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/understudynpx skills add understudylabs/understudy-agent-tools --skill understudygit 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 | $0.00092 | $0.06154 |
| Opus 5 | $0.00046 | $0.03077 |
| Sonnet 5 | $0.00018 | $0.01231 |
| Haiku 4.5 | $0.00009 | $0.00615 |
Grade A, and why
understudy 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.
How it starts
The opening of the file, as written. The whole thing — 449 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Understudy
Understudy is agent improvement infrastructure: it helps a coding agent improve its developer's LLM system from real traces. This skill is the orchestrator — it gives your agent the loop and routes each stage to exactly one worker skill. It does not do the work inline.
The loop is backend-agnostic: it can begin locally without registration, but the default route is the strongest active model and managed cloud execution when the developer has not selected Local. A dropped dataset authorizes analysis through the active model. Activating a named, bounded cloud workflow authorizes its normal uploads, provider calls, temporary resources, evaluation, receipts, and cleanup.
The improvement loop
- Understand the codebase — find where LLM calls happen and the current model / provider / harness / routing / eval setup.
- Understand the objective (cost, speed, quality, reliability, compliance, or a weighted mix).
- Understand the constraints (what must not be violated).
- Understand the workload — inspect prompts in situ, trace the request/response code path, profile the dataset/traces, and confirm the task meaning with the user.
- Capture or locate real traces.
- Build or improve a decision-sized, representative eval harness; rerun the incumbent baseline.
- Run local optimization against eval failures.
- Compare candidate vs baseline on the objective.
- Recommend the best route for the stated objective — harness, model, supplier, gateway/inference-layer route, deployment approach — with cost and time transparent.
- Implement the selected route safely with the smallest coherent change that fully addresses the measured cause.
- Produce an Understudy Agent Improvement Report the developer can review.
Frame every job
Keep these six separate and explicit — say them back before acting:
- Objective — what are we optimizing for?
- Constraints — what are we not allowed to violate?
- Evidence — what traces / evals / prices / measurements do we have?
- Workload — what task does the prompt/data/code path actually represent?
- Route — what harness / model / supplier / deployment path?
- Action — what does the agent actually change?
- Verification — how do we prove the change helped?
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.
- 2d ago First seen · 449 lines · 92 tokens per session scan A 229967b2e809
understudy is a skill published in the GitHub repository understudylabs/understudy-agent-tools (16 stars, last pushed 2d ago), licensed MIT. It adds 92 tokens to every session and 6,154 once invoked, about $0.0005 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…