Your agents spend money in a loop. This prices the call before it happens.

One agent costs what it costs. A fleet of them spends in a loop nobody is watching per iteration, and the bill arrives a month later as one number with no per-decision detail inside it.

Trazum installs into that loop. The MCP server's first tool is spend_guard, and it is the only one here whose trigger is not a sentence somebody types:

"May I spend this?"  ->  yes, no, or cannot-tell.

A refusal carries the cheaper ways to make the same call, each priced for that call and each naming what it assumes. The ceilings come from your trazum.config.json; the spend so far comes from the usage log your host already writes. Nothing is called and nothing is spent to answer.

claude plugin marketplace add Davmunrey/Trazum
claude plugin install trazum@trazum

That one line brings the skill and the MCP server. For any other MCP client, npx -y @trazum/mcp over stdio does the same.

Or run it right now, without installing anything: the Playground

That link opens the CLI's pure subset running in the page, against sample files already loaded, through the same @trazum/core functions the terminal runs. Nothing you paste leaves your browser.

The argument, in one screenshot

Real output, transcribed. Read the last two lines: the rules recovered $24.00 a month, and the advisory above them is worth $528.40, 22 times more. That gap is the entire argument for this tool.

The prompt is the part everyone looks at, and usually the cheap part. In the run above, forty percent of the text came out and it moved 3.5% of the bill. What moved the rest was a question nobody was asking: does this task need the model it is running on?

Every figure has a receipt. Fifteen advisories, each priced per month and reproducible on a single file: caching you are not getting, work that could go through the Batch API, a schema costing tokens on every call to describe a shape the request could carry as a parameter. Underneath them, twelve deterministic rules that shorten the text itself: same input, same output, free, offline, and never touching code, URLs or placeholders. On top, an optional LLM pass for the compression rules cannot do, through whichever provider you configure, which never runs unless you ask.

                      ┌──────────────┐
                      │ @trazum/core │   the library: rules, tokens, pricing
                      └──────┬───────┘   zero dependencies, browser-safe
         ┌─────────────┬─────┴────────┬──────────────┐
   @trazum/cli    @trazum/mcp    @trazum/web       action/
 42 commands       MCP server      Next.js     comments on pull requests
                 for your agents

Contents

• What it actually does — the five things, and what it refuses to touch
• The forty-two commands: the whole surface, one line each
• Getting started — CLI, web, the GitHub Action, pre-commit
• The first five minutes — init, and the four things it refuses to write
• Building on the format — the contracts, the guarantees, and the doctrine
• More than one machine — several people's documents, one bill, every gap preserved
• Did anything stop running — the outside view of a scheduled job
• Which few-shot examples earn their tokens — measured, and it asks before spending
• An MCP server for your agents — budget a prompt before sending it
• Languages — what the dictionaries cover, and what they deliberately do not
• Connecting your own LLM — one wire format, four native providers, and the SSRF rules
• Every model you pay for by the token — pricing across seven providers, live via OpenRouter
• Token counting — the estimator, and the error band it prints
• Limitations, stated plainly — read this one
• Running it on a schedule — cron, systemd, Actions, and where the answer runs out
• Everything else — the documentation index, arranged by whether you are choosing this, using it, extending it or maintaining it
• Layout · Updating prices · Privacy · Roadmap

What it actually does

1. Tells you where the money actually is. This is the part worth reading first, because it is where the numbers are. Every advisory is priced per month against your own call volume, and none of them is about making the text shorter:

The last four are advisory only. A contradiction has a right answer that only the author knows, and an example that looks redundant may be demonstrating a boundary case on purpose. Trazum points; it does not cut.

The one finding that is not a trade-off

Most of what Trazum reports is a choice: shorter against clearer, cheaper against more capable. Moving an output schema out of the prompt is neither.

→ The output schema could travel in the request instead of the prompt
  A schema block introduced by "output format" defines `category`, `reply`,
  `escalate_to_human`, `confidence`, costing about 62 tokens on every call.

Those tokens are paid on every call to have the model read a shape and be asked, politely, to match it. output_config.format, response_format, responseSchema — whatever your provider calls it — takes the same shape as a request parameter, where the decoder is constrained rather than persuaded. Cheaper and stricter.

Trazum reports it and never does it, because it is not a change to the prompt: it is a change to the code that sends the prompt. A rule that deleted the schema would leave a prompt asking for a shape it no longer describes, sent by a client nobody updated — strictly worse than what it started from.

The one way this could do harm, and what stops it. Output format: {...} is a contract and moving it is free; Input: {...} inside a few-shot example is data the prompt needs, and moving it breaks the prompt. So nothing is guessed: a block counts only when a phrase from the output-cue dictionary appears immediately before it, in one of the seven languages the rules cover. No phrase, no finding — a false negative, which is the right direction to be wrong in.

The example detector finds near-copies — the way few-shot blocks actually grow. It deliberately does not flag paraphrases: that case needs a model, and is on the roadmap for the LLM pass.

Read the full README on GitHub →