RRepoGEO

REPOGEO REPORT · LITE

solatis/claude-config

Default branch main · commit 6e72bc29 · scanned 6/7/2026, 2:28:17 PM

GitHub: 851 stars · 136 forks

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 solatis/claude-config, 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 paragraph to clarify it's a workflow/methodology

    Why:

    CURRENT
    I use Claude Code for most of my work. After months of iteration, I noticed a pattern: LLM-assisted code rots faster than hand-written code. Technical debt accumulates because the LLM does not know what it does not know, and neither do you until it is too late. This repo is my solution: skills and workflows that force planning before execution, keep context focused, and catch mistakes before they compound.
    COPY-PASTE FIX
    This repository presents a **proven workflow and methodology** for software developers leveraging Claude Code, specifically engineered to prevent technical debt and maintain high code quality in LLM-assisted projects. It provides practical skills and strategies for managing context, enforcing planning, and proactively catching errors, directly addressing the unique challenges of integrating large language models into the development process.
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm-workflow, code-quality, technical-debt-prevention, ai-assisted-development, claude-ai, prompt-engineering, software-development-methodology
  • mediumabout#3
    Clarify the repository description

    Why:

    CURRENT
    My claude code customizations / config.
    COPY-PASTE FIX
    A structured workflow and methodology for preventing technical debt and maintaining code quality in LLM-assisted software development with Claude AI.

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 solatis/claude-config
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepSource
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepSource · recommended 2×
  2. pytest-dev/pytest · recommended 2×
  3. prettier/prettier · recommended 2×
  4. eslint/eslint · recommended 2×
  5. psf/black · recommended 2×
  • CATEGORY QUERY
    How to maintain code quality when using AI for software development?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot Enterprise
    2. DeepSource
    3. SonarQube
    4. Cypress (cypress-io/cypress)
    5. Playwright (microsoft/playwright)
    6. JUnit (junit-team/junit5)
    7. NUnit (nunit/nunit)
    8. Pytest (pytest-dev/pytest)
    9. Prettier (prettier/prettier)
    10. ESLint (eslint/eslint)
    11. Black (psf/black)
    12. Git
    13. GitHub
    14. GitLab
    15. Bitbucket
    16. Code Climate
    17. Tabnine
    18. Pylint (pylint-dev/pylint)

    AI recommended 18 alternatives but never named solatis/claude-config. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Strategies to prevent technical debt with AI code generation tools?
    you: not recommended
    AI recommended (in order):
    1. ESLint (eslint/eslint)
    2. Black (psf/black)
    3. Prettier (prettier/prettier)
    4. GitHub Pull Requests
    5. GitLab Merge Requests
    6. Bitbucket Pull Requests
    7. Jira
    8. Asana
    9. Trello
    10. Jest (facebook/jest)
    11. Pytest (pytest-dev/pytest)
    12. JUnit
    13. NUnit (NUnit/NUnit)
    14. Jenkins (jenkinsci/jenkins)
    15. GitHub Actions
    16. GitLab CI/CD
    17. SonarQube (SonarSource/sonarqube)
    18. DeepSource
    19. CodeClimate
    20. Git (git/git)
    21. Gitflow
    22. GitHub Flow

    AI recommended 22 alternatives but never named solatis/claude-config. 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 solatis/claude-config?
    pass
    AI named solatis/claude-config explicitly

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

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

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

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