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/manage-local-modelsnpx skills add understudylabs/understudy-agent-tools --skill manage-local-modelsgit 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/manage-local-models)<a href="https://agentmods.dev/skills/understudylabs/understudy-agent-tools/manage-local-models"><img src="https://agentmods.dev/badge/skills/understudylabs/understudy-agent-tools/manage-local-models.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 | $0.00127 | $0.02511 |
| Opus 5 | $0.00063 | $0.01256 |
| Sonnet 5 | $0.00025 | $0.00502 |
| Haiku 4.5 | $0.00013 | $0.00251 |
Grade A, and why
manage-local-models 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 4d 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Manage Local Models
Get open-weight models onto the machine, keep them organized, and teach the user
enough to choose well. This skill is acquisition + curation + education; to score
a local model against a workload, use
../run-local-model-lab/SKILL.md.
The habit this skill installs: use a small verified model to prove the local
loop, then choose the workload candidate from task fit, evidence, hardware, and
time. The bootstrap model is not a route recommendation. Skip directly to a
stronger rung when a weak-model sequence would delay the answer; big quality
lives one understudy route away when you need it.
Safety Gates
- No download without explicit approval + a size cap. Always state model, quantization, and GB on disk first, then confirm. Weights are large; a wrong pull can fill a disk.
- Background big pulls. Announce the ETA, start the download in the background, and keep working — do not block the user on a progress bar.
- Gated weights need consent. Gemma (and some others) require accepting a license and using a Hugging Face token. Walk the user through acceptance; never print, log, or commit the token. The Ollama path serves Gemma without an HF token.
- Local-first, no upload. Pulling weights is a download only; nothing about the user's data leaves the machine.
- Make size/spec/price claims from fresh official sources (HF model cards, the Ollama library, vendor pages), never from memory — label anything indicative.
Intake
Read ~/.understudy/profile.json for hardware, installed runtimes, and the
user's experience tier (set tone accordingly). Inventory what is already cached
before proposing a download — the best pull is often one they already have. Disk
locations and registry links are in reference.md.
If the Understudy desktop app is running, prefer its daemon. Check
~/.understudy/agent-card.json and trust the app block only after a pid
check on app.pid plus a health probe of <app.base_url>/health
(understudy daemon status does exactly this; then run
understudy desktop capabilities; schema in
../onboard/reference.md). A running app can
start/poll/cancel verified snapshot downloads into the same
~/.understudy/models cache and already serves warm slots
(app.warm_models) and exposes them through understudy desktop chat with
canonical runtime evidence — reuse it instead of spawning your own MLX servers
or a second download of the same weights. The concrete verbs:
understudy desktop model list / model catalog (cached snapshots and the
bundled certified catalog), slot list|add|assign (residency),
download list|start (managed downloads), and chat --slot <id>. If the app
is not installed, it ships as GitHub Releases on
understudylabs/understudy-agent-tools (macOS Apple Silicon .dmg, tags
desktop-v*); the headless CLI covers everything in this skill without it.
What ships with it
3 files 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.
- 4d ago First seen · 171 lines · 127 tokens per session scan A 8cba127e22bb
manage-local-models is a skill published in the GitHub repository understudylabs/understudy-agent-tools (16 stars, last pushed 3d ago), licensed MIT. It adds 127 tokens to every session and 2,511 once invoked, about $0.0006 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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