RRepoGEO

REPOGEO REPORT · LITE

code-yeongyu/lazycodex

Default branch main · commit 7bbf9d78 · scanned 6/12/2026, 11:56:57 PM

GitHub: 954 stars · 50 forks

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 code-yeongyu/lazycodex, 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 core value proposition before introducing OmO

    Why:

    CURRENT
    The current README immediately follows the H1/description with a `NOTE` about `[OmO] 60K Stars`.
    COPY-PASTE FIX
    Insert a clear, concise paragraph *before* the OmO note, explicitly stating what LazyCodex *is* and *does* for its target audience, e.g., 'LazyCodex is a powerful CLI tool designed for software developers to orchestrate AI agents for complex code generation tasks, offering project memory, planning, execution, and verified completion specifically within the Codex environment.'
  • mediumtopics#2
    Add more specific functional topics to improve categorization

    Why:

    CURRENT
    ai, ai-agents, claude, claude-code, cli, codex, developer-tools, lazy, lazycodex, oh-my-openagent, omo, openai, orchestration, typescript
    COPY-PASTE FIX
    ai, ai-agents, claude, claude-code, cli, codex, developer-tools, lazy, lazycodex, oh-my-openagent, omo, openai, orchestration, typescript, code-generation, software-development, verified-completion, planning, execution
  • lowabout#3
    Explicitly mention 'CLI tool' in the repository description

    Why:

    CURRENT
    The one and only agent harness for complex codebases. Project memory, planning, execution, and verified completion inside Codex.
    COPY-PASTE FIX
    The one and only CLI agent harness for complex codebases. Project memory, planning, execution, and verified completion inside Codex.

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 code-yeongyu/lazycodex
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 1×
  2. Significant-Gravitas/AutoGPT · recommended 1×
  3. yoheinakajima/babyagi · recommended 1×
  4. joaomdmoura/crewAI · recommended 1×
  5. microsoft/autogen · recommended 1×
  • CATEGORY QUERY
    How can I orchestrate AI agents for complex software development tasks with verified completion?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. AutoGPT (Significant-Gravitas/AutoGPT)
    3. BabyAGI (yoheinakajima/babyagi)
    4. CrewAI (joaomdmoura/crewAI)
    5. Microsoft AutoGen (microsoft/autogen)
    6. Hugging Face Transformers Agents (huggingface/transformers)
    7. ESLint (eslint/eslint)
    8. Black (psf/black)
    9. MyPy (python/mypy)
    10. Pytest (pytest-dev/pytest)
    11. Jest (facebook/jest)
    12. JUnit (junit-team/junit5)
    13. Cypress (cypress-io/cypress)
    14. SonarQube (SonarSource/sonarqube)

    AI recommended 14 alternatives but never named code-yeongyu/lazycodex. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a CLI tool to manage AI code generation agents with planning and execution.
    you: not recommended
    AI recommended (in order):
    1. Auto-GPT
    2. BabyAGI
    3. LangChain CLI
    4. CrewAI
    5. Open Interpreter
    6. GPT Engineer

    AI recommended 6 alternatives but never named code-yeongyu/lazycodex. 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 code-yeongyu/lazycodex?
    pass
    AI named code-yeongyu/lazycodex explicitly

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

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

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

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code-yeongyu/lazycodex — 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