RRepoGEO

REPOGEO REPORT · LITE

Orchestra-Research/AI-Research-SKILLs

Default branch main · commit 28f2d292 · scanned 5/10/2026, 3:01:51 AM

GitHub: 8,118 stars · 627 forks

AI VISIBILITY SCORE
27 /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
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 Orchestra-Research/AI-Research-SKILLs, 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 README opening to clarify niche and differentiate from agent frameworks

    Why:

    CURRENT
    > **The most comprehensive open-source skills library enabling AI agents to autonomously conduct AI research — from idea to paper**
    COPY-PASTE FIX
    > **The definitive open-source library providing *specialized research skills* for AI agents, transforming general-purpose agents (like those built with LangChain or LlamaIndex) into autonomous AI researchers capable of taking an idea to a published paper.**
  • mediumtopics#2
    Refine and expand topics to include agent-specific research automation

    Why:

    CURRENT
    ai, ai-research, claude, claude-code, claude-skills, codex, gemini, gpt-5, grpo, huggingface, machine-leanring, megatron, skills, vllm
    COPY-PASTE FIX
    ai, ai-research, ai-research-skills, ai-agents, autonomous-agents, research-automation, agent-skills, llm-agents, ml-research, claude, claude-code, claude-skills, codex, gemini, gpt-5, huggingface, machine-learning, megatron, skills, vllm
  • lowcomparison#3
    Add a dedicated 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## How is this different from Agent Frameworks (e.g., LangChain, LlamaIndex)?

While frameworks like LangChain and LlamaIndex provide the foundational architecture for building AI agents, Orchestra-Research/AI-Research-SKILLs offers the *specialized intellectual capabilities* (skills) that these agents need to perform complex tasks like autonomous AI research. We provide the 'brain' for your agent framework's 'body'.

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 Orchestra-Research/AI-Research-SKILLs
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. LlamaIndex · recommended 1×
  3. Google Search API · recommended 1×
  4. SerpAPI · recommended 1×
  5. Wolfram Alpha API · recommended 1×
  • CATEGORY QUERY
    How can I equip my AI agent with advanced capabilities for autonomous research?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Google Search API
    4. SerpAPI
    5. Wolfram Alpha API
    6. ArXiv API
    7. Beautiful Soup
    8. Requests
    9. OpenAI API
    10. GPT-4

    AI recommended 10 alternatives but never named Orchestra-Research/AI-Research-SKILLs. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a comprehensive toolkit to develop and deploy AI research models efficiently.
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. PyTorch Lightning (Lightning-AI/lightning)
    3. Hydra (facebookresearch/hydra)
    4. Weights & Biases (wandb/wandb)
    5. TensorFlow (tensorflow/tensorflow)
    6. Keras (keras-team/keras)
    7. TensorFlow Extended (TFX) (tensorflow/tfx)
    8. MLflow (mlflow/mlflow)
    9. Hugging Face Transformers (huggingface/transformers)
    10. Hugging Face Accelerate (huggingface/accelerate)
    11. JupyterLab (jupyterlab/jupyterlab)
    12. VS Code (microsoft/vscode)
    13. Docker (moby/moby)
    14. Kubernetes (kubernetes/kubernetes)
    15. DVC (Data Version Control) (iterative/dvc)
    16. Ray (ray-project/ray)

    AI recommended 16 alternatives but never named Orchestra-Research/AI-Research-SKILLs. 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 Orchestra-Research/AI-Research-SKILLs?
    pass
    AI did not name Orchestra-Research/AI-Research-SKILLs — 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 Orchestra-Research/AI-Research-SKILLs in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Orchestra-Research/AI-Research-SKILLs 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 Orchestra-Research/AI-Research-SKILLs solve, and who is the primary audience?
    pass
    AI did not name Orchestra-Research/AI-Research-SKILLs — 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
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