RRepoGEO

REPOGEO REPORT · LITE

leodavinci1/kanbots

Default branch main · commit 7b256a02 · scanned 6/17/2026, 10:26:57 PM

GitHub: 528 stars · 43 forks

AI VISIBILITY SCORE
22 /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
1 / 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 leodavinci1/kanbots, 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 the README's opening statement for clarity

    Why:

    CURRENT
    > **A kanban board that runs 11 agent CLIs in parallel.**
    COPY-PASTE FIX
    > **A local-first kanban board for orchestrating multiple AI coding agents (Claude, Codex, Gemini, etc.) in parallel, each in its own isolated Git worktree.**
  • mediumreadme#2
    Emphasize multi-agent orchestration in the '11 agent CLIs supported' highlight

    Why:

    CURRENT
    11 agent CLIs supported** — Claude Code, Codex, Gemini, Cursor, Copilot, Amp, OpenCode, Droid, CCR, Qwen, plus any ACP-compatible CLI. Each run is isolated in a per-run worktree; a pre-push hook prevents agents from pushing.
    COPY-PASTE FIX
    11+ AI Coding Agents Supported** — Orchestrate Claude Code, Codex, Gemini, Cursor, Copilot, and more, each running in an isolated Git worktree for parallel development. A pre-push hook ensures agent changes are reviewed before landing.

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 leodavinci1/kanbots
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GitHub Projects
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub Projects · recommended 1×
  2. GitHub Actions · recommended 1×
  3. OpenAI API · recommended 1×
  4. LangChain · recommended 1×
  5. Anthropic's Claude · recommended 1×
  • CATEGORY QUERY
    How to manage coding tasks using multiple AI agents on a kanban board?
    you: not recommended
    AI recommended (in order):
    1. GitHub Projects
    2. GitHub Actions
    3. OpenAI API
    4. LangChain
    5. Anthropic's Claude
    6. GPT-4
    7. CodeGuru Reviewer
    8. Jira Software
    9. Zapier
    10. Make.com
    11. Linear
    12. AWS Lambda
    13. Google Cloud Functions
    14. Vercel
    15. Trello
    16. Butler
    17. Azure DevOps Boards
    18. Azure Functions
    19. Azure OpenAI Service
    20. LlamaIndex

    AI recommended 20 alternatives but never named leodavinci1/kanbots. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a tool to orchestrate parallel AI agent development workflows with isolated environments.
    you: not recommended
    AI recommended (in order):
    1. Kubeflow Pipelines
    2. MLflow
    3. Apache Airflow
    4. Metaflow
    5. Prefect
    6. Dagster

    AI recommended 6 alternatives but never named leodavinci1/kanbots. 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 leodavinci1/kanbots?
    pass
    AI did not name leodavinci1/kanbots — 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 leodavinci1/kanbots in production, what risks or prerequisites should they evaluate first?
    pass
    AI named leodavinci1/kanbots 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 leodavinci1/kanbots solve, and who is the primary audience?
    pass
    AI did not name leodavinci1/kanbots — 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?

Embed your GEO score

Drop this badge into the README of leodavinci1/kanbots. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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leodavinci1/kanbots — RepoGEO report