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Free tier batch plan

Skill Neeeophytee/ai-cost-cutter-skills/skills/free-tier-batch-plan

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 free-tier-batch-plan

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

Size a big one-time batch job against a free tier's rate limit and token budget BEFORE starting it, with a proven wall-clock ETA. Use when the user wants to label a dataset, summarize an archive, or process a large backlog for free (or on a tiny rate limit), or asks "will this finish overnight?"

SKILL.md

3.3 KB, 797 tokens by cl100k_base, as published. Nobody here has run it

Prove the batch fits before you start it

Some free tiers have a strange, exploitable shape: a painfully low rate ceiling paired with a huge monthly token budget (Mistral's free Experiment tier, as of 2026-06: about 2 requests/minute but around a billion tokens/month). Useless for anything live; ideal for a patient overnight grind. The failure mode is starting the job unsized and finding it stalled, or 12% done, in the morning. Your job is the arithmetic, up front.

Steps

  1. Count the backlog: how many items, and a realistic est_tokens_per_item (input + output; when unsure, run 3 samples and measure rather than guessing).
  2. Look up the tier's actual limits (the rate ceiling in requests/minute and any monthly token budget) from the provider's current docs, not from memory or this file.
  3. Compute two numbers and show both:
    • Budget fit: items x tokens/item vs. the monthly budget. Over budget means shrink the job or split it across months.
    • ETA: items / (rpm x 60) hours at the ceiling. This tells the user whether "overnight" is 8 hours or 3 days; both are fine, but only if known in advance.
  4. Run the proof below; it fails on a job that doesn't fit its budget.
  5. Only then queue it, with a runner that actually paces to the rate limit (bursting past it gets throttled or banned, which destroys the ETA you just computed).

Prove it

cat > batch.json <<'JSON'
{
  "provider": "mistral",
  "items": 3000,
  "rate_limit_rpm": 2,
  "est_tokens_per_item": 1200,
  "monthly_token_budget": 1000000000
}
JSON
node -e '
const c = JSON.parse(require("fs").readFileSync("batch.json", "utf8"));
function bad(m) { console.error("BAD: " + m); process.exit(1); }
if (!c.items || c.items < 1) bad("no items to process");
if (!c.rate_limit_rpm || c.rate_limit_rpm < 1) bad("rate_limit_rpm must be at least 1");
if (!c.est_tokens_per_item || c.est_tokens_per_item < 1) bad("est_tokens_per_item must be set (measure 3 samples if unsure)");
const used = c.items * c.est_tokens_per_item;
if (c.monthly_token_budget && used > c.monthly_token_budget) bad("job needs " + used + " tokens but the monthly budget is " + c.monthly_token_budget);
const etaH = (c.items / (c.rate_limit_rpm * 60)).toFixed(1);
console.log("batch OK: " + c.items + " item(s) at " + c.rate_limit_rpm + " req/min = ~" + etaH + "h, using " + used + " of " + c.monthly_token_budget + " monthly tokens");
'

Guardrails

  • Nothing live or interactive belongs on a 2-requests/minute tier. This pattern is for a one-time backlog with nobody waiting on it.
  • Free tiers often use prompts to improve models. Sensitive or confidential material goes on a paid tier; still pennies, and worth it.
  • This pays off for a single big grind, not recurring work. If the same batch runs weekly, price a paid lane and compare honestly.

<sub>Backed by a machine-verified recipe, re-checked by CI: Grind a huge one-time job overnight on a free tier</sub>

Gives 0 of the 12 instructions most plan spec skills give in 797 tokens

Counted across 1,100 of the 1,860 authors here whose files we hold, read 2026-08-06

  • ask one question at a timein 46 of 1100, across 38 files
  • Break plans into vertical slicesin 28 of 1100, across 10 files
  • Publish issues in dependency orderin 27 of 1100, across 9 files
  • Iterate until user approves the breakdownin 24 of 1100, across 6 files
  • Explore the repository to understand the codebase statein 24 of 1100, across 7 files
  • Use domain glossary vocabularyin 23 of 1100, across 5 files
  • Apply correct triage labels to published issuesin 23 of 1100, across 5 files
  • Write failing tests before implementation codein 23 of 1100, across 18 files
  • Prefer AFK slices over HITLin 22 of 1100, across 7 files
  • ask clarifying questions until requirements are concretein 21 of 1100, across 13 files
  • Respect existing architecture decision recordsin 20 of 1100, across 5 files
  • write a specification before writing any codein 20 of 1100, across 12 files

Said here and by no other author read

  • count backlog items
  • measure token cost per item
  • look up tier limits from docs
  • compute budget fit
  • compute wall-clock eta
  • queue the job after passing verification

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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.