RRepoGEO

REPOGEO REPORT · LITE

Orchestra-Research/AI-Research-SKILLs

Default branch main · commit 773a5294 · scanned 6/20/2026, 12:26:56 AM

GitHub: 9,866 stars · 739 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
33 /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
2 / 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
    Add a clarifying statement to the README's introduction

    Why:

    COPY-PASTE FIX
    Add this sentence immediately after the main tagline: 'Unlike general AI agent frameworks (e.g., LangChain, AutoGPT) or foundational ML libraries (e.g., PyTorch, Hugging Face), this library provides the *specific, actionable skills* that empower AI agents to autonomously conduct complex AI research, from ideation to paper publication.'
  • hightopics#2
    Correct typo and add specific agent-related topics

    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, claude, claude-code, claude-skills, codex, gemini, gpt-5, grpo, huggingface, machine-learning, megatron, skills, vllm, ai-agents, llm-agents, agent-skills, ai-research-agents
  • mediumreadme#3
    Add a dedicated 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example, titled 'How is Orchestra-Research/AI-Research-SKILLs Different?': 'Unlike general AI agent frameworks (e.g., LangChain, AutoGPT) or foundational ML libraries (e.g., PyTorch, Hugging Face), Orchestra-Research/AI-Research-SKILLs focuses on providing a structured, holistic breakdown of the *operational skills* required for AI agents to *conduct* AI research autonomously. It is designed to be integrated *into* agent systems to enhance their capabilities, rather than serving as a standalone agent framework or a general-purpose machine learning library.'

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. AutoGPT · recommended 1×
  4. BabyAGI · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    How can I equip my AI agent with advanced capabilities for autonomous research and development?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGPT
    4. BabyAGI
    5. Hugging Face Transformers
    6. Wolfram Alpha API
    7. Google Search API
    8. SerpAPI
    9. OpenAI API

    AI recommended 9 alternatives but never named Orchestra-Research/AI-Research-SKILLs. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source libraries provide a comprehensive set of skills for various AI model development tasks?
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. TensorFlow (tensorflow/tensorflow)
    3. Keras (keras-team/keras)
    4. scikit-learn (scikit-learn/scikit-learn)
    5. Hugging Face Transformers (huggingface/transformers)
    6. FastAI (fastai/fastai)
    7. XGBoost (dmlc/xgboost)
    8. LightGBM (microsoft/LightGBM)
    9. OpenCV (opencv/opencv)

    AI recommended 9 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 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?

  • 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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Orchestra-Research/AI-Research-SKILLs — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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