RRepoGEO

REPOGEO REPORT · LITE

GanyuanRan/Aegis

Default branch main · commit be553ee3 · scanned 6/16/2026, 9:11:34 PM

GitHub: 529 stars · 28 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 GanyuanRan/Aegis, 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 to explicitly state its purpose for AI coding agents

    Why:

    CURRENT
    # Aegis

<p align="center">
    <strong>Aegis Method Pack</strong><br/>
    面向 AI 编程 agent 的 baseline-first、evidence-driven 工作流程纪律包。
</p>
    COPY-PASTE FIX
    # Aegis: A Method Pack for Architecture-Aware AI Coding Agents

Aegis helps make AI coding agents architecture-aware: baseline-first, evidence-verified, drift-checked, and safe across long tasks.
  • mediumtopics#2
    Expand topics to include more specific AI agent development keywords

    Why:

    CURRENT
    add, agent-skills, ai-agents, ai-coding, architecture-driven-development, baseline-first, claude-code, codex, coding-agents, evidence-driven, first-principles, opencode, software-architecture, superpowers, tdd
    COPY-PASTE FIX
    add, agent-skills, ai-agents, ai-coding, architecture-driven-development, baseline-first, claude-code, codex, coding-agents, evidence-driven, first-principles, opencode, software-architecture, superpowers, tdd, llm-agents, agent-framework, ai-workflow, agent-orchestration, multi-agent-systems, agent-development, reliable-ai, robust-ai
  • lowhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://dev.to/_879c5a0279451d52e43c3/aegis-a-method-pack-for-more-reliable-ai-coding-agents-1gfm

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 GanyuanRan/Aegis
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Cursor.sh
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Cursor.sh · recommended 1×
  2. GitHub Copilot Enterprise · recommended 1×
  3. CodiumAI · recommended 1×
  4. OpenAI API (GPT-4/GPT-4o) · recommended 1×
  5. Tabnine Enterprise · recommended 1×
  • CATEGORY QUERY
    How to ensure AI coding agents produce reliable, architecture-aware code for complex projects?
    you: not recommended
    AI recommended (in order):
    1. Cursor.sh
    2. GitHub Copilot Enterprise
    3. CodiumAI
    4. OpenAI API (GPT-4/GPT-4o)
    5. Tabnine Enterprise
    6. Phind
    7. AWS CodeWhisperer

    AI recommended 7 alternatives but never named GanyuanRan/Aegis. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework for AI agent development with baseline-first checks and evidence-driven verification.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. AutoGen
    5. CrewAI
    6. AgentVerse

    AI recommended 6 alternatives but never named GanyuanRan/Aegis. 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 GanyuanRan/Aegis?
    pass
    AI named GanyuanRan/Aegis explicitly

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

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

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

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GanyuanRan/Aegis — 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