RRepoGEO

REPOGEO REPORT · LITE

anthropics/defending-code-reference-harness

Default branch main · commit 9e0f6c6c · scanned 6/7/2026, 5:37:49 PM

GitHub: 5,052 stars · 325 forks

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 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 anthropics/defending-code-reference-harness, 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 the README's opening to clarify its purpose as a customizable LLM-powered security framework

    Why:

    CURRENT
    A reference implementation for autonomous vulnerability discovery and remediation with Claude, based on our learnings from partnering with security teams at several organizations since launching Claude Mythos Preview.
    COPY-PASTE FIX
    This repository provides a reference implementation and customizable harness for *building your own* autonomous, LLM-powered vulnerability discovery and remediation systems using Claude. It distills best practices from our work with security teams and is designed for those looking to integrate advanced AI into their code security workflows.
  • hightopics#2
    Add more specific topics to differentiate from traditional security tools

    Why:

    CURRENT
    ["security"]
    COPY-PASTE FIX
    ["llm-security", "ai-security", "code-security", "vulnerability-scanning", "threat-modeling", "generative-ai", "claude-ai", "security-harness"]
  • mediumlicense#3
    Clarify the project's license directly in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under the terms specified in the LICENSE file. Please refer to the LICENSE file for full details on the applicable license(s).

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 anthropics/defending-code-reference-harness
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
github/codeql
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. github/codeql · recommended 2×
  2. jeremylong/DependencyCheck · recommended 2×
  3. Snyk · recommended 1×
  4. GitHub Advanced Security (GHAS) · recommended 1×
  5. github/dependabot-core · recommended 1×
  • CATEGORY QUERY
    How can I automate finding and fixing security vulnerabilities in my codebase?
    you: not recommended
    AI recommended (in order):
    1. Snyk
    2. GitHub Advanced Security (GHAS)
    3. CodeQL (github/codeql)
    4. Dependabot (github/dependabot-core)
    5. SonarQube (sonarsource/sonarqube)
    6. OWASP Dependency-Check (jeremylong/DependencyCheck)
    7. Veracode
    8. Checkmarx One
    9. Trivy (aquasecurity/trivy)

    AI recommended 9 alternatives but never named anthropics/defending-code-reference-harness. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source tools help build custom AI-driven code security scanning systems?
    you: not recommended
    AI recommended (in order):
    1. Semgrep (returntocorp/semgrep)
    2. Bandit (PyCQA/bandit)
    3. ESLint (eslint/eslint)
    4. OWASP Dependency-Check (jeremylong/DependencyCheck)
    5. SpotBugs (spotbugs/spotbugs)
    6. TensorFlow (tensorflow/tensorflow)
    7. PyTorch (pytorch/pytorch)
    8. CodeQL (github/codeql)

    AI recommended 8 alternatives but never named anthropics/defending-code-reference-harness. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 anthropics/defending-code-reference-harness?
    pass
    AI did not name anthropics/defending-code-reference-harness — 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 anthropics/defending-code-reference-harness in production, what risks or prerequisites should they evaluate first?
    pass
    AI named anthropics/defending-code-reference-harness 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 anthropics/defending-code-reference-harness solve, and who is the primary audience?
    pass
    AI did not name anthropics/defending-code-reference-harness — 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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anthropics/defending-code-reference-harness — 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