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/lsh1215/oh-my-study-with-menpx agentmods add skills/lsh1215/oh-my-study-with-me/study-vaultWrote 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/lsh1215/oh-my-study-with-me/study-vault)<a href="https://agentmods.dev/skills/lsh1215/oh-my-study-with-me/study-vault"><img src="https://agentmods.dev/badge/skills/lsh1215/oh-my-study-with-me/study-vault/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/lsh1215/oh-my-study-with-me/study-vault"><img src="https://agentmods.dev/badge/skills/lsh1215/oh-my-study-with-me/study-vault.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00056 | $0.02522 |
| Opus 5 | $0.00028 | $0.01261 |
| Sonnet 5 | $0.00011 | $0.00504 |
| Haiku 4.5 | $0.00006 | $0.00252 |
Grade A, and why
study-vault 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 — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Study Vault - Pre-Note Generation Skill
Argument Parsing
- No argument → start from source selection
[book-name keyword]→ start immediately with that book (PDF flow)[URL]→ fetch and generate vault from the URL content (web/GitHub flow)[book-name] [chapter-range]→ specific chapters only (e.g.,kafka Ch3-Ch5)
Argument: $ARGUMENTS
Difference from /oh-my-study-with-me:study
| study | study-vault | |
|---|---|---|
| Approach | Read and converse interactively to build understanding | Analyze the whole first, then generate notes |
| Speed | Slow (interactive) | Fast (auto-generated) |
| Depth | Deep (Why? conversation) | Broad (grasp overall structure) |
| Best for | Deep diving into key chapters | Bird's-eye view of a new book, structuring concept relationships, review notes |
The two skills are complementary:
- Use
/oh-my-study-with-me:study-vaultto get an overview of the book's entire structure first. - Use
/oh-my-study-with-me:studyto deep dive into key chapters. - Review using the vault's practice problems.
Design Principles
- Source verification: Only write notes after actually reading and confirming the content, not just the filename.
- Active recall: Write all answers in collapsed state (details/summary). Make the learner think before expanding.
- Concept connections: Every note cross-references related notes.
- Self-review: Run a quality checklist at the end.
Phase 1: Source Exploration and Structure Analysis
Detect the source type from the argument or user input:
Web URL Flow
- Use WebFetch to retrieve the article/documentation content.
- Extract the content structure (headings, sections, subsections).
- Build a topic hierarchy from the extracted structure.
- Continue to step 3 below.
GitHub Repo Flow
- Use WebFetch to fetch the README.
- Use
ghCLI or WebFetch to get the repo structure (directory tree, key files). - Identify the main architectural components and documentation sections.
- Build a topic hierarchy from the repo structure and README.
- Continue to step 3 below.
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 · 300 lines · 0 tokens per session scan A d1827cac13a9
study-vault is a skill published in the GitHub repository lsh1215/oh-my-study-with-me (8 stars, last pushed 6mo ago), licensed MIT. It adds 56 tokens to every session and 2,522 once invoked, about $0.0003 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 2026-08-31.
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