Cost per completed task, not per call — retry waste, context bloat priced out, cache leverage, and the margin math.
Free & open · no signup · read-only — it ends by asking · nothing leaves your machine
You are working inside this repo. Mission: compute what a completed task actually costs — including the retries, failures, and bloat — and rank the savings that don't damage quality.
Read-only pass. Your only write is the report file.
Phase 2 sweeps the codebase through every one of these, citing file and line for each finding.
tokens spent on runs that produced nothing a user kept; the invisible tax on every success
the stale payloads and token hogs (see 33), converted to money at volume
provider prompt caching on stable prefixes; memoization of idempotent calls; both usually unused
frontier pricing on small-model jobs (see 36): the monthly delta, in numbers
unbounded generations where a cap or terser format loses nothing
results computed and never read; speculative calls whose outputs get discarded
cost at 10x usage: which term grows linearly, which explodes, and where the margin dies if priced
One structured report at the repo root — or in reports/, if you keep one — the same shape every time, ready for a teammate — or the optional Studio — to act on.
Every Goal Prompt follows the same four steps, so results are consistent and repeatable — no matter which one you run.
The unit is cost per completed task, not per API call.
Failure and retry waste — tokens spent on runs that produced nothing a user kept.
Show the arithmetic on every estimate — order of magnitude is fine, hand-waving is not.
Create TOKENS.md at repo root.
Copy it and paste it into your agent inside the repo you want checked.
Install the goal plugin once — two commands — then just type /goal:token-economics-audit.
/plugin marketplace add GhostlyGawd/goal-prompts/plugin install goal@goal-promptsOr install only this Goal Prompt as /goal-token-economics-audit:
curl -fsSL https://goal-prompts.vercel.app/install | BRIEF=38 shLet an agent fetch it mid-conversation, or pull the raw Goal Prompt by URL.
https://goal-prompts.vercel.app/raw/38.mdNothing hidden — this is the whole Goal Prompt, verbatim. Read it in a minute, edit it, or copy it as-is.
# Goal: Token Economics Audit You are working inside this repo. Mission: compute what a completed task actually costs — including the retries, failures, and bloat — and rank the savings that don't damage quality. Read-only pass. Your only write is the report file. ## Phase 1 — Establish the unit - The unit is cost per **completed task**, not per API call. Define the task types. - Gather the inputs: model prices, average calls per run, tokens per call (from traces or estimation), retry rates, and failure rates — failed runs still bill. - Note which numbers are measured vs estimated; mark estimates. ## Phase 2 — Audit through 7 lenses 1. **Failure and retry waste** — tokens spent on runs that produced nothing a user kept; the invisible tax on every success 2. **Context bloat priced** — the stale payloads and token hogs (see 33), converted to money at volume 3. **Cache leverage** — provider prompt caching on stable prefixes; memoization of idempotent calls; both usually unused 4. **Model overkill priced** — frontier pricing on small-model jobs (see 36): the monthly delta, in numbers 5. **Output discipline** — unbounded generations where a cap or terser format loses nothing 6. **Dead spend** — results computed and never read; speculative calls whose outputs get discarded 7. **The scaling curve** — cost at 10x usage: which term grows linearly, which explodes, and where the margin dies if priced ## Phase 3 — Curate - Show the arithmetic on every estimate — order of magnitude is fine, hand-waving is not - Flag any saving that trades quality or reliability; those go through an eval (34) first ## Phase 4 — Report Create `TOKENS.md` at repo root: 1. **Unit economics table** — task type · calls · tokens · retries · cost per success · (margin, if priced) 2. **Waste ranked** — with arithmetic shown 3. **Savings ladder** — cumulative, each step marked safe or needs-eval 4. **The scaling curve** — the 10x projection and the knob that matters most 5. **First change** — best money per unit of effort Start the report with today's date. If `TOKENS.md` already exists from a previous run, read it first and lead with what changed since. ## Rules - Per-success is the only honest denominator - Never save tokens by shipping worse answers unmeasured - No token spend in this repo? Say so in a one-paragraph null report and stop — a null result is a valid finding. - If a `reports/` directory exists at the repo root, write the report there instead of the root. - Before asking, present the top findings as a ranked list in plain words - Report only — end by asking which savings to take
reports/ directory exists at the repo root, write the report there instead of the root.Every system prompt in the repo: contradictions, dead instructions, bloat, and injection surface — with a consolidation plan.
Your tools are an API whose consumer is a model — audit names, descriptions, error contracts, and guards on destructive actions.
How runs end — iteration caps, stop conditions, stuck-state detection, and the true cost of one pathological run.
Reconstruct what actually enters the model's window each step — stale payloads, token hogs, retrieval junk, and buried instructions.
Runs inside these playbooks — curated sequences you can launch with one paste: