Local LLM cost proxy: forecasts each call and recommends the cheapest model that clears the task.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag β we're steadily working through the catalog.
π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
A Claude Skill that turns Claude into a neutral LLM cost advisor: estimate what any call or agent workflow will cost across candidate models, get the cheapest model that clears the task's capability bar, and see the comparison as a Mermaid diagram β with honest ranges, never false point estimates.
Works two ways:
Claude.ai / Desktop: Settings β Capabilities β Skills β Add β upload this
folder (or the packaged .skill).
Claude Code: /plugin marketplace add labham-llc/agentcents-skill
then /plugin install agentcents@labham-skills
Then just ask things like:
what would it cost to classify ~500 support emails a day, about 200 tokens each?
I'm building a 5-step research agent β which model should each step use?
Snapshot verified: 2026-07-15 (see _meta.verified_utc) β refreshed with each agentcents release.
agentcents is a local proxy that meters,
forecasts, and budget-enforces LLM API calls β and grades its own forecasting
accuracy on a calibration page. pip install agentcents. The skill is free;
so is the agentcents free tier.
Β© Labham LLC Β· skill instructions and snapshot data may be redistributed with attribution; the agentcents software itself is proprietary (see its LICENSE).
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