RRepoGEO

REPOGEO REPORT · LITE

datacurve-ai/deep-swe

Default branch main · commit 578129c4 · scanned 6/6/2026, 6:08:23 PM

GitHub: 650 stars · 35 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 datacurve-ai/deep-swe, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ai-agents, benchmark, software-engineering, code-generation, evaluation, llm-evaluation, coding-assistant
  • highlicense#2
    Add an MIT License file to the repository

    Why:

    COPY-PASTE FIX
    MIT License

Copyright (c) [YEAR] [COPYRIGHT HOLDER]

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
  • mediumreadme#3
    Clarify README's initial positioning to emphasize benchmark role

    Why:

    CURRENT
    # DeepSWE

DeepSWE is a benchmark for measuring frontier coding agents on original, long-horizon software engineering tasks drawn from active open-source repositories.
    COPY-PASTE FIX
    # DeepSWE: A Benchmark for Frontier Coding Agents

DeepSWE is a comprehensive benchmark designed for rigorously measuring and evaluating frontier coding agents on original, long-horizon software engineering tasks drawn from active open-source repositories.

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 datacurve-ai/deep-swe
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GitHub Copilot Enterprise
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub Copilot Enterprise · recommended 1×
  2. cursor-sh/cursor · recommended 1×
  3. facebookresearch/codellama · recommended 1×
  4. Google Gemini · recommended 1×
  5. Anthropic Claude 3 Opus · recommended 1×
  • CATEGORY QUERY
    How to evaluate AI coding assistants on realistic, long-horizon engineering challenges?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot Enterprise
    2. Cursor (cursor-sh/cursor)
    3. Code Llama (facebookresearch/codellama)
    4. Google Gemini
    5. Anthropic Claude 3 Opus
    6. CodiumAI (Codium-ai/pr-agent)
    7. ChatGPT Plus (GPT-4)
    8. GitHub Copilot
    9. JetBrains AI Assistant

    AI recommended 9 alternatives but never named datacurve-ai/deep-swe. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a benchmark to test AI agents across multiple languages for software development.
    you: not recommended
    AI recommended (in order):
    1. HumanEval
    2. MultiPL-E
    3. MBPP (Mostly Basic Python Problems)
    4. CodeXGLUE
    5. LeetCode
    6. HackerRank
    7. APPS (Automated Programming Problem Solving)
    8. SWE-bench
    9. CodeNet

    AI recommended 9 alternatives but never named datacurve-ai/deep-swe. 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 datacurve-ai/deep-swe?
    pass
    AI named datacurve-ai/deep-swe explicitly

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

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

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

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datacurve-ai/deep-swe — 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