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 AtlasOmnia/donna-starter --skill office-document-reviewgit clone --depth 1 https://github.com/AtlasOmnia/donna-starterWrote 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/atlasomnia/donna-starter/office-document-review)<a href="https://agentmods.dev/skills/atlasomnia/donna-starter/office-document-review"><img src="https://agentmods.dev/badge/skills/atlasomnia/donna-starter/office-document-review/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/atlasomnia/donna-starter/office-document-review"><img src="https://agentmods.dev/badge/skills/atlasomnia/donna-starter/office-document-review.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 | $0.00038 | $0.01221 |
| Opus 5 | $0.00019 | $0.00611 |
| Sonnet 5 | $0.00008 | $0.00244 |
| Haiku 4.5 | $0.00004 | $0.00122 |
Grade A, and why
office-document-review 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- office-document-review — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Office Document Review
Use this skill when the user wants you to inspect a OneDrive/SharePoint-style folder tree, locate a subfolder by name/date, and proofread all documents in it for spelling and grammar.
This is a workflow skill, not a single-app skill: it covers local file-system browsing, document text extraction, and batch proofreading notes.
Triggers
- "go to the OneDrive folder"
- "look in folder X and then folder Y"
- "proofread all those documents"
- "check for spelling/grammar errors in the docs"
- "review the files in this folder"
Core workflow
- Resolve the real path first
- Prefer a filesystem path over guessing from the user’s wording.
- Folder names in casual speech may not match the actual on-disk name exactly.
- If the exact folder cannot be found, list the visible folder names and state what is missing before asking for clarification.
- Search the tree recursively
- Use the live file tree to locate the target folder and confirm the actual path.
- When the user gives a date-like folder name, search for variant spellings and punctuation (
6-2-26,6.2.26,6_2_26,6-2-2026) before assuming it does not exist.
- Inventory the documents
- Identify all files in the target folder.
- Group by extension and prioritize editable office docs (
.docx,.doc,.odt,.rtf,.txt) before PDFs or spreadsheets.
- Extract readable text
- For Word/Office files, extract the text in a way that preserves headings and paragraphs as much as possible.
- If the file-reading tool rejects
.docxas binary, do not classify the document as unreadable and do not repeat the same call across the batch. A DOCX is a ZIP package: validate it with Pythonzipfile, then usepython-docxwhen available or extractword/document.xmland convert paragraph/table XML to bounded text. Record package validity, paragraph/table counts, and extraction method. Use native Word or PDF rendering separately when layout—not just text—is under review. - When validating generated fixtures, compare extracted text against the fixture specification or authoritative
.txtsource before opening Word. A valid ZIP alone does not prove the fixture contains the intended content. - For scanned or image-based files, use OCR or an extraction tool first.
- If a file is not meaningfully text-reviewable after at least one appropriate format-specific fallback, say so explicitly instead of pretending to proofread it.
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 · 108 lines · 38 tokens per session scan A b6cc86779655
office-document-review is a skill published in the GitHub repository AtlasOmnia/donna-starter (110 stars, last pushed 11d ago), licensed MIT. It adds 38 tokens to every session and 1,221 once invoked, about $0.0002 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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