RRepoGEO

REPOGEO REPORT · LITE

hamelsmu/evals-skills

Default branch main · commit 814ebeae · scanned 6/21/2026, 5:57:17 AM

GitHub: 1,410 stars · 143 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
28 /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
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 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 improve categorization

    Why:

    COPY-PASTE FIX
    llm-evaluation, ai-agents, claude-code, llm-skills, evaluation-audits, prompt-engineering, generative-ai, llm-ops
  • highreadme#2
    Reposition README H1 and opening paragraph to clarify its role as an AI agent skillset

    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: A Plugin for AI Coding Agents

This repository provides a collection of specialized skills designed as a plugin for AI coding agents (like Claude Code) to systematically build, audit, and improve your LLM evaluation pipelines.
  • mediumreadme#3
    Add a 'Why Evals Skills?' section to differentiate from general LLM eval frameworks

    Why:

    COPY-PASTE FIX
    ## Why Evals Skills? (vs. LangChain, DeepEval, Ragas, etc.)

Evals Skills is not a standalone LLM evaluation framework like LangChain, DeepEval, or Ragas. Instead, it's a specialized plugin that equips AI coding agents with actionable "skills" to *assist* you in auditing, improving, and generating evaluations. It acts as an intelligent co-pilot for your agent, guiding it through common pitfalls and best practices in LLM evaluation, rather than providing the core infrastructure for defining and running tests.

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
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. DeepEval · recommended 2×
  3. Ragas · recommended 2×
  4. Humanloop · recommended 2×
  5. Arize AI Phoenix · recommended 1×
  • CATEGORY QUERY
    How can I improve the reliability and robustness of my LLM evaluation pipelines?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Arize AI Phoenix
    3. Weights & Biases W&B Prompts
    4. DeepEval
    5. Ragas
    6. Humanloop
    7. MLflow

    AI recommended 7 alternatives but never named hamelsmu/evals-skills. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help AI agents build and audit effective large language model evaluations?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Ragas
    3. DeepEval
    4. Phoenix
    5. W&B Prompts
    6. Humanloop
    7. OpenAI Evals

    AI recommended 7 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 did not name hamelsmu/evals-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 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