RRepoGEO

REPOGEO REPORT · LITE

wzdnzd/harvester

Default branch main · commit 19601ff4 · scanned 6/14/2026, 8:47:26 AM

GitHub: 558 stars · 113 forks

AI VISIBILITY SCORE
33 /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
2 / 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 wzdnzd/harvester, 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
  • hightopics#1
    Add broader, more descriptive topics

    Why:

    CURRENT
    ai, anthropic, deepseek, gemini, openai, qwen
    COPY-PASTE FIX
    data-acquisition, web-scraping, osint, threat-intelligence, github-api, shodan, fofa, framework, go, golang, data-collection
  • highreadme#2
    Reposition the README's opening paragraph to emphasize general purpose

    Why:

    CURRENT
    A universal, adaptive data acquisition framework designed for comprehensive information acquisition from multiple sources including GitHub, network mapping platforms (FOFA, Shodan), and arbitrary web endpoints. While the current implementation focuses on AI service provider key discovery as a practical example, the framework is architected for extensibility to support diverse data acquisition scenarios.
    COPY-PASTE FIX
    A universal, adaptive data acquisition framework designed for comprehensive information acquisition from multiple sources including GitHub, network mapping platforms (FOFA, Shodan), and arbitrary web endpoints. This framework is architected for extensibility to support diverse data acquisition scenarios, with a current practical example focusing on AI service provider key discovery.
  • mediumreadme#3
    Clarify the existing license(s) in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under [Specify License Name(s) here, e.g., 'a custom license combining MIT and Apache-2.0 terms']. Please refer to the [LICENSE](LICENSE) file 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 wzdnzd/harvester
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Apache Nifi
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Apache Nifi · recommended 1×
  2. Apache Airflow · recommended 1×
  3. Scrapy · recommended 1×
  4. Prefect · recommended 1×
  5. Luigi · recommended 1×
  • CATEGORY QUERY
    What are the best frameworks for building custom data acquisition pipelines from diverse web sources?
    you: not recommended
    AI recommended (in order):
    1. Apache Nifi
    2. Apache Airflow
    3. Scrapy
    4. Prefect
    5. Luigi
    6. Meltano

    AI recommended 6 alternatives but never named wzdnzd/harvester. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I automatically discover and monitor sensitive information exposed across public internet sources?
    you: not recommended
    AI recommended (in order):
    1. TruffleHog (trufflesecurity/trufflehog)
    2. GitGuardian
    3. Shodan
    4. Have I Been Pwned (HIBP) for Domains
    5. Google Dorks
    6. SpiderFoot (smicallef/spiderfoot)

    AI recommended 6 alternatives but never named wzdnzd/harvester. 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 wzdnzd/harvester?
    pass
    AI did not name wzdnzd/harvester — likely talking about a different project

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

  • If a team adopts wzdnzd/harvester in production, what risks or prerequisites should they evaluate first?
    pass
    AI named wzdnzd/harvester 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 wzdnzd/harvester solve, and who is the primary audience?
    pass
    AI named wzdnzd/harvester explicitly

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

Embed your GEO score

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wzdnzd/harvester — 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