Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add brenbuilds1/cutweight --skill cutweight-dietgit clone --depth 1 https://github.com/brenbuilds1/cutweightWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/brenbuilds1/cutweight/cutweight-diet)<a href="https://agentmods.dev/skills/brenbuilds1/cutweight/cutweight-diet"><img src="https://agentmods.dev/badge/skills/brenbuilds1/cutweight/cutweight-diet/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/brenbuilds1/cutweight/cutweight-diet"><img src="https://agentmods.dev/badge/skills/brenbuilds1/cutweight/cutweight-diet.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00117 | $0.01113 |
| Opus 5.5 | $0.00047 | $0.00445 |
| Sonnet 5.5 | $0.00023 | $0.00223 |
| Haiku 4.5 | $0.00012 | $0.00111 |
Grade A, and why
cutweight-diet scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Diet
Skill descriptions, memory files, MCP tool schemas: your agent reads all of it on every message before any work happens. Caching makes that cheap in dollars. It still fills the context window, and full windows measurably degrade output. This skill reads the whole bill.
Method
Rough tokens for English markdown: characters / 3 on Claude models carrying the Opus 4.7 tokenizer (Opus 4.7+, Sonnet 5, Fable 5), characters / 3.5 to 4 on older Claude (Sonnet 4.6, Haiku 4.5) and on GPT-style tokenizers. The newer Claude tokenizer counts roughly 30% higher, varying by content, so never reuse counts across models. When exact matters, Anthropic's count-tokens endpoint is free and per-model; for GPT, tiktoken runs locally. Label every number measured or estimated.
- Skills. Find every installed skill:
~/.claude/skills,.claude/skills,~/.codex/skills,.agents/skills, plugin directories. Record two numbers each: always-on (frontmatter name plus description, injected into every request) and on-trigger (body plus every file it tells the agent to read). - Memory files. Find everything loaded at session start: CLAUDE.md and AGENTS.md at user level, project level, subdirectories, local variants, plus files they import. All always-on. Measure each. Leave style linting to linters; the job here is the bill. Files a human wrote (CLAUDE.md, AGENTS.md) are fair to trim. Files an agent maintains for itself (auto-memory indexes, session memories) get billed, NEVER hand-trimmed: their lines are retrieval keys, and cutting them breaks the agent's recall in ways that surface weeks later. If one must shrink, the owning agent compacts it in-context.
- MCP tool schemas. Usually the biggest line item: each connected server
injects its tool catalog into every request, commonly 500-1,400 tokens
per tool. List servers from
.mcp.jsonand client settings, count each server's tools, estimate schema cost. If the client defers tool loading (tool search, lazy loading), bill only what loads eagerly and say so. A server you cannot enumerate is unknown, never zero. - Overlap and dead weight (skills). Name pairs claiming the same trigger phrases. If session history exists, list skills that never fired; otherwise ask the user and mark the rest unknown, not unused.
- Read the fattest items. Rules earn their tokens. Backstory does not.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago Changed · -4 lines · +11 tokens per session 587189e23821
- 23d ago First seen · 95 lines · 106 tokens per session scan A 79cba013c724
cutweight-diet is a skill published in the GitHub repository brenbuilds1/cutweight (3 stars, last pushed 10d ago), licensed MIT. It adds 117 tokens to every session and 1,113 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-23.
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