RRepoGEO

REPOGEO REPORT · LITE

iamfakeguru/agent-md

Default branch main · commit ae8117e9 · scanned 6/11/2026, 2:32:57 PM

GitHub: 953 stars · 189 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 iamfakeguru/agent-md, 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 the README's first paragraph to clarify purpose and audience

    Why:

    CURRENT
    `agent-md` installs one source-of-truth rules file, repo-local hooks, persistent task state, and a few helper scripts so agents can stop guessing and start proving their work.
    COPY-PASTE FIX
    iamfakeguru/agent-md provides production-grade, portable contracts and verifiable directives for autonomous coding agents (Claude Code, Codex, Cursor, Windsurf, Aider). It installs source-of-truth rules, repo-local hooks, and persistent task state, enabling agents to stop guessing and start proving their work, unlike generic linting tools or broad AI frameworks.
  • mediumhomepage#2
    Set a homepage URL for the repository

    Why:

    COPY-PASTE FIX
    Add `https://github.com/iamfakeguru/agent-md` as the homepage URL in the repository's 'About' section, or create a dedicated project website and link it there.

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 iamfakeguru/agent-md
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GitHub
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub · recommended 1×
  2. GitLab · recommended 1×
  3. Bitbucket · recommended 1×
  4. pylint/pylint · recommended 1×
  5. PyCQA/flake8 · recommended 1×
  • CATEGORY QUERY
    How can I standardize directives and enforce coding practices for AI development agents?
    you: not recommended
    AI recommended (in order):
    1. GitHub
    2. GitLab
    3. Bitbucket
    4. Pylint (pylint/pylint)
    5. Flake8 (PyCQA/flake8)
    6. Black (psf/black)
    7. ESLint (eslint/eslint)
    8. GitHub Actions
    9. GitLab CI/CD
    10. Jenkins (jenkinsci/jenkins)
    11. Sphinx (sphinx-doc/sphinx)
    12. MkDocs (mkdocs/mkdocs)
    13. Confluence
    14. pre-commit (pre-commit/pre-commit)
    15. Docker (moby/moby)
    16. Kubernetes (kubernetes/kubernetes)

    AI recommended 16 alternatives but never named iamfakeguru/agent-md. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to ensure autonomous coding agents follow instructions and verify their output?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Pydantic
    3. Guardrails AI
    4. Instructor
    5. Pytest
    6. Jest

    AI recommended 6 alternatives but never named iamfakeguru/agent-md. 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 iamfakeguru/agent-md?
    pass
    AI named iamfakeguru/agent-md explicitly

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

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

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

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iamfakeguru/agent-md — 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