RRepoGEO

REPOGEO REPORT · LITE

nizos/tdd-guard

Default branch main · commit b76b571a · scanned 6/22/2026, 4:21:42 AM

GitHub: 2,205 stars · 168 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
28 /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
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 nizos/tdd-guard, 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 intro to emphasize AI agent guardrail and unique prevention

    Why:

    CURRENT
    Automated Test-Driven Development enforcement for Claude Code.
    COPY-PASTE FIX
    TDD Guard is an automated policy enforcement and guardrail system for AI coding agents like Claude Code. It actively monitors and *prevents* LLM agents from violating Test-Driven Development principles, ensuring they write failing tests before implementation and avoid over-engineering.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://www.npmjs.com/package/tdd-guard
  • lowtopics#3
    Expand repository topics with more specific keywords

    Why:

    CURRENT
    agentic-coding, automation, claude-code, code-quality, hooks, llm-tools, tdd
    COPY-PASTE FIX
    agentic-coding, automation, claude-code, code-quality, hooks, llm-tools, tdd, ai-guardrails, policy-enforcement, developer-workflow-automation

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 nizos/tdd-guard
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Cucumber
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Cucumber · recommended 1×
  2. behave/behave · recommended 1×
  3. swagger-api/swagger-ui · recommended 1×
  4. OpenAPITools/openapi-generator · recommended 1×
  5. junit-team/junit5 · recommended 1×
  • CATEGORY QUERY
    How to enforce strict test-driven development practices for AI code generation?
    you: not recommended
    AI recommended (in order):
    1. Cucumber
    2. Behave (behave/behave)
    3. Swagger UI (swagger-api/swagger-ui)
    4. OpenAPI Generator (OpenAPITools/openapi-generator)
    5. JUnit 5 (junit-team/junit5)
    6. Pytest (pytest-dev/pytest)
    7. Jest (facebook/jest)
    8. Go testing package
    9. Playwright (microsoft/playwright)
    10. Selenium WebDriver (SeleniumHQ/selenium)
    11. ESLint (eslint/eslint)
    12. Black (psf/black)
    13. Flake8 (PyCQA/flake8)
    14. Checkstyle (checkstyle/checkstyle)
    15. gofmt
    16. golint
    17. SonarQube
    18. Hypothesis (HypothesisWorks/hypothesis)
    19. QuickCheck
    20. jqwik (jqwik-team/jqwik)
    21. Git
    22. GitHub
    23. GitLab
    24. Bitbucket
    25. GitHub Actions
    26. GitLab CI/CD
    27. Jenkins (jenkinsci/jenkins)
    28. CircleCI

    AI recommended 28 alternatives but never named nizos/tdd-guard. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to ensure LLM coding agents write tests before implementation and avoid over-engineering?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Guardrails AI
    3. OpenAI Function Calling
    4. Code Llama
    5. CodeGemma
    6. Semantic Kernel

    AI recommended 6 alternatives but never named nizos/tdd-guard. 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 nizos/tdd-guard?
    pass
    AI did not name nizos/tdd-guard — 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 nizos/tdd-guard in production, what risks or prerequisites should they evaluate first?
    pass
    AI named nizos/tdd-guard 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 nizos/tdd-guard solve, and who is the primary audience?
    pass
    AI named nizos/tdd-guard explicitly

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

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nizos/tdd-guard — 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