RRepoGEO

REPOGEO REPORT · LITE

DataDog/pup

Default branch main · commit 841588cd · scanned 6/10/2026, 6:42:10 AM

GitHub: 871 stars · 85 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/pup, 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
    Clarify README's opening to explicitly counter AI's miscategorization

    Why:

    CURRENT
    Every AI agent needs a loyal companion. Meet Pup — the CLI that gives your agents full access to Datadog's observability platform (because even autonomous agents need good tooling, not just tricks).
    COPY-PASTE FIX
    Every AI agent needs a loyal companion. Meet Pup — the CLI that gives your agents full access to Datadog's observability platform (because even autonomous agents need good tooling, not just tricks). **Note: Pup is a Datadog-specific CLI for AI agents, not a general-purpose HTML parser or S3 Select tool.**
  • mediumtopics#2
    Add more specific topics to improve recall for AI agent and automation queries

    Why:

    CURRENT
    cli, client, datadog, observability
    COPY-PASTE FIX
    cli, client, datadog, observability, ai-agents, automation, programmatic-access
  • lowcomparison#3
    Add a 'Comparison' section to the README explaining Pup's advantage over direct API/SDK usage for AI agents

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## Pup vs. Direct API/SDKs' with content like: 'While Datadog offers comprehensive APIs and SDKs, Pup provides a streamlined, agent-ready CLI experience. It simplifies complex API interactions into discoverable commands, offers structured output for easy parsing, and enforces scoped access via OAuth2 + PKCE, making it ideal for autonomous AI workflows where direct API management can be cumbersome.'

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/pup
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Datadog API & SDKs
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Datadog API & SDKs · recommended 1×
  2. New Relic APIs (NerdGraph & REST API) · recommended 1×
  3. Prometheus HTTP API & Client Libraries · recommended 1×
  4. Grafana HTTP API & SDKs · recommended 1×
  5. Splunk REST API & SDKs · recommended 1×
  • CATEGORY QUERY
    How can AI agents programmatically access observability platform data and manage infrastructure?
    you: not recommended
    AI recommended (in order):
    1. Datadog API & SDKs
    2. New Relic APIs (NerdGraph & REST API)
    3. Prometheus HTTP API & Client Libraries
    4. Grafana HTTP API & SDKs
    5. Splunk REST API & SDKs
    6. Terraform
    7. Ansible
    8. Kubernetes API
    9. AWS SDKs
    10. Azure SDKs
    11. Google Cloud Client Libraries

    AI recommended 11 alternatives but never named DataDog/pup. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What CLI tools offer structured output for automating observability tasks with AI workflows?
    you: not recommended
    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/pup?
    pass
    AI named DataDog/pup 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/pup in production, what risks or prerequisites should they evaluate first?
    pass
    AI named DataDog/pup 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/pup solve, and who is the primary audience?
    pass
    AI named DataDog/pup 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/pup — 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