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/instrumentnpx skills add understudylabs/understudy-agent-tools --skill instrumentgit 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 | $0.00098 | $0.01737 |
| Opus 5 | $0.00049 | $0.00869 |
| Sonnet 5 | $0.00020 | $0.00347 |
| Haiku 4.5 | $0.00010 | $0.00174 |
Grade A, and why
instrument 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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
CLI invocation, a test, or a curl to their own app. If they cannot, stop — How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instrument
../ingest-traces/SKILL.md and
../capture-evidence/SKILL.md assume traces
already exist. This worker is the on-ramp before that: a running app, zero
traces, and a developer who wants captures flowing without editing app
code. One redirect, one test call, one verified capture — then hand off.
Say this up front: "This takes about 5 minutes: ~1 minute to detect your SDK, ~2 minutes to sign in if you haven't, ~1 minute to redirect and verify a test call. No code changes, and nothing leaves your machine without your approval."
Resolve CLI
Prefer the installed understudy binary. If it is unavailable inside a repo
checkout, run through the package script:
npm run build
node dist/bin.js instrument --check --json
Safety Gates
- No app-code edits on this path. The whole point is env-var redirection.
If a codebase hardcodes a base URL so env vars cannot work, say so and offer
the code-patch recipes in
skills/onboard/as an explicitly separate, approval-gated step — do not silently edit. - Redirecting traffic through the gateway is an external write of the developer's prompts and completions (that is what capture is). State this plainly and get explicit approval before the first redirected call.
- Never print, commit, or write
sk_*values. Useunderstudy run -- <cmd>to inject credentials into the child process only (see../use-understudy-gateway/SKILL.md). - Set redirect env vars in the launch command or the developer's shell
session, not in checked-in files. Only write to
.envif the developer explicitly asks, and never commit it. - Do not declare success until a capture is verified to exist (step 4).
Step 1 — Detect what the app talks to (~1 min)
Inspect the project read-only. Look at package.json /
requirements.txt / pyproject.toml / lockfiles and grep call sites:
| Found | Provider path |
|---|---|
@anthropic-ai/sdk, anthropic (py) |
Anthropic-shape → ANTHROPIC_BASE_URL |
openai (ts/py) |
OpenAI-shape → OPENAI_BASE_URL |
langchain, langchain-openai, langchain-anthropic |
Wraps the SDKs above — same env vars apply |
ai + @ai-sdk/openai / @ai-sdk/anthropic (Vercel AI SDK) |
Usually needs a baseURL in the provider factory — check first; env redirect only works if the factory reads the env var |
litellm |
OpenAI-shape → OPENAI_BASE_URL or litellm's own base-url config |
none of the above / hardcoded baseURL |
Env redirect will not bite — see the honest fallbacks below |
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 · 143 lines · 98 tokens per session scan A 40682ba07eb5
instrument is a skill published in the GitHub repository understudylabs/understudy-agent-tools (16 stars, last pushed 3d ago), licensed MIT. It adds 98 tokens to every session and 1,737 once invoked, about $0.0005 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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