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 agentmods add skills/understudylabs/understudy-agent-tools/compare-model-sweepnpx skills add understudylabs/understudy-agent-tools --skill compare-model-sweepgit clone --depth 1 https://github.com/understudylabs/understudy-agent-toolsWrote 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/understudylabs/understudy-agent-tools/compare-model-sweep)<a href="https://agentmods.dev/skills/understudylabs/understudy-agent-tools/compare-model-sweep"><img src="https://agentmods.dev/badge/skills/understudylabs/understudy-agent-tools/compare-model-sweep.svg" alt="Measured on agentmods" 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 | $0.00087 | $0.02865 |
| Opus 5 | $0.00044 | $0.01432 |
| Sonnet 5 | $0.00017 | $0.00573 |
| Haiku 4.5 | $0.00009 | $0.00286 |
Grade A, and why
compare-model-sweep 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 3d 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 — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compare Model Sweep
Use this worker when the question is not "can one model pass this eval?" but "which candidate sits on the useful frontier for this workload?" The skill runs one frozen harness across a candidate matrix, records each run, and emits a decision-ready Pareto report an agent can use for route decisions.
Prefer this after ../understand-workload/SKILL.md,
../optimize-agentic-workload/SKILL.md, or
../run-local-model-lab/SKILL.md has already
identified a resettable eval and a first local candidate.
Safety Gates
Launching a named bounded sweep authorizes its declared provider calls, uploads, benchmark rows, evaluation, receipts, and cleanup. Ask again only if the sweep expands its displayed data, destination, spend, retention, download, or production-impact envelope. Never place private traces or customer data in a public sweep report.
Recommend a candidate matrix sized to resolve the objective, including a strong
anchor when it could change the decision. Present expected spend and wall-clock
before the run and follow
../understudy/reference.md → Outcome-first spend
posture. Do not omit the informative candidate merely to keep the first sweep
cheap; one activated plan covers the whole named matrix.
Do not claim a model is cheaper, faster, or better unless the sweep used the same rows, harness, metric, tool-access mode, prompt, seed, and state reset for every candidate. If those differ, label the result as an ablation or diagnostic, not a Pareto comparison.
Flow
-
Freeze the comparison contract. Record workload id, harness command, split/row ids, metric, prompt, tool-access mode, seed, timeout, concurrency, and budget. Write it to
.understudy/model-sweeps/<timestamp>/sweep-plan.json. -
Normalize candidate routes. Include each model id, route type (
local,understudy-managed-catalog,understudy-routed,byo-provider, orhosted-other), endpoint/base URL, loader/runtime, local-vs-remote boundary, max tokens, reasoning effort, pricing basis, and whether the model is cached or requires download.
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.
- 3d ago First seen · 230 lines · 87 tokens per session scan A cf3ba05b25f6
compare-model-sweep is a skill published in the GitHub repository understudylabs/understudy-agent-tools (16 stars, last pushed 4d ago), licensed MIT. It adds 87 tokens to every session and 2,865 once invoked, about $0.0004 per session on Opus 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 2026-08-30.
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