RRepoGEO

REPOGEO REPORT · LITE

anthropic-experimental/agentic-misalignment

Default branch main · commit ea0630e1 · scanned 6/17/2026, 9:27:54 AM

GitHub: 627 stars · 138 forks

AI VISIBILITY SCORE
23 /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
2 / 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 anthropic-experimental/agentic-misalignment, 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
  • highabout#1
    Add a concise description to the About section

    Why:

    COPY-PASTE FIX
    A research framework for experimentally studying agentic misalignment behaviors like blackmail and information leakage in frontier language models using fictional scenarios.
  • mediumreadme#2
    Add a 'How is this different?' section to the README

    Why:

    COPY-PASTE FIX
    ## How is this different?
This framework uniquely focuses on experimentally demonstrating and studying the emergence of agentic misalignment in AI systems, distinguishing it from general AI agent frameworks or broader AI safety benchmarks.

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 anthropic-experimental/agentic-misalignment
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Anthropic's Constitutional AI (CAI) Framework
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Anthropic's Constitutional AI (CAI) Framework · recommended 1×
  2. Microsoft's Counterfit · recommended 1×
  3. Google's Adversarial ML Fairness Library · recommended 1×
  4. OpenAI Evals · recommended 1×
  5. Hugging Face Evaluate Library · recommended 1×
  • CATEGORY QUERY
    How can I evaluate advanced language models for potential malicious or misaligned behaviors?
    you: not recommended
    AI recommended (in order):
    1. Anthropic's Constitutional AI (CAI) Framework
    2. Microsoft's Counterfit
    3. Google's Adversarial ML Fairness Library
    4. OpenAI Evals
    5. Hugging Face Evaluate Library
    6. Fiddler AI
    7. Google's Responsible AI Toolkit
    8. What-If Tool
    9. Fairness Indicators
    10. Microsoft's Azure AI Content Safety

    AI recommended 10 alternatives but never named anthropic-experimental/agentic-misalignment. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework to experimentally study dangerous emergent behaviors in large language models.
    you: not recommended
    AI recommended (in order):
    1. FAR AI's Interpretability Tools
    2. Anthropic's Interpretability Tools
    3. OpenAI Evals (openai/evals)
    4. Red Teaming Language Models (RTML) Framework
    5. Hugging Face Transformers (huggingface/transformers)
    6. MLCommons' MLPerf Inference
    7. LangChain (langchain-ai/langchain)
    8. LlamaIndex (run-llama/llama_index)

    AI recommended 8 alternatives but never named anthropic-experimental/agentic-misalignment. 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 anthropic-experimental/agentic-misalignment?
    pass
    AI named anthropic-experimental/agentic-misalignment explicitly

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

  • If a team adopts anthropic-experimental/agentic-misalignment in production, what risks or prerequisites should they evaluate first?
    pass
    AI named anthropic-experimental/agentic-misalignment 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 anthropic-experimental/agentic-misalignment solve, and who is the primary audience?
    pass
    AI did not name anthropic-experimental/agentic-misalignment — 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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anthropic-experimental/agentic-misalignment — 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