RRepoGEO

REPOGEO REPORT · LITE

mozilla-ai/cq

Default branch main · commit 2017d5d6 · scanned 6/20/2026, 10:11:23 AM

GitHub: 1,199 stars · 61 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 mozilla-ai/cq, 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
  • highreadme#1
    Reposition the README H1 to specify category

    Why:

    CURRENT
    # cq
    COPY-PASTE FIX
    # cq: An Open Standard for Collective AI Knowledge
  • hightopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    agents, go, python
    COPY-PASTE FIX
    ai-agents, multi-agent-systems, collective-intelligence, knowledge-sharing, agent-learning, standard, go, python
  • mediumcomparison#3
    Add a 'How cq Differs' section to clarify unique value

    Why:

    COPY-PASTE FIX
    ## How cq Differs

Unlike general-purpose LLM evaluation frameworks or dataset management tools, cq focuses specifically on enabling AI agents to share and query collective knowledge. It provides a standard for agents to learn from each other's experiences, preventing redundant failures and accelerating collective intelligence, rather than just managing data or evaluating models in isolation.

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 mozilla-ai/cq
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 1×
  2. openai/baselines · recommended 1×
  3. deepmind/acme · recommended 1×
  4. huggingface/transformers · recommended 1×
  5. tensorflow/hub · recommended 1×
  • CATEGORY QUERY
    How can AI agents share learned experiences to avoid repeating common mistakes?
    you: not recommended
    AI recommended (in order):
    1. Ray RLlib (ray-project/ray)
    2. OpenAI Baselines (openai/baselines)
    3. Acme (deepmind/acme)
    4. Hugging Face Transformers (huggingface/transformers)
    5. TensorFlow Hub (tensorflow/hub)
    6. PyTorch Hub (pytorch/hub)
    7. ONNX (onnx/onnx)
    8. TensorFlow Federated (tensorflow/federated)
    9. PySyft (OpenMined/PySyft)
    10. Flower (adap/flower)
    11. Neo4j (neo4j/neo4j)
    12. Apache Jena (apache/jena)
    13. Protege (protegeproject/protege)
    14. PettingZoo (Farama-Foundation/PettingZoo)
    15. MAgent (PKU-MARL/MAgent)
    16. OpenSpiel (deepmind/open_spiel)

    AI recommended 16 alternatives but never named mozilla-ai/cq. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools facilitate collective knowledge sharing among multiple AI agents?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Pinecone
    4. Redis
    5. Apache Kafka
    6. Faiss
    7. Hugging Face Datasets library

    AI recommended 7 alternatives but never named mozilla-ai/cq. 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 mozilla-ai/cq?
    pass
    AI did not name mozilla-ai/cq — 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 mozilla-ai/cq in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mozilla-ai/cq 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 mozilla-ai/cq solve, and who is the primary audience?
    pass
    AI named mozilla-ai/cq 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 mozilla-ai/cq. 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/mozilla-ai/cq.svg)](https://repogeo.com/en/r/mozilla-ai/cq)
HTML
<a href="https://repogeo.com/en/r/mozilla-ai/cq"><img src="https://repogeo.com/badge/mozilla-ai/cq.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

mozilla-ai/cq — 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