RRepoGEO

REPOGEO REPORT · LITE

DataDog/documentation

Default branch master · commit 216a059e · scanned 6/9/2026, 8:47:02 PM

GitHub: 606 stars · 1,299 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface DataDog/documentation, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening to highlight its role as a large-scale documentation portal example

    Why:

    CURRENT
    Welcome to the Datadog documentation repository. The markdown stored in this repo is published to the [Datadog documentation site][17] using [hugo][1], a static website generation tool.
    COPY-PASTE FIX
    Welcome to the Datadog documentation repository, a large-scale, collaborative documentation portal built with [Hugo][1], a leading static website generation tool. This repository contains the markdown source for the official [Datadog documentation site][17], serving as a real-world example of managing extensive technical documentation.
  • mediumtopics#2
    Add more specific topics related to documentation platforms and static site generation

    Why:

    CURRENT
    datadog, documentation, hacktoberfest, hugo, wiki
    COPY-PASTE FIX
    datadog, documentation, hacktoberfest, hugo, wiki, static-site-generator, documentation-platform, content-management
  • lowlicense#3
    Clarify the repository's license in the README

    Why:

    COPY-PASTE FIX
    This repository's content is licensed under [insert specific license name(s) here, e.g., 'the Datadog Documentation License and Apache 2.0 for code examples']. Please see the [LICENSE file](LICENSE) for full details.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface DataDog/documentation
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MkDocs
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. MkDocs · recommended 2×
  2. Docusaurus · recommended 2×
  3. Jekyll · recommended 2×
  4. Material for MkDocs · recommended 1×
  5. Hugo · recommended 1×
  • CATEGORY QUERY
    How can I easily publish technical documentation for my project using markdown?
    you: not recommended
    AI recommended (in order):
    1. MkDocs
    2. Material for MkDocs
    3. Docusaurus
    4. Jekyll
    5. Hugo
    6. Read the Docs
    7. Sphinx

    AI recommended 7 alternatives but never named DataDog/documentation. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good static site generators for creating collaborative, version-controlled documentation portals?
    you: not recommended
    AI recommended (in order):
    1. Docsify
    2. Docusaurus
    3. MkDocs
    4. Gatsby
    5. Next.js
    6. MDX
    7. Jekyll

    AI recommended 7 alternatives but never named DataDog/documentation. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of DataDog/documentation?
    pass
    AI named DataDog/documentation explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts DataDog/documentation in production, what risks or prerequisites should they evaluate first?
    pass
    AI named DataDog/documentation explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo DataDog/documentation solve, and who is the primary audience?
    pass
    AI named DataDog/documentation explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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DataDog/documentation — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite