RRepoGEO

REPOGEO REPORT · LITE

hamelsmu/evals-skills

Default branch main · commit febdb335 · scanned 5/11/2026, 3:07:28 AM

GitHub: 1,256 stars · 134 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 hamelsmu/evals-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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm-evaluation, ai-evals, llm-ops, evaluation-pipelines, ai-agents, quality-assurance, prompt-engineering, machine-learning
  • highreadme#2
    Reposition the README's opening to emphasize LLM evaluation quality

    Why:

    CURRENT
    # Eval Skills for AI Coding Agents

Skills that guide AI coding agents to help you build LLM evaluations.
    COPY-PASTE FIX
    # Eval Skills for LLM Evaluation Pipelines

Skills that help you audit and improve the quality of your LLM evaluation pipelines, often by guiding AI coding agents.
  • mediumreadme#3
    Add a 'Why use this?' section highlighting the core differentiator

    Why:

    COPY-PASTE FIX
    ## Why Use Eval Skills?

Unlike broader MLOps platforms or general LLM frameworks, Eval Skills provides a lightweight, extensible collection of specific, diagnostic LLM skill tests. These are designed for quick, local iteration, independent of any specific model or complex evaluation framework, helping you pinpoint and fix common issues in your LLM evaluation process efficiently.

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 hamelsmu/evals-skills
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Weights & Biases (W&B)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Weights & Biases (W&B) · recommended 1×
  2. MLflow · recommended 1×
  3. Deepchecks · recommended 1×
  4. Great Expectations · recommended 1×
  5. LangChain · recommended 1×
  • CATEGORY QUERY
    What tools help ensure quality and prevent common errors in large language model evaluation pipelines?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases (W&B)
    2. MLflow
    3. Deepchecks
    4. Great Expectations
    5. LangChain
    6. LlamaIndex
    7. Haystack
    8. Pydantic
    9. pytest

    AI recommended 9 alternatives but never named hamelsmu/evals-skills. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can an AI assistant help audit and improve my LLM evaluation process?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Evaluate (huggingface/evaluate)
    2. NLPGradient
    3. DeepEval (confident-ai/deepeval)
    4. Argilla (argilla-io/argilla)
    5. Humanloop
    6. Galileo AI
    7. Giskard (Giskard-AI/giskard)
    8. Robustness Gym (robustness-gym/robustness-gym)
    9. OpenAI Evals (openai/evals)
    10. Fairness Indicators (Google) (google/fairness-indicators)
    11. Aequitas (dssg/aequitas)
    12. IBM AI Fairness 360 (AIF360) (Trusted-AI/AIF360)
    13. Label Studio (heartexlabs/label-studio)
    14. Snorkel AI (snorkel-team/snorkel)
    15. Weights & Biases (W&B Prompts) (wandb/wandb)

    AI recommended 15 alternatives but never named hamelsmu/evals-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
    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 hamelsmu/evals-skills?
    pass
    AI named hamelsmu/evals-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 hamelsmu/evals-skills in production, what risks or prerequisites should they evaluate first?
    pass
    AI named hamelsmu/evals-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 hamelsmu/evals-skills solve, and who is the primary audience?
    pass
    AI named hamelsmu/evals-skills explicitly

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

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hamelsmu/evals-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