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REPOGEO REPORT · LITE

stanford-iris-lab/meta-harness-tbench2-artifact

Default branch main · commit 57fefdb2 · scanned 5/18/2026, 1:12:52 AM

GitHub: 1,037 stars · 149 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
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 stanford-iris-lab/meta-harness-tbench2-artifact, 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 README H1 and opening sentence to clarify purpose

    Why:

    CURRENT
    # Meta-Harness

Agent scaffold for Terminal-Bench 2.0, built on top of Terminus-KIRA by KRAFTON AI and Harbor's Terminus-2 framework.
    COPY-PASTE FIX
    # Meta-Harness: An AI Agent Scaffold for Efficient Command-Line Task Execution

Meta-Harness is an AI agent scaffold designed to boost efficiency in command-line tasks by reducing initial exploration, built for the Terminal-Bench 2.0 benchmark on top of Terminus-KIRA by KRAFTON AI and Harbor's Terminus-2 framework.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ai-agents, command-line, terminal, benchmarking, llm-agents, agent-scaffold, environment-bootstrapping, terminal-bench
  • mediumlicense#3
    Add a LICENSE file

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    (Create a LICENSE file in the root of the repository, e.g., MIT or Apache-2.0, to clearly state the terms of use.)

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 stanford-iris-lab/meta-harness-tbench2-artifact
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Function Calling
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Function Calling · recommended 1×
  2. LangChain · recommended 1×
  3. LlamaIndex · recommended 1×
  4. Chroma · recommended 1×
  5. Pinecone · recommended 1×
  • CATEGORY QUERY
    How to make AI agents more efficient at command-line tasks by reducing initial exploration?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Function Calling
    2. LangChain
    3. LlamaIndex
    4. Chroma
    5. Pinecone
    6. Weaviate
    7. Semantic Kernel
    8. Guardrails AI
    9. Pydantic
    10. ShellGPT

    AI recommended 10 alternatives but never named stanford-iris-lab/meta-harness-tbench2-artifact. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help evaluate or develop AI agents for complex shell environments?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym
    2. AgentGym
    3. ALFWorld
    4. MiniWoB++
    5. RLlib
    6. Pommerman
    7. subprocess
    8. pexpect

    AI recommended 8 alternatives but never named stanford-iris-lab/meta-harness-tbench2-artifact. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 stanford-iris-lab/meta-harness-tbench2-artifact?
    pass
    AI did not name stanford-iris-lab/meta-harness-tbench2-artifact — 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 stanford-iris-lab/meta-harness-tbench2-artifact in production, what risks or prerequisites should they evaluate first?
    pass
    AI named stanford-iris-lab/meta-harness-tbench2-artifact 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 stanford-iris-lab/meta-harness-tbench2-artifact solve, and who is the primary audience?
    pass
    AI did not name stanford-iris-lab/meta-harness-tbench2-artifact — 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?

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