RRepoGEO

REPOGEO REPORT · LITE

harbor-framework/terminal-bench

Default branch main · commit 1a6ffa96 · scanned 5/10/2026, 5:27:40 AM

GitHub: 2,177 stars · 509 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 harbor-framework/terminal-bench, 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
    Clarify README's immediate purpose statement

    Why:

    CURRENT
    # terminal-bench
    COPY-PASTE FIX
    # terminal-bench

A benchmark for evaluating AI agents in real terminal environments.
  • mediumreadme#2
    Add a 'Why Terminal-Bench?' section to differentiate from competitors

    Why:

    COPY-PASTE FIX
    ## Why Terminal-Bench?

Unlike general LLM evaluation frameworks (e.g., LM Harness, OpenAI Evals) or broader AI agent benchmarks (e.g., SWE-bench, AgentBench), Terminal-Bench uniquely focuses on evaluating AI agents within *real terminal environments*. We provide a robust platform for testing an agent's proficiency in executing complex, multi-step command-line operations and end-to-end tasks, such as compiling code, training models, or setting up servers, directly where they would operate.

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 harbor-framework/terminal-bench
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
EleutherAI/lm-eval
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. EleutherAI/lm-eval · recommended 1×
  2. LiteLLM · recommended 1×
  3. OpenAI Evals · recommended 1×
  4. LangChain · recommended 1×
  5. Hugging Face `evaluate` library · recommended 1×
  • CATEGORY QUERY
    How to benchmark large language models performing complex tasks within a terminal?
    you: not recommended
    AI recommended (in order):
    1. LM Harness (EleutherAI/lm-eval)
    2. LiteLLM
    3. OpenAI Evals
    4. LangChain
    5. Hugging Face `evaluate` library
    6. Custom Python/Bash Scripts
    7. MLflow

    AI recommended 7 alternatives but never named harbor-framework/terminal-bench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools to evaluate AI model proficiency in executing multi-step command-line operations?
    you: not recommended
    AI recommended (in order):
    1. AgentBench
    2. SWE-bench
    3. AutoGPT
    4. BabyAGI
    5. SuperAGI
    6. Docker
    7. Python
    8. Bash
    9. Pylint
    10. Flake8
    11. Caliper

    AI recommended 11 alternatives but never named harbor-framework/terminal-bench. 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 harbor-framework/terminal-bench?
    pass
    AI named harbor-framework/terminal-bench explicitly

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

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

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

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harbor-framework/terminal-bench — 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