Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ZeKaiNie/universal-examprep-skillnpx agentmods add skills/zekainie/universal-examprep-skill/exam-ingestWrote 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-ingest)<a href="https://agentmods.dev/skills/zekainie/universal-examprep-skill/exam-ingest"><img src="https://agentmods.dev/badge/skills/zekainie/universal-examprep-skill/exam-ingest/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-ingest"><img src="https://agentmods.dev/badge/skills/zekainie/universal-examprep-skill/exam-ingest.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 | $0.00116 | $0.04601 |
| Opus 5.5 | $0.00046 | $0.01840 |
| Sonnet 5.5 | $0.00023 | $0.00920 |
| Haiku 4.5 | $0.00012 | $0.00460 |
Grade A, and why
exam-ingest 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 1mo 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
exam-ingest — validated workspace initialization
Purpose
Convert a confirmed materials folder into a validated cram workspace. Build and repair the knowledge base only; do not teach or grade. The normal path produces structured ingestion facts under .ingest/, compiled chapter wiki and bank files, progress state, visual evidence, and an explicit readiness verdict before handing control back to exam-cram.
This module is the explicit processing_mode=full route. A missing, legacy, or
lightweight processing choice must not activate it; route that learner through
scripts/lightweight_session.py instead.
Activation
Activate when the confirmed workspace lacks its wiki, bank, or progress state; when the student supplies new/changed course materials; or when validate_workspace.py reports ingestion readiness blocked. Do not treat the mere existence of generated files as proof that the workspace is ready.
Inputs
- A student-confirmed materials directory containing PDF, DOCX, PPTX, XLSX, common standalone raster images, txt, or Markdown. Scans/images without usable sidecar text, damaged/encrypted files, unsupported features/formats, and ambiguous problem/solution pairs may require evidence-backed AI/human review.
- A target workspace directory explicitly confirmed by the student. Never default to the repository, process current directory, or an inferred course folder. The workspace must be separate from the materials tree so reruns cannot ingest generated outputs. If no workspace is confirmed, use
update_progress.py workspace-list --json, then ask the student to select or provide one before writing anything.
Workflow
- Pass the executable start gate, then use the official ingestion entry. The exact materials/workspace pair, all three learning choices, and explicit
processing_mode=fullmust already have been persisted withexam_start.py confirmas specified byexam-cram; a bare registry row orupdate_progress.py setis insufficient. Verify read-only withexam_start.py status --materials <dir> --workspace <ws> --json; requireready_to_ingest=true, then run from the package root:
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.
- 1mo ago First seen · 101 lines · 116 tokens per session scan A 1e27318c4b74
exam-ingest is a skill published in the GitHub repository ZeKaiNie/universal-examprep-skill (333 stars, last pushed 8d ago), licensed MIT. It adds 116 tokens to every session and 4,601 once invoked, about $0.0005 per session on Opus 5.5. 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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