
  3. State select_by before running: exact-match rate, win rate on pairwise comparison, latency-under-quality-bar; anything, as long as it's written down first. A criterion chosen after seeing outputs is a rationalization.
  4. Check the arithmetic: prompts x models must fit the free daily cap, or the bake-off dies at the rate limit halfway through with a biased partial result. Run the proof below.
  5. Run, score, keep the winner; then move the workload to whichever provider hosts the winner properly. The free tier was for deciding, not serving.

Prove it

cat > bakeoff.json <<'JSON'
{
  "prompts": ["summarize ticket", "extract fields", "classify intent", "draft reply", "rate sentiment"],
  "models": ["gpt-5.5", "deepseek-r1", "llama-4-maverick"],
  "select_by": "win_rate",
  "free_daily_cap": 50
}
JSON
node -e '
const c = JSON.parse(require("fs").readFileSync("bakeoff.json", "utf8"));
function bad(m) { console.error("BAD: " + m); process.exit(1); }
const P = (c.prompts || []).length, M = (c.models || []).length;
if (P < 2) bad("need at least 2 prompts to compare meaningfully");
if (M < 2) bad("need at least 2 models for a bake-off");
if (!c.select_by) bad("no selection criterion stated before running (that is how rationalization starts)");
const calls = P * M;
if (c.free_daily_cap && calls > c.free_daily_cap) bad("bake-off needs " + calls + " calls but the free cap is " + c.free_daily_cap + "/day");
console.log("bakeoff OK: " + P + " prompt(s) x " + M + " model(s) = " + calls + " call(s), within " + c.free_daily_cap + "/day; pick by " + c.select_by);
'

Guardrails

  • The free tier is for deciding, not serving: roughly 10 requests/minute and 50/day on top models, with contexts capped near 8k in / 4k out; keep bake-off prompts compact and never point production at it.
  • Scoring with an LLM judge inherits the judge's blind spots, especially when judge and contestant share a family. Prefer a concrete metric (exact match, schema-valid, tests pass) wherever one exists.
  • One bake-off is a snapshot, not a marriage. Hosted models drift and reprice; for a workload where the choice keeps mattering, re-run the same bake-off on a schedule and keep the receipts.

<sub>Backed by a machine-verified recipe, re-checked by CI: A GitHub Models bake-off that fits the free cap</sub>

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.