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/onboardnpx skills add understudylabs/understudy-agent-tools --skill onboardgit 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 | $0.00111 | $0.02651 |
| Opus 5 | $0.00056 | $0.01326 |
| Sonnet 5 | $0.00022 | $0.00530 |
| Haiku 4.5 | $0.00011 | $0.00265 |
Grade A, and why
onboard 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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Understudy Onboarding
The first thing a new user experiences. Goal: in a few minutes, leave them with (1) a small open model running locally on their own machine, (2) a clear sense of what Understudy is and why it matters, and (3) a saved profile so you never re-ask what you already learned.
Run this after install-agent-adapter. It follows the
engagement doctrine in
../../docs/engagement-and-pacing.md:
start the slow download first, then interview while it runs. Detail —
profile schema, interview bank, tooling-detection table — is in
reference.md.
Safety Gates
- Download approval + size cap. Name the exact model, quantization, and disk size, and get a quick yes before pulling weights. Default to the smallest verified American open model that gives a real onboarding win, and label it as a bootstrap model rather than a workload recommendation.
- Local-first, no upload. Profiling, interview answers, and the model run entirely on the machine. The profile is local; it holds preferences and detected tooling — never secrets, keys, or customer data.
- Gated weights (e.g. Gemma via Hugging Face) need license acceptance + an HF token; the Ollama path avoids this. Never print or commit a token.
Intake
Returning user? If ~/.understudy/profile.json exists, read it, greet them by
where they left off, confirm nothing major changed, and skip straight to the
work — do not re-run the full interview. Only first-timers get the full flow.
If the launch prompt came from install.sh --lower-my-ant-bill, treat the
primary goal as lowering Anthropic/Claude API spend. Still do the local-first
profile and quick proof, but keep the interview short and route the real work
to ../lower-anthropic-bill/SKILL.md:
inventory Anthropic call sites, re-baseline tokenizer risk, audit cache hits,
and build an opportunity ledger before any code edits or provider calls.
What ships with it
7 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 · 184 lines · 111 tokens per session scan A 70b1f20b56fd
onboard is a skill published in the GitHub repository understudylabs/understudy-agent-tools (16 stars, last pushed 3d ago), licensed MIT. It adds 111 tokens to every session and 2,651 once invoked, about $0.0006 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.
Other skills, from other repositories
hr-onboarding
A new-hire onboarding plan as a single page — first week schedule, buddy + manager intro, learning track, equipment checklist, and "you're set when…" outcomes. Use when the brief mentions "onboarding", "new hire", "first week plan", or "入职".
lark-im
飞书即时通讯：收发消息和管理群聊。发送和回复消息、搜索聊天记录、管理群聊成员、上传下载图片和文件、管理表情回复、发送应用内/短信/电话加急、发送和处理交互卡片（Interactive Card）、监听卡片按钮回调（card.action.trigger）。当用户需要发消息、查看或搜索聊天记录、下载聊天中的文件、查看群成员、搜索群、创建群聊或话题群、管理标记数据、管理 Feed 置顶（添加/移除/查询置顶会话）、管理标签数据、处理卡片回调时使用。.
feishu
Work with Feishu or Lark bots, docs, sheets, bitables, approval flows, and OpenAPI/MCP setup without hardcoding credentials.
reflect
Review recent work, find repeated workflow patterns, and suggest reusable skills, agents, commands, config changes, or playbooks. Use when the user asks to learn from past sessions, improve recurring workflows, or identify what should be turned into reusable agent instructions.
mochi-remind
Handle due reminders — notify the user with natural language and mark them done.
organize-threads
猫猫辅助整理未分类 thread，分析标题和元数据，建议合适的标签。 Use when: 用户说"帮我整理"、"分类 thread"、点击整理按钮。 Not for: 删除/编辑标签本身。 Output: 按 thread 的标签建议列表。.