RRepoGEO

REPOGEO REPORT · LITE

microsoft/waza

Default branch main · commit 76920273 · scanned 6/16/2026, 6:06:45 PM

GitHub: 1,004 stars · 56 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 microsoft/waza, 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
  • highreadme#1
    Reposition README H1 and add explicit status to correct AI miscategorization

    Why:

    CURRENT
    # Waza

A Go CLI for evaluating AI agent skills — scaffold eval suites, run benchmarks, and compare results across models.
    COPY-PASTE FIX
    # Waza: A Go CLI for AI Agent Skill Evaluation

**Waza is an actively maintained Go CLI specifically designed for evaluating and benchmarking AI agent skills and large language models.** It helps you scaffold evaluation suites, run benchmarks, and compare results across different models to improve skill quality and effectiveness.
  • mediumabout#2
    Enhance the repository description with more keywords

    Why:

    CURRENT
    CLI / Framework for Agent Skills - create, test, measure and improve skill quality and effectiveness
    COPY-PASTE FIX
    Waza is a Go CLI and framework for evaluating AI agent skills and large language models. Create, test, measure, and improve skill quality and effectiveness through benchmarking and comparison.

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 microsoft/waza
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Humanloop
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Humanloop · recommended 2×
  2. LangChain · recommended 1×
  3. LlamaIndex · recommended 1×
  4. Phoenix · recommended 1×
  5. W&B Prompts · recommended 1×
  • CATEGORY QUERY
    How can I systematically evaluate the quality and effectiveness of my AI agent skills?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Phoenix
    4. W&B Prompts
    5. OpenAI Evals
    6. Humanloop
    7. Ragas
    8. Deepchecks

    AI recommended 8 alternatives but never named microsoft/waza. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help benchmark and compare various large language models for agent skill development?
    you: not recommended
    AI recommended (in order):
    1. LangChain Evaluation (langchain-ai/langchain)
    2. LlamaIndex Evaluation (run-llama/llama_index)
    3. Humanloop
    4. Weights & Biases Prompts (wandb/wandb)
    5. MLflow (mlflow/mlflow)
    6. Ragas (explodinggradients/ragas)
    7. DeepEval (confident-ai/deepeval)

    AI recommended 7 alternatives but never named microsoft/waza. 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 microsoft/waza?
    pass
    AI named microsoft/waza explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of microsoft/waza. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/microsoft/waza.svg)](https://repogeo.com/en/r/microsoft/waza)
HTML
<a href="https://repogeo.com/en/r/microsoft/waza"><img src="https://repogeo.com/badge/microsoft/waza.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

microsoft/waza — 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