RRepoGEO

REPOGEO REPORT · LITE

claw-eval/claw-eval

Default branch main · commit d3f02d49 · scanned 5/31/2026, 2:32:25 PM

GitHub: 626 stars · 54 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 claw-eval/claw-eval, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root. If a specific license is intended, use its standard text (e.g., MIT, Apache-2.0). If the license is custom or compound, describe it clearly in the `LICENSE` file and reference it in the README.
  • highreadme#2
    Strengthen README opening to explicitly position Claw-Eval as an LLM agent evaluation platform/benchmark

    Why:

    CURRENT
    > Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agents. <br> 300 human-verified tasks | 2,159 rubrics | 9 categories | Completion · Safety · Robustness.
    COPY-PASTE FIX
    Claw-Eval is a robust evaluation platform and benchmark specifically designed for assessing the performance and reliability of Large Language Model (LLM) agents. It features 300 human-verified tasks across 9 categories, including a unique focus on code generation, repair, and realistic vulnerability detection. Our rigorous evaluation logic, including Pass^3 metrics, ensures trustworthy assessment of agent completion, safety, and robustness.
  • mediumtopics#3
    Expand repository topics to include more specific evaluation and agent-related terms

    Why:

    CURRENT
    agent, harness, llm, openclaw
    COPY-PASTE FIX
    agent, harness, llm, openclaw, llm-agents, agent-evaluation, benchmark, evaluation-platform, code-llm, vulnerability-detection, human-verified

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 claw-eval/claw-eval
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AgentBench
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. AgentBench · recommended 2×
  2. LangSmith · recommended 1×
  3. LlamaIndex Evaluation · recommended 1×
  4. OpenAI Evals · recommended 1×
  5. Humanloop · recommended 1×
  • CATEGORY QUERY
    What are good platforms for evaluating the performance and capabilities of large language model agents?
    you: not recommended
    AI recommended (in order):
    1. LangSmith
    2. LlamaIndex Evaluation
    3. OpenAI Evals
    4. AgentBench
    5. Humanloop
    6. MLflow
    7. Weights & Biases

    AI recommended 7 alternatives but never named claw-eval/claw-eval. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a robust evaluation harness to benchmark autonomous AI agent reliability across diverse tasks.
    you: not recommended
    AI recommended (in order):
    1. AgentBench
    2. AutoGPT Benchmarks (Significant-Gravitas/AutoGPT)
    3. GAIA
    4. SWE-bench
    5. MiniWoB++
    6. ALFWorld
    7. ProcTHOR/AI2-THOR

    AI recommended 7 alternatives but never named claw-eval/claw-eval. 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 claw-eval/claw-eval?
    pass
    AI named claw-eval/claw-eval explicitly

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

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

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

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claw-eval/claw-eval — 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