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 ai-analyst-lab/ai-analyst --skill google-doc-exportgit clone --depth 1 https://github.com/ai-analyst-lab/ai-analystWrote 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/google-doc-export)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/google-doc-export"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/google-doc-export/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/ai-analyst-lab/ai-analyst/google-doc-export"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/google-doc-export.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 | $0.00192 | $0.04163 |
| Opus 5.5 | $0.00077 | $0.01665 |
| Sonnet 5.5 | $0.00038 | $0.00833 |
| Haiku 4.5 | $0.00019 | $0.00416 |
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 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 — 459 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Google Doc Export
Purpose
Create properly formatted Google Docs via the MCP API. 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) withhelpers/export/gdoc_builder.py - Simple text-only doc (meeting notes, memo) → Use direct MCP API (Section B)
- Non-analysis document (proposal, spec) → Check
helpers/INDEX.mdfor helpers, else 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. Use helpers/export/gdoc_builder.py to create .docx locally
# 2. Upload with conversion flag
upload_file_to_drive(
file_path="/path/to/report.docx",
convert_to_google_doc=True
)
# 3. Done! Returns Google Doc URL
Available MCP function: mcp__google-docs__upload_file_to_drive(file_path, convert_to_google_doc=True)
Alternative: Direct MCP API Calls (simple text-only docs)
When: Quick text-only docs with no images/tables (meeting notes, simple memos)
Which server: This repo's doc agents (google-doc-creator, google-doc-reviewer) target the google-workspace MCP server, whose Docs functions are create_doc, insert_doc_elements, insert_doc_image, inspect_doc_structure, batch_update_doc, update_paragraph_style, and get_doc_as_markdown. Drive uploads (upload_file_to_drive, upload_image_to_drive) come from the google-docs server. auth-preflight detects which servers are installed; call only functions the installed server exposes (Section F lists both).
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 · 459 lines · 192 tokens per session scan A a84ff039f9f9
google-doc-export is a skill published in the GitHub repository ai-analyst-lab/ai-analyst (322 stars, last pushed 1mo ago), licensed MIT. It adds 192 tokens to every session and 4,163 once invoked, about $0.0008 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 2026-09-12.
google-doc-export compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| google-doc-exportai-analyst-lab/ai-analyst | Skill | 322 | 1mo ago | MIT | A | 192 | Claude Code |
| orbit-notionnexu-io/open-design | Skill | 106k | yesterday | Apache-2.0 | A | 117 | Claude Code, Codex |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 2d ago | Apache-2.0 | A | 123 | Claude Code |
| apify-youtube-transcript-scrapersickn33/agentic-awesome-skills | Skill | 49k | yesterday | MIT | A | 48 | Claude Code |
| legal-holdanthropics/claude-for-legal | Skill | 9.9k | 3d ago | Apache-2.0 | A | 68 | Claude Code |
| ppt-reviewOrkas-AI/Orkas | Skill | 9.0k | yesterday | MIT | A | 2 | Claude Code, Codex |
| feishuHmbown/CodeWhale | Skill | 41k | 5d ago | MIT | A | 33 | Claude Code, Codex |