RRepoGEO

REPOGEO REPORT · LITE

Dicklesworthstone/claude_code_agent_farm

Default branch main · commit c95b77c1 · scanned 6/9/2026, 5:07:36 AM

GitHub: 841 stars · 95 forks

AI VISIBILITY SCORE
22 /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
1 / 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 Dicklesworthstone/claude_code_agent_farm, 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
  • hightopics#1
    Add specific code-agent topics to improve categorization

    Why:

    CURRENT
    ai-agents, automation, developer-tools, python, tmux
    COPY-PASTE FIX
    ai-code-agents, code-refactoring, automated-bug-fixing, parallel-llm-orchestration, developer-productivity, claude-ai, multi-agent-systems, automation, developer-tools, python, tmux
  • highreadme#2
    Add a unique differentiator to the README's introduction

    Why:

    CURRENT
    Claude Code Agent Farm is a powerful orchestration framework that runs multiple Claude Code (`cc`) sessions in parallel to systematically improve your codebase. It supports multiple technology stacks and workflow types, allowing teams of AI agents to work together on large-scale code improvements.
    COPY-PASTE FIX
    Claude Code Agent Farm is a powerful orchestration framework that runs multiple Claude Code (`cc`) sessions in parallel to systematically improve your codebase. It supports multiple technology stacks and workflow types, allowing teams of AI agents to work together on large-scale code improvements. It uniquely specializes in parallel *code-focused* agent operations, providing robust coordination and real-time monitoring for automated code quality and bug resolution, distinguishing it from generic LLM orchestration tools.
  • mediumlicense#3
    Add explicit license clarification to README

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under a custom license as detailed in the [LICENSE file](./LICENSE). Please refer to the file for full details on usage and distribution.

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 Dicklesworthstone/claude_code_agent_farm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. CrewAI · recommended 1×
  3. Haystack · recommended 1×
  4. OpenAI Assistants API · recommended 1×
  5. Autogen · recommended 1×
  • CATEGORY QUERY
    How can I orchestrate multiple AI agents for parallel code refactoring and bug fixing?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. CrewAI
    3. Haystack
    4. OpenAI Assistants API
    5. Autogen
    6. Celery
    7. Prefect
    8. Airflow

    AI recommended 8 alternatives but never named Dicklesworthstone/claude_code_agent_farm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help coordinate and monitor multiple AI agents improving a large codebase?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Weights & Biases Prompts (wandb/wandb)
    4. MLflow (mlflow/mlflow)
    5. OpenTelemetry (open-telemetry/opentelemetry-specification)
    6. Codedamn AI
    7. RabbitMQ (rabbitmq/rabbitmq-server)
    8. Apache Kafka (apache/kafka)

    AI recommended 8 alternatives but never named Dicklesworthstone/claude_code_agent_farm. 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 Dicklesworthstone/claude_code_agent_farm?
    pass
    AI did not name Dicklesworthstone/claude_code_agent_farm — likely talking about a different project

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

  • If a team adopts Dicklesworthstone/claude_code_agent_farm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Dicklesworthstone/claude_code_agent_farm 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 Dicklesworthstone/claude_code_agent_farm solve, and who is the primary audience?
    pass
    AI did not name Dicklesworthstone/claude_code_agent_farm — likely talking about a different project

    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