RRepoGEO

REPOGEO REPORT · LITE

peteromallet/dataclaw

Default branch main · commit 68268ef2 · scanned 6/23/2026, 4:17:08 PM

GitHub: 2,101 stars · 235 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
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 peteromallet/dataclaw, 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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition README's core purpose statement

    Why:

    CURRENT
    Turn your Claude Code, Codex, and other coding-agent conversation history into structured data and publish it to Hugging Face with a single command. DataClaw parses session logs, redacts secrets and PII, and uploads the result as a ready-to-use dataset.
    COPY-PASTE FIX
    DataClaw is a tool to export your AI assistant coding conversations (from Claude Code, Codex, and other coding agents) into structured, shareable datasets, ready for publication on Hugging Face. It parses session logs, redacts secrets and PII, and uploads the result as a ready-to-use dataset.
  • mediumhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/peteromallet/dataclaw

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 peteromallet/dataclaw
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
VS Code
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. VS Code · recommended 1×
  2. Sublime Text · recommended 1×
  3. Notepad++ · recommended 1×
  4. ChatGPT · recommended 1×
  5. ChatGPT Export & Share · recommended 1×
  • CATEGORY QUERY
    How can I export my AI assistant coding conversations into a shareable dataset?
    you: not recommended
    AI recommended (in order):
    1. VS Code
    2. Sublime Text
    3. Notepad++
    4. ChatGPT
    5. ChatGPT Export & Share
    6. Save ChatGPT
    7. Python
    8. Selenium
    9. Playwright
    10. OpenAI API
    11. Pandas

    AI recommended 11 alternatives but never named peteromallet/dataclaw. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to anonymize and publish my programming assistant chat logs as structured data?
    you: not recommended
    AI recommended (in order):
    1. Presidio (microsoft/presidio)
    2. Google Cloud Data Loss Prevention (DLP) API
    3. Anonymize (sveetch/anonymize)
    4. Faker (joke2k/faker)
    5. Splunk

    AI recommended 5 alternatives but never named peteromallet/dataclaw. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 peteromallet/dataclaw?
    pass
    AI named peteromallet/dataclaw explicitly

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

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

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

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peteromallet/dataclaw — 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