understudy-onboard

An onboarding guide for Understudy, a toolkit for improving AI apps and coding agents. It walks through setup, machine profiling, and choosing a local workload to measure.

In plain words
What is it for?
Use it to sign up, inspect your computer, start Understudy’s improvement process, and select a real local workload or data sample for measurement.
Why use it?
It gives you a structured first step instead of leaving you to guess how to install, configure, and evaluate Understudy.

Command

Install

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.

agentmods
npx agentmods add commands/understudylabs/understudy-agent-tools/understudy-onboard
Clone the repo
git clone --depth 1 https://github.com/understudylabs/understudy-agent-tools
Per session 6 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 65 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00006 $0.00065
Opus 5 $0.00003 $0.00032
Sonnet 5 $0.00001 $0.00013
Haiku 4.5 $0.00001 $0.00006

Measured 2d ago against content hash b180d476b4fe, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

understudy-onboard 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 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.

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.

.opencode/commands/understudy-onboard.md · 10 lines

What it actually says

Use the Understudy onboarding skill for this project. Guide me through the agent-first sign-up if needed, profile this machine, launch the ladder climb, and help pick a real local workload or data slice for the first measured Understudy improvement.

Changes

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.

  1. 2d ago First seen · 10 lines · 6 tokens per session scan A b180d476b4fe

Subscribe to this mod's changes

understudy-onboard is a command published in the GitHub repository understudylabs/understudy-agent-tools (16 stars, last pushed 3d ago), licensed MIT. It adds 6 tokens to every session and 65 once invoked, about $0.0000 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.