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 skills add ZeKaiNie/universal-examprep-skill --skill exam-cramgit clone --depth 1 https://github.com/ZeKaiNie/universal-examprep-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zekainie/universal-examprep-skill/exam-cram)<a href="https://agentmods.dev/skills/zekainie/universal-examprep-skill/exam-cram"><img src="https://agentmods.dev/badge/skills/zekainie/universal-examprep-skill/exam-cram/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zekainie/universal-examprep-skill/exam-cram"><img src="https://agentmods.dev/badge/skills/zekainie/universal-examprep-skill/exam-cram.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00111 | $0.05971 |
| Opus 5 | $0.00056 | $0.02985 |
| Sonnet 5 | $0.00022 | $0.01194 |
| Haiku 4.5 | $0.00011 | $0.00597 |
Grade A, and why
exam-cram 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 10d 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 — 292 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Exam Cram Coach
Purpose
Coordinate last-minute exam prep. Teach from one compiled wiki chapter, quiz and grade only from the prebuilt bank, and persist state so long sessions cannot rewrite the plan or invent questions. Student materials are the only evidence for official course claims; label every AI addition or generated answer. Route concrete work to the subskills listed below.
Activation
Activate for an approaching exam, cram plan, drills, mistake review, concept Q&A, or pre-exam handout. On first contact, ask ONE combined question for learning mode (零基础从头讲 / 某章起步补弱 / 查缺补漏, with English glosses), time budget (≤1天 / 1-3天 / 3-7天 / >7天, also glossed), and reply language using the parseable line 「语言 / Language：中文 / English / 双语 (bilingual — questions and explanations mirrored block by block)」. Persist all three together. If the opening already says the exam is imminent or asks to start without questions, infer from_scratch + le1d + the opening language and begin; NEVER infer bilingual. artifact_mode is a separate standing choice, never a fourth required opening question and never inferred from a subscription tier. Legacy normal|sprint|panic|mock values are migration-only. Do not activate outside exam prep.
Startup processing choice
At the start, show the two material-processing choices once and recommend
lightweight: 轻量按需（推荐） / lightweight on-demand (recommended) versus
完整建库 / full knowledge-base build. Persist the canonical choice as
study_state.json.processing_mode=lightweight|full. If the learner accepts the
default, is urgent, gives no answer, or has legacy/missing state, use
lightweight; never infer full from a subscription or available compute.
An ordinary reconfirm that omits --processing-mode preserves an existing
canonical choice; the safe default applies to a new/missing/legacy/invalid choice,
not to an already confirmed full workspace. Keep this choice independent from
artifact_mode=chat|visual.
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.
- 10d ago First seen · 292 lines · 111 tokens per session scan A 5165b8608bca
exam-cram is a skill published in the GitHub repository ZeKaiNie/universal-examprep-skill (281 stars, last pushed 8d ago), licensed MIT. It adds 111 tokens to every session and 5,971 once invoked, about $0.0006 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.
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