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token-miser

Skill1 starsChanged 31 days agoMITView on GitHub

Reduce model token and cost waste through measured quality, task routing, compact context and paired usage receipts; verify current models and pricing.

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 2 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Read relevant context once.

    line 9
  2. Distinctive:

    Treat session model changes as recommendations unless the client exposes an authorized switch.

    line 13
  3. Very specific:

    Run the bundled regression suite with python3 -m unittest discover -s tests -v from this skill directory.

    line 27

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Token and cost discipline

Choose the cheapest available model/configuration that meets the task's measured acceptance criteria. Discover the actual model menu, quotas and pricing from the configured provider; do not invent model aliases, free capacity or a private routing service.

  • Read relevant context once; send precise excerpts and deltas with enough baseline to interpret them.
  • Cache reusable context only when the provider/client supports it. Distinguish cache hits, ordinary input and output usage. Encoding text as base64 is not useful semantic compression.
  • Bound output, retries and parallel work. Obtain existing scoped authority for paid calls and additional agents; a cost skill does not authorize spending.
  • Route mechanical tasks to smaller models only after testing correctness and tool behavior. Escalate when the failure mode warrants it. Count retries and reviewer time when comparing costs.
  • Treat session model changes as recommendations unless the client exposes an authorized switch. Do not pretend a skill can switch a running session by naming a model.
  • Compare matched tasks with the same rubric and quality threshold. Report successful-task cost and latency, not only per-token price.

Optional receipt ledger

scripts/token_ledger.py records validated call receipts and reports paired baseline/after observations. It requires Python 3.10+ on a POSIX platform (fcntl); it makes no provider calls. Inspect --help, record --help and report --help before use. Run from this skill directory or use the installed absolute script path:

python3 scripts/token_ledger.py --help
python3 scripts/token_ledger.py report calls.jsonl --format markdown

Supply benchmark/input/rubric hashes, declared and observed model, measured token counts, provider-reported cost/latency/quality, and dated pricing provenance. Never fabricate a receipt. Stale pricing, model mismatches, corrupt records and incomplete pairing cannot establish savings. Store receipts in a user-chosen private directory; do not include prompts, credentials or personal content.

Run the bundled regression suite with python3 -m unittest discover -s tests -v from this skill directory. Interpret results within the measured workload; do not promise universal savings.

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