agentleFS
Sign inStart free
ShawhinTShawhinT/non-coding-skills/cost-optimizer/SKILL.md

cost-optimizer

Skill40 starsChanged 13 days agoNo licenceView on GitHub

Estimate the token usage and dollar cost of the CURRENT conversation, then translate it into a break-even wage (effective hourly cost) so the user can judge whether the task was worth it. Use whenever the user asks what a conversation cost, how many tokens it used, "was this worth it", "what's the break-even wage", "effective hourly cost", "run the cost estimate", "/cost", "estimate the tokens", or any end-of-conversation cost check. Also fire when the user is weighing the value they got against what a task cost. Produces an estimate (not a billed figure) with list-price and cache-adjusted cost, plus a break-even wage; model-tier suggestions are only a forward-looking note since the desktop app can't switch models mid-conversation. Do NOT use for pulling real historical usage from the Anthropic Console (that's a manual export) or for pricing questions unrelated to the current chat.

8 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

10 rules. 0 are common, 7 are distinctive, and 3 are very specific to this project.

  • 0

    Common

    Most of the 8 edited versions we read keep these.

  • 7

    Distinctive

    This author's own choices. Worth a look.

  • 3

    Very specific

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

No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.

“Common” is measured: kept by at least 6 in 10 of the 8 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free