RRepoGEO

REPOGEO REPORT · LITE

mgechev/skillgrade

Default branch main · commit e8ed5440 · scanned 6/10/2026, 9:07:57 AM

GitHub: 511 stars · 37 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
40 /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
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 mgechev/skillgrade, 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
  • highabout#1
    Clarify the repository description to focus on AI agent evaluation.

    Why:

    CURRENT
    "Unit tests" for your agent skills
    COPY-PASTE FIX
    A framework for evaluating AI agent skills and their ability to use custom tools and functions.
  • hightopics#2
    Expand repository topics to include more specific AI agent evaluation keywords.

    Why:

    CURRENT
    agent, claude-code, codex, eval, gemini-cli, skill
    COPY-PASTE FIX
    ai-agent, agent-evaluation, llm-evaluation, tool-use, function-calling, agent-testing, skill-evaluation, llm-testing
  • mediumreadme#3
    Add a 'Why Skillgrade?' or 'Comparison' section to the README.

    Why:

    COPY-PASTE FIX
    Add a new section to your README, for example, after 'Quick Start', titled 'Why Skillgrade?'. Include text similar to: 'While general LLM evaluation frameworks like DeepEval or LangChain Evaluation assess broad model performance, Skillgrade is purpose-built for evaluating AI agents. It focuses specifically on testing an agent's ability to correctly discover, integrate, and utilize custom tools and functions, ensuring your agents reliably perform skill-based tasks.'

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 mgechev/skillgrade
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. Pytest · recommended 1×
  4. unittest.mock.patch · recommended 1×
  5. pytest-mock · recommended 1×
  • CATEGORY QUERY
    How to test if my AI agent correctly uses custom tools and functions?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Pytest
    4. unittest.mock.patch
    5. pytest-mock
    6. OpenAI Evals
    7. Playwright
    8. Selenium
    9. Postman
    10. Insomnia

    AI recommended 10 alternatives but never named mgechev/skillgrade. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for grading AI agent responses based on predefined tasks and criteria?
    you: not recommended
    AI recommended (in order):
    1. LangChain Evaluation
    2. Arize AI
    3. Phoenix
    4. Weights & Biases
    5. DeepEval
    6. Humanloop
    7. Giskard
    8. NLTK
    9. spaCy
    10. scikit-learn
    11. OpenAI
    12. Anthropic

    AI recommended 12 alternatives but never named mgechev/skillgrade. 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 mgechev/skillgrade?
    pass
    AI named mgechev/skillgrade explicitly

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

  • If a team adopts mgechev/skillgrade in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mgechev/skillgrade 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 mgechev/skillgrade solve, and who is the primary audience?
    pass
    AI named mgechev/skillgrade explicitly

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

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mgechev/skillgrade — 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