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 agentmods add skills/ai-analyst-lab/ai-analyst-plugin/google-doc-exportnpx skills add ai-analyst-lab/ai-analyst-plugin --skill google-doc-exportgit clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst-pluginWrote 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/ai-analyst-lab/ai-analyst-plugin/google-doc-export)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst-plugin/google-doc-export"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plugin/google-doc-export.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 | $0.00083 | $0.04300 |
| Opus 5 | $0.00042 | $0.02150 |
| Sonnet 5 | $0.00017 | $0.00860 |
| Haiku 4.5 | $0.00008 | $0.00430 |
Grade A, and why
google-doc-export 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 5d 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 — 473 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Google Doc Export
Requires the Google Docs connector enabled in Cowork.
Tool discovery. Exact tool names differ by environment; never hardcode them. Discover the connector's actual tools in-session and read each tool's own schema before calling it. This skill names capabilities (upload-file-to-Drive with Google Doc conversion, create document, append text, write formatted content, insert image, upload image to Drive, read document); the discovered tool's schema is the authority on names and parameters. If a needed capability has no tool, stop and tell the user to enable the connector; never fake the export. What a given environment's wrappers accept cannot be verified from this file without a live connector, which is exactly why you discover and read schemas in-session.
Purpose
Create properly formatted Google Docs through the connector. Prevents common issues: text/image overlap, broken heading hierarchy, excessive whitespace, inconsistent formatting.
Section 0: Quick Decision Tree — START HERE
Step 1: What type of document are you creating?
- Analysis report/writeup → Use
.docx → Google Docsworkflow (Section A), building the.docxwith python-docx per the Analysis Readout rules - Simple text-only doc (meeting notes, memo) → Use direct connector calls (Section B)
- Non-analysis document (proposal, spec) → Use python-docx directly
Step 2: Choose your approach based on document type:
✅ Recommended: .docx → Google Docs Conversion (use for 90% of cases)
When: Any doc with charts, tables, or complex formatting (analysis reports, writeups)
Why: Most reliable. Avoids index calculation errors, handles images/tables automatically, always creates local backup.
How:
# 1. Build the .docx locally with python-docx (see Section A Step 1)
# 2. Upload with the connector's upload-file-to-Drive capability, conversion flag on
<upload file to drive tool>(
file_path="<path to report.docx>",
convert_to_google_doc=True
)
# 3. Done! Returns Google Doc URL
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.
- 5d ago First seen · 473 lines · 83 tokens per session scan A d6821a6ea2da
google-doc-export is a skill published in the GitHub repository ai-analyst-lab/ai-analyst-plugin (32 stars, last pushed 9d ago), licensed MIT. It adds 83 tokens to every session and 4,300 once invoked, about $0.0004 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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