RRepoGEO

REPOGEO REPORT · LITE

lllllllama/RigorPilot-Skills

Default branch main · commit 56bc41c4 · scanned 6/28/2026, 3:53:57 PM

GitHub: 515 stars · 9 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 lllllllama/RigorPilot-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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise 'About' description

    Why:

    COPY-PASTE FIX
    Research-first Agent Skills for Deep Learning Experiments, ensuring comparability, reproducible evidence, and auditable changes while an agent reproduces, improves, or explores a research repository.
  • mediumreadme#2
    Strengthen README's initial positioning on AI agent capabilities

    Why:

    CURRENT
    # RigorPilot Skills

Research-first Agent Skills for Deep Learning Experiments.

**Main idea:** RigorPilot keeps AI-assisted deep learning research grounded in
comparability, reproducible evidence, and auditable changes while an agent
reproduces, improves, or explores a research repository.
    COPY-PASTE FIX
    # RigorPilot Skills: Research-first AI Agent Skills for Deep Learning Experiments

RigorPilot provides specialized AI Agent Skills designed for deep learning research. Its core purpose is to keep AI-assisted deep learning research grounded in comparability, reproducible evidence, and auditable changes while an agent reproduces, improves, or explores a research repository.

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 lllllllama/RigorPilot-Skills
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MLflow
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. MLflow · recommended 1×
  2. Weights & Biases (W&B) · recommended 1×
  3. Comet ML · recommended 1×
  4. Neptune.ai · recommended 1×
  5. DVC (Data Version Control) · recommended 1×
  • CATEGORY QUERY
    How to manage deep learning experiments with an AI agent for reproducibility and auditable changes?
    you: not recommended
    AI recommended (in order):
    1. MLflow
    2. Weights & Biases (W&B)
    3. Comet ML
    4. Neptune.ai
    5. DVC (Data Version Control)
    6. Git
    7. Pachyderm

    AI recommended 7 alternatives but never named lllllllama/RigorPilot-Skills. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an AI agent framework for deep learning research focused on meaningful progress, not just scores.
    you: not recommended
    AI recommended (in order):
    1. DeepMind Lab (deepmind/lab)
    2. OpenAI Gym (openai/gym)
    3. Unity ML-Agents Toolkit (Unity-Technologies/ml-agents)
    4. MetaWorld (rlworkgroup/metaworld)
    5. RLlib (ray-project/ray)

    AI recommended 5 alternatives but never named lllllllama/RigorPilot-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
    fail

    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 lllllllama/RigorPilot-Skills?
    pass
    AI did not name lllllllama/RigorPilot-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 lllllllama/RigorPilot-Skills in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lllllllama/RigorPilot-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 lllllllama/RigorPilot-Skills solve, and who is the primary audience?
    pass
    AI named lllllllama/RigorPilot-Skills explicitly

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

Embed your GEO score

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lllllllama/RigorPilot-Skills — 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