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/laddernpx skills add understudylabs/understudy-agent-tools --skill laddergit clone --depth 1 https://github.com/understudylabs/understudy-agent-toolsWhat 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 | $0.00084 | $0.01588 |
| Opus 5 | $0.00042 | $0.00794 |
| Sonnet 5 | $0.00017 | $0.00318 |
| Haiku 4.5 | $0.00008 | $0.00159 |
Grade A, and why
ladder scanned grade A with 1 finding 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 2d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -N 'http://localhost:8011/run?task=sort-email&model=gemma-4-e2b' How it starts
The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ladder — the onboarding "climb"
A small, self-contained local web UI for watching and comparing LLMs on the same task. It is the no-data front door: a developer launches it and watches a small local model and a frontier model attempt the same task side by side — live reasoning, real tool calls, strict scoring — without needing any of their own traces first. As the task gets harder, the small local model keeps pace and then drops off where the frontier model carries on. That contrast is the hook into the rest of the improvement loop.
When to use
- A new user asks "what can a local model actually do?" / "is a small model good enough?" / "show me local vs frontier."
- During or right after
onboard, to make the local-vs-frontier difference concrete before steering them toward their own workload. - Any time a visceral, zero-setup-data demo of agentic behavior + scoring helps.
For the user's own eval across many candidate models, hand off to
compare-model-sweep. To serve a local model against the user's real
workload, hand off to run-local-model-lab.
Run it
Prerequisites (otherwise-silent assumptions):
- Apple Silicon (arm64) Mac with
uvonPATH— the local lane needs a current mlx stack (systemmlx_lmis often too old to load the model). - The local model cached at
~/.understudy/models/gemma-4-e2b-it-qat-mlx-vlm-understudy— thegemma-4-e2bid resolves to that directory, not a dir literally namedgemma-4-e2b. If it's missing, pull it with themanage-local-modelsskill (understudy models pull gemma-4-e2b-it-qat-mlx-vlm-understudy); the server prints an actionable error if it's absent. - Remote lanes only: run
understudy loginonce sounderstudy runcan inject the gateway key and org id. Without it a remote lane returns the literal notice[gateway not configured …]rather than erroring — don't mistake that for a real run.
Run from the repo root (the path below is relative):
understudy run -- uv run --with mlx-vlm --with mlx-lm python skills/ladder/serve.py
What ships with it
8 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.
- 2d ago First seen · 126 lines · 84 tokens per session scan A 3585a2a1220b
ladder is a skill published in the GitHub repository understudylabs/understudy-agent-tools (16 stars, last pushed 3d ago), licensed MIT. It adds 84 tokens to every session and 1,588 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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