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 cxcscmu/SkillLearnBench --skill office-extractiongit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWrote 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/cxcscmu/skilllearnbench/office-extraction)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/office-extraction"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/office-extraction.svg" alt="Measured on agentmods" 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 | $0.00018 | $0.00256 |
| Opus 5 | $0.00009 | $0.00128 |
| Sonnet 5 | $0.00004 | $0.00051 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
office-extraction 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 3d 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.
What it actually says
Office Extraction Skill
This skill provides methods for extracting text from Microsoft Office documents (.docx, .pptx).
Methods
1. Using docx2txt or pandoc
If installed, these tools can convert Office docs to text.
# For DOCX
pandoc -t plain "document.docx" -o "output.txt"
2. Using Gemini CLI docx and pptx Skills
These specialized skills provide direct tools for document manipulation.
// For DOCX
await docx.read_file({ file_path: "paper.docx" });
// For PPTX
await pptx.read_presentation({ file_path: "slides.pptx" });
3. Manual Extraction (unzip)
DOCX and PPTX are ZIP archives. You can unzip them and read the XML.
unzip -p "document.docx" word/document.xml | sed -e 's/<[^>]*>//g'
Usage Pattern
Extract the main body text or titles to identify the document's subject matter.
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.
- 3d ago First seen · 40 lines · 18 tokens per session scan A afebbaed5966
office-extraction is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 1mo ago), licensed MIT. It adds 18 tokens to every session and 256 once invoked, about $0.0001 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 2026-09-03.
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