RRepoGEO

REPOGEO REPORT · LITE

DanMcInerney/architect-loop

Default branch main · commit e6da696c · scanned 6/20/2026, 7:08:04 PM

GitHub: 526 stars · 47 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 DanMcInerney/architect-loop, 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 opening to clearly state it's an agent framework

    Why:

    CURRENT
    # architect-loop

**Claude Fable handles planning and review; GPT-5.5 Codex handles implementation and research.** Two Claude Code skills wire that split into a repo-centered loop: specs and gates are written first, Codex works in fresh contexts, and Fable reviews the evidence before anything is integrated. It runs on the subscriptions you already have — no API keys required by default.
    COPY-PASTE FIX
    # architect-loop: A Cross-Vendor Autonomous Agent Framework

**Architect-loop is an autonomous agent framework that orchestrates Claude Fable for planning and review, and GPT-5.5 Codex for implementation and research.** It wires these capabilities into a repo-centered loop using two Claude Code skills: specs and gates are written first, Codex works in fresh contexts, and Fable reviews the evidence before anything is integrated. It runs on the subscriptions you already have — no API keys required by default.
  • mediumreadme#2
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Other Agent Frameworks

Unlike general-purpose orchestration libraries, architect-loop provides a simple, opinionated, and extensible framework that explicitly structures the iterative "plan-act-reflect" control loop for autonomous agents. While tools like AutoGPT and CrewAI offer broad agent capabilities, architect-loop focuses on a specific cross-vendor (Claude Fable + GPT-5.5 Codex) loop for code generation and research, emphasizing a repo-centered memory and a clear separation of planning/review (Fable) and implementation/research (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 DanMcInerney/architect-loop
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AutoGPT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. AutoGPT · recommended 1×
  2. BabyAGI · recommended 1×
  3. CrewAI · recommended 1×
  4. LangChain Agents · recommended 1×
  5. MetaGPT · recommended 1×
  • CATEGORY QUERY
    Seeking a tool to automate software development tasks using multiple AI agents.
    you: not recommended
    AI recommended (in order):
    1. AutoGPT
    2. BabyAGI
    3. CrewAI
    4. LangChain Agents
    5. MetaGPT
    6. Smol-Developer

    AI recommended 6 alternatives but never named DanMcInerney/architect-loop. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good AI systems for automating both code planning and implementation phases?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot X
    2. Cursor
    3. Codeium
    4. Tabnine
    5. Replit AI
    6. OpenAI API

    AI recommended 6 alternatives but never named DanMcInerney/architect-loop. 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 DanMcInerney/architect-loop?
    pass
    AI named DanMcInerney/architect-loop explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite