RRepoGEO

REPOGEO REPORT · LITE

dora-rs/dora

Default branch main · commit f342aeb0 · scanned 6/24/2026, 4:47:23 AM

GitHub: 3,801 stars · 408 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 dora-rs/dora, 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 core value proposition in the README's opening

    Why:

    CURRENT
    The README excerpt shows the H1 followed by navigation links and then a paragraph about 'agentic engineering' before features.
    COPY-PASTE FIX
    Add a concise, direct statement of Dora's purpose as the very first paragraph after the main heading, for example: 'Dora is a **Dataflow-Oriented Robotic Architecture** (DORA) and a 100% Rust framework for building real-time robotics and AI applications. It provides low-latency, composable, and distributed dataflow capabilities, allowing you to model applications as directed graphs or pipelines.'
  • mediumtopics#2
    Expand repository topics with more specific keywords

    Why:

    CURRENT
    dataflow, embodied-ai, low-latency, robotics, rust
    COPY-PASTE FIX
    dataflow, embodied-ai, low-latency, robotics, rust, robotics-middleware, ai-robotics, real-time-systems, distributed-systems
  • lowreadme#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    Add a new top-level section to the README, e.g., `## Comparison with Alternatives` or `## Why Dora?`. This section should briefly outline how Dora differs from common alternatives like ROS 2 or NVIDIA Isaac ROS, focusing on its Rust-native, lightweight, and edge-optimized aspects.

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 dora-rs/dora
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ROS 2
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ROS 2 · recommended 2×
  2. NVIDIA Isaac ROS · recommended 1×
  3. Open Robotics' Ignition Gazebo · recommended 1×
  4. Google's MediaPipe · recommended 1×
  5. Apache Flink · recommended 1×
  • CATEGORY QUERY
    How to build low-latency, composable AI-based robotic applications with dataflow pipelines?
    you: not recommended
    AI recommended (in order):
    1. ROS 2
    2. NVIDIA Isaac ROS
    3. Open Robotics' Ignition Gazebo
    4. Google's MediaPipe
    5. Apache Flink
    6. ZeroMQ (0MQ)
    7. nanomsg
    8. NNG

    AI recommended 8 alternatives but never named dora-rs/dora. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Rust framework for real-time distributed dataflow in embodied AI systems.
    you: not recommended
    AI recommended (in order):
    1. Zenoh
    2. ROS 2
    3. Apache Kafka
    4. NATS
    5. gRPC

    AI recommended 5 alternatives but never named dora-rs/dora. 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 dora-rs/dora?
    pass
    AI named dora-rs/dora explicitly

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

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

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

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dora-rs/dora — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite