Agent skill

Google Doc Export

by ai-analyst-lab in ai-analyst-lab/ai-analyst

Create properly formatted Google Docs via the MCP API. An agent skill from ai-analyst-lab/ai-analyst.

MITAuto-check passedDocuments & Office

Install Google Doc Export

skills CLI
$ npx skills add ai-analyst-lab/ai-analyst --skill google-doc-export -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ai-analyst-lab/ai-analyst google-doc-export --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/google-doc-export .claude/skills/google-doc-export && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
google-doc-export
GitHub stars
304
Token cost
~4.3k tokens
SKILL.md length
1,187 words
Files
1
Skills in repo
43
Repo updated
First seen
Licence
MIT

At a glance

Create properly formatted Google Docs via the MCP API. An agent skill from ai-analyst-lab/ai-analyst.

  • Works in 3 steps: Generate .docx Locally → Upload with Conversion → Confirm Deliverables
  • The user wants to create a Doc
  • SKILL.md covers Purpose, Section 0: Quick Decision Tree…, Section A: Using the .docx →… and Section B: Direct MCP API…, plus 6 more sections
  • Reaches docs.google.com

What it does

Google Doc Export is an agent skill from ai-analyst-lab/ai-analyst. Create properly formatted Google Docs via the MCP API. This skill prevents common issues like text/image overlap, broken heading hierarchy, excessive whitespace, and inconsistent formatting. Use this skill automatically whenever you're building a Google Doc, calling any Google Docs MCP tool on the google-workspace server (createdoc, insertdocelements, insertdocimage, batchupdatedoc) or the google-docs server (uploadfiletodrive, writeformattedcontent), designing a document structure, or when the google-doc-creator…

Its SKILL.md is about 4.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Documents & Office, covering Cloud office suites. It works with Google Docs, Model Context Protocol, Google Workspace and Microsoft Word. The repository describes itself as: AI Product Analyst — Claude Code-powered data analysis toolkit. The licence is MIT.

When your agent uses it

  • The user wants to create a Doc
  • Export to Google Docs
  • Share analysis as a Doc
  • Build a formatted document

Example prompts

  • “/google-doc-export”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Generate .docx Locally
  2. Upload with Conversion
  3. Confirm Deliverables

What it can do on your machine

Read from SKILL.md and the folder at commit 52c0744. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • docs.google.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Google Doc Export loads about 4.3k tokens when it runs. Until then it costs about 252 tokens; SKILL.md has 1,187 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~252
When it runs · the whole SKILL.md, loaded when a task matches
~4.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ai-analyst-lab/ai-analyst at commit 52c0744, republished under its MIT licence (© ai-analyst-lab). 1,187 words, ~4,331 tokens.

Download SKILL.mdSave it as .claude/skills/google-doc-export/SKILL.md (or your agent's skills folder).
name
google-doc-export
description
Create properly formatted Google Docs via the MCP API. This skill prevents common issues like text/image overlap, broken heading hierarchy, excessive whitespace, and inconsistent formatting. Use this skill automatically whenever you're building a Google Doc, calling any Google Docs MCP tool on the google-workspace server (create_doc, insert_doc_elements, insert_doc_image, batch_update_doc) or the google-docs server (upload_file_to_drive, write_formatted_content), designing a document structure, or when the google-doc-creator or google-doc-reviewer agent is running. This skill is essential for ANY workflow involving Google Docs creation, document formatting, analysis writeups in Google Docs, report generation to Docs, chart embedding in documents, or exporting analysis results to shareable Docs. Make sure to use this skill whenever the user wants to create a Doc, export to Google Docs, share analysis as a Doc, build a formatted document, or mentions Google Docs in any capacity.

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 Docs workflow (Section A) with helpers/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.md for helpers, else use python-docx directly

Step 2: Choose your approach based on document type:

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:

python
# 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).


This is the easiest and most reliable approach for complex documents.

Step 1: Generate .docx Locally

IMPORTANT: Always check for existing helpers before writing .docx code from scratch.

Option 1A: Use helpers/export/gdoc_builder.py (PREFERRED for analysis documents)

When to use: Creating analysis reports, findings writeups, or any document following the Analysis Readout template (Context → Summary → Analysis → Next Steps → Resources).

Why: Pre-built, tested, handles all formatting automatically. Don't reinvent the wheel.

python
from helpers.export.gdoc_builder import build_readout

# Build structured analysis document
doc_data = {
    "title": "Q1 Analysis",
    "findings": [...],  # Your analysis content
    "charts": ["/path/to/chart1.png", "/path/to/chart2.png"]
}

docx_path = build_readout(doc_data)  # Returns path to .docx file

The builder automatically applies:

  • Proper heading hierarchy (H1 → H2 → H3 → H4)
  • Bold labels ("The Insight:", "Why this matters for product:")
  • Chart embedding at 6 inches wide with captions
  • Figure numbering
  • Professional spacing
  • Analysis Readout template structure
Option 1B: Use python-docx directly (ONLY if no helper exists)

When to use: Creating non-analysis documents (proposals, specs, design docs) that don't fit the Analysis Readout template.

Requirements:

  • Check helpers/INDEX.md first to verify no helper exists for your use case
  • If building from scratch, create the .docx with proper heading hierarchy
  • Always save to the repo's outputs/ directory
  • Use descriptive filename with date suffix: report_[title]_[YYYYMMDD].docx

Example:

python
from docx import Document
from docx.shared import Inches, Pt
from docx.enum.text import WD_ALIGN_PARAGRAPH

doc = Document()
# Add title
title = doc.add_heading('Document Title', level=1)
# Add content sections...
# Add charts
doc.add_picture('/path/to/chart.png', width=Inches(6))
# Save
doc.save('outputs/report_title_20260404.docx')
Step 2: Upload with Conversion

CRITICAL: The local .docx file IS your backup. Do not delete it.

python
result = mcp__google-docs__upload_file_to_drive(
    file_path=docx_path,
    convert_to_google_doc=True
)

# Returns: {"file_id": "...", "url": "https://docs.google.com/document/d/..."}
Step 3: Confirm Deliverables

You now have TWO deliverables (always provide both to the user):

  1. Live Google Doc - result["url"]

    • Editable, shareable, lives in Google Drive
    • Charts embedded permanently (no expiration)
  2. Local backup - docx_path

    • Archival copy in /outputs/ directory
    • Useful for version control, offline access
    • REQUIRED: Always mention both the Google Doc URL AND the local file path in your response to the user
Why This Works Better

Google's .docx converter handles:

  • Image placement (no index calculation needed)
  • Table creation (no manual cell population)
  • Bold/italic/heading styles
  • Spacing and layout

No risk of index invalidation, no image timing issues, no expiring image URLs.


Section B: Direct MCP API Approach (Simple Docs Only)

For simple text-only documents, you can use MCP functions directly.

Create and Populate
python
# 1. Create blank doc
result = mcp__google-docs__create_document(title="Meeting Notes")
doc_id = result["document_id"]

# 2. Add formatted content
content_blocks = [
    {"type": "heading1", "text": "Meeting Notes\n"},
    {"type": "body", "text": "Attendees: Alice, Bob\n\n"},
    {"type": "heading2", "text": "Discussion Points\n"},
    {"type": "body", "text": "We reviewed the Q1 results...\n"}
]

mcp__google-docs__write_formatted_content(
    document_id=doc_id,
    content_blocks=json.dumps(content_blocks)
)
Insert Images (if needed)
python
# 1. Upload image to Drive first
image_result = mcp__google-docs__upload_image_to_drive(
    file_path="/path/to/chart.png"
)
image_url = image_result["url"]

# 2. Read doc to find insertion index
doc_content = mcp__google-docs__read_document(document_id=doc_id)
# Find the index where you want the image

# 3. Insert image with BOTH width and height
mcp__google-docs__insert_image(
    document_id=doc_id,
    image_url=image_url,
    width_pts=400,
    height_pts=300  # REQUIRED - calculate from aspect ratio if needed
)

Critical: Always specify BOTH width_pts and height_pts. Omitting height causes API error.


Section C: Document Structure Standards

Standard Analysis Document Template

Use this structure for analysis reports:

  • Text inserted before images — all text content must be in the doc before any image insertion. Images shift all indices.
  • Images in dedicated paragraphs — every image gets its own paragraph. Never insert an image into a paragraph that already contains text.
  • Bottom-to-top image insertion — insert the last section's image first, then work backwards. Prevents index invalidation.
  • Heading hierarchy is clean — exactly one H1, H2 for sections, H3 for subsections. No skipped levels.
  • No more than 2 consecutive empty paragraphs anywhere in the document.
  • Drive file IDs used for images — never public-host URLs (they expire and leak data).
  • Table spacing — every table must have 1 empty paragraph before and after it. Text must never run directly into a table or start immediately after one.
  • No stub headings — never insert a heading without body content beneath it. If data for a section doesn't exist, omit the heading entirely.
  • Both width AND height specified for images — insert_image requires both; omitting height is an API error.

Section B: Document Structure Template

Standard Analysis Document
H1: [Document Title]
    [Subtitle — scope, date, author]

H2: Executive Summary
    [3-5 sentence overview]
    [Numbered key findings — max 3]
    [Bottom line statement]

H2: Section 1: [Topic]
    [Chart image — centered, 400pt wide]
    [The Insight: bold label + finding]
    [Supporting evidence paragraphs]
    [Why this matters for product: bold label + implication]

H2: Section 2: [Topic]
    ... (repeat pattern)

H2: Data Quality and Limitations
    [Outlier investigation]
    [Sample size notes]
    [Methodology caveats]

H2: Recommendations
    [Numbered list of actionable recommendations]
    [Each with a bold title + explanation paragraph]

H2: Appendix
    [Summary statistics tables]
Section Spacing Rules
After H1:          2 empty paragraphs
After H2:          1 empty paragraph
Before chart:      1 empty paragraph
After chart:       1 empty paragraph
Before table:      1 empty paragraph
After table:       1 empty paragraph
Between sections:  2 empty paragraphs (includes the pre-H2 spacing)
Between paragraphs: 0 empty paragraphs (natural paragraph spacing)
After bullet list:  1 empty paragraph

Spacing Rules
After H1:          2 empty paragraphs
After H2:          1 empty paragraph
Before chart:      1 empty paragraph
After chart:       1 empty paragraph
Before table:      1 empty paragraph
After table:       1 empty paragraph
Between sections:  2 empty paragraphs
Between paragraphs: 0 empty paragraphs (natural spacing)
After bullet list:  1 empty paragraph
Bold Labels (Auto-Applied by gdoc_builder)

These phrases should always be bold when they appear at the start of a paragraph:

  • "The Insight:"
  • "Why this matters for product:"
  • "Bottom line:"
  • "Key context:"
  • "Data quality flag:"
  • "Sample size warning:"

Section D: Image Sizing Reference

Standard chart:     width=400, height=300  (4:3 ratio)
Wide chart:         width=500, height=280  (16:9 ratio)
Square chart:       width=350, height=350  (1:1 ratio)
Small inline:       width=250, height=200  (for side notes)

Always specify both width and height. If only one dimension is known, calculate the other from the image's aspect ratio.


Show full SKILL.md (522 more words)Show less

Section E: Common Pitfalls

PitfallWhat happensPrevention
Use a public file-host URLExpires quickly and leaks dataUpload to Drive first or use .docx embed
Omit height in insert_imageAPI error: "height must be greater than 0"Always specify both width AND height
Call a function from the other MCP serverTool not found errorSection F lists each server's functions; auth-preflight reports which is installed
No local backupDoc only exists in Google's cloudUse .docx → Google Docs conversion
Complex doc via API callsIndex errors, image placement failuresUse .docx conversion instead
Too many empty paragraphsExcessive whitespace, unprofessionalMax 2 consecutive empty paragraphs
Stub headings with no bodyOrphaned headings confuse readersOnly insert headings that have content beneath

Section F: Quick Reference - Available MCP Functions

Two MCP servers appear in this repo. Use the one that is installed (auth-preflight reports it); do not mix a function from one with a document created on the other.

python
# google-workspace server — used by google-doc-creator and google-doc-reviewer
mcp__google-workspace__create_doc(title) → {"document_id": str}
mcp__google-workspace__insert_doc_elements(document_id, elements)      # text, headings, tables
mcp__google-workspace__insert_doc_image(document_id, image_url, index, width, height)
mcp__google-workspace__inspect_doc_structure(document_id, detailed=True) # indices for edits
mcp__google-workspace__batch_update_doc(document_id, requests)          # raw Docs API batch
mcp__google-workspace__update_paragraph_style(document_id, ...)
mcp__google-workspace__get_doc_as_markdown(document_id)

# google-docs server — simple text-only docs and Drive uploads
mcp__google-docs__create_document(title) → {"document_id": str}
mcp__google-docs__read_document(document_id) → str
mcp__google-docs__append_text(document_id, text) → status
mcp__google-docs__write_formatted_content(document_id, content_blocks) → status
mcp__google-docs__insert_image(document_id, image_url, width_pts, height_pts) → status
mcp__google-docs__upload_image_to_drive(file_path, file_name) → {"file_id": str, "url": str}
mcp__google-docs__upload_file_to_drive(file_path, convert_to_google_doc) → {"file_id": str, "url": str}

The .docx → Google Docs workflow (Section A) needs only upload_file_to_drive and is the recommended path for any document with tables or images.


Section G: Citation Pattern & Provenance Appendix

When creating analysis documents with findings, embed provenance data at three levels:

Level 1: Data Stamps (Always Present)

Every finding paragraph must include a data stamp inline, immediately after the finding title or key claim:

**Finding 1: Mobile converts at half the rate of desktop**
[50K rows | Jan-Mar 2026 | EVENTS | Confidence: B (82/100)]

Data stamps are built via helpers/provenance/provenance_assembler.py:

python
from helpers.provenance.provenance_assembler import build_data_stamp, render_data_stamp

stamp = build_data_stamp(
    row_count=50000,
    date_range="Jan-Mar 2026",
    primary_table="EVENTS",
    confidence_grade="B",
    confidence_score=82,
)
# stamp["one_liner"] = "[50K rows | Jan-Mar 2026 | EVENTS | Confidence: B (82/100)]"

In .docx via gdoc_builder.py, data stamps render as a small italic paragraph below each finding heading. In direct MCP mode, insert as body text with 9pt font and muted gray color.

For Tier 2+ analyses, add citation markers and a provenance appendix.

Two-pass approach:

Pass 1 — Build content:

  1. For each finding, insert a citation marker [F1] after the data stamp

  2. At the end of the document (before any existing Appendix), add:

    H2: Provenance Appendix
    
    H3: F1: Mobile converts at half the rate
    **Data:** [50K rows | Jan-Mar 2026 | EVENTS | Confidence: B (82/100)]
    **Methodology:** segmented comparison, COUNT by device
    **SQL:**
    ```sql
    SELECT device, COUNT(*) FROM events GROUP BY device

    Cross-verification: Type B: Parts-to-whole — Verified (PASS, diff 0.2%)

    H3: F2: ...

Pass 2 — Link citations (.docx workflow only): After building the .docx via gdoc_builder.py, the builder automatically creates:

  • Bookmark anchors on each H3 in the Provenance Appendix (named F1, F2, etc.)
  • Hyperlinks from [F1] markers in the body to the corresponding bookmark

For direct MCP mode, citation links are not possible (the API doesn't support internal bookmarks). Use the [F1] text markers without hyperlinks — the reader can scroll to the appendix.

For Tier 3 analyses, add a link to the analysis receipt at the bottom of the document:

H2: Analysis Receipt
Full audit trail with all queries, methodology, and reproducibility data:
→ outputs/analysis_receipt_{DATASET}_{DATE}.md
Building Provenance Blocks

All provenance data comes from helpers/provenance/provenance_assembler.py:

python
from helpers.provenance.provenance_assembler import build_provenance_blocks, render_provenance_appendix

blocks = build_provenance_blocks(
    findings=findings_list,          # from narrative parser
    confidence_result=confidence,    # from validation
    cross_verification=cv_data,      # from cross-verification YAML
    connection_type="snowflake",
    database="ANALYTICS",
)

# Render each block as markdown for the appendix
for block in blocks:
    appendix_md = render_provenance_appendix(block)
Checklist for Citation-Enabled Documents
  • Every finding has a data stamp (even without citation links)
  • Citation markers [F1], [F2] appear after each data stamp (Tier 2+)
  • Provenance Appendix section exists with one H3 per finding (Tier 2+)
  • Each appendix entry has: data stamp, methodology, SQL (if available), cross-verification (if available)
  • Bookmark links resolve correctly in .docx output (Tier 2+)
  • Receipt link present at document end (Tier 3 only)

© ai-analyst-lab, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/google-doc-export of ai-analyst-lab/ai-analyst.

Open the folder on GitHubat commit 52c0744

Compare with similar skills

Google Doc Export next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Google Doc Export this skillai-analyst-lab/ai-analyst304—~4.3kAutomated safety check: PassMIT
Managing Google Workspacetaylorwilsdon/google_workspace_mcp3.3k—~2.9kAutomated safety check: PassMIT
Google Docssanjay3290/ai-skills431—~636Automated safety check: PassApache-2.0
Gwskv0906/pm-kit138—~1.6kAutomated safety check: PassMIT
Google Workspacemitsuhiko/agent-stuff3.2k—~919Automated safety check: PassApache-2.0
Gdoc To Markdowniurykrieger/claude-bedrock1051 repos~3.8kAutomated safety check: NotesMIT

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Questions about Google Doc Export

What does Google Doc Export do?

Create properly formatted Google Docs via the MCP API. An agent skill from ai-analyst-lab/ai-analyst. Google Doc Export is an agent skill from ai-analyst-lab/ai-analyst. Create properly formatted Google Docs via the MCP API.

When should I use Google Doc Export?

Google Doc Export fits situations like: the user wants to create a Doc; export to Google Docs; share analysis as a Doc; build a formatted document.

How do I install Google Doc Export in Claude Code?

Run `npx skills add ai-analyst-lab/ai-analyst --skill google-doc-export -a claude-code`. Or copy the skill folder (.claude/skills/google-doc-export in ai-analyst-lab/ai-analyst) into .claude/skills/google-doc-export in your project. Claude Code loads it when a task matches its description.

How do I install Google Doc Export in Codex?

Run `npx skills add ai-analyst-lab/ai-analyst --skill google-doc-export -a codex`. Or copy the skill folder (.claude/skills/google-doc-export in ai-analyst-lab/ai-analyst) into .agents/skills/google-doc-export in your project. Codex loads it when a task matches its description.

Can I use Google Doc Export in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ai-analyst-lab/ai-analyst --skill google-doc-export -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/google-doc-export, .gemini/skills/google-doc-export, .github/skills/google-doc-export and .opencode/skills/google-doc-export in your project.

What does Google Doc Export need to run?

SKILL.md names no scripts, command-line tools or credentials: Google Doc Export is instructions for the agent only. Our summary lists: Python 3.

Does Google Doc Export access the network?

SKILL.md names 1 domain. In commands or code: docs.google.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Google Doc Export safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Google Doc Export use?

Google Doc Export is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Google Doc Export use?

About 4.3k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Google Doc Export?

Skills that share tags, products or a category with Google Doc Export: Managing Google Workspace (taylorwilsdon/google_workspace_mcp, 3.3k stars), Google Docs (sanjay3290/ai-skills, 431 stars), Gws (kv0906/pm-kit, 138 stars) and Google Workspace (mitsuhiko/agent-stuff, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Google Doc Export?

ai-analyst-lab (a GitHub organization) maintains it in ai-analyst-lab/ai-analyst, which has 304 GitHub stars. The repository holds 43 skills in this directory. The repository was last updated on September 30, 2026.

Source: ai-analyst-lab/ai-analyst on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.