RRepoGEO

REPOGEO REPORT · LITE

teaql/teaql-agent-kit

Default branch main · commit 81621003 · scanned 6/7/2026, 12:11:52 AM

GitHub: 2,767 stars · 951 forks

AI VISIBILITY SCORE
33 /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
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 teaql/teaql-agent-kit, 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 to clarify its unique category for AI agents

    Why:

    CURRENT
    TeaQL Agent Kit is the agent-native entry point for the TeaQL ecosystem. It is designed for Codex, Claude Code, and similar coding agents that can read a repository, follow instructions, generate or modify files, run commands, inspect errors, and iterate until the project works.
    COPY-PASTE FIX
    TeaQL Agent Kit provides **semantic guardrails and deterministic execution for AI coding agents**, ensuring generated code is reliable, type-safe, and adheres to domain models. Unlike generic testing frameworks or simple code generators, TeaQL empowers agents like Codex and Claude Code to build robust Java and Rust applications with architectural integrity.
  • mediumabout#2
    Expand the repository description to highlight semantic guardrails for AI agents

    Why:

    CURRENT
    Deterministic execution for non-deterministic AI.
    COPY-PASTE FIX
    Semantic guardrails and deterministic execution for AI coding agents, ensuring reliable, type-safe code generation that adheres to domain models.
  • mediumtopics#3
    Add more specific topics to reinforce the 'AI agent guardrails' category

    Why:

    CURRENT
    agentic-coding, ai-coding, code-generation, coding-agent, ddd, developer-tools, domain-driven-design, java, rust, semantic-guardrails, semantic-modeling, teaql
    COPY-PASTE FIX
    agentic-coding, ai-coding, code-generation, coding-agent, ddd, developer-tools, domain-driven-design, java, rust, semantic-guardrails, semantic-modeling, teaql, ai-agent-guardrails, reliable-ai-code, deterministic-ai-code

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 teaql/teaql-agent-kit
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
JUnit
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. JUnit · recommended 1×
  2. pytest-dev/pytest · recommended 1×
  3. facebook/jest · recommended 1×
  4. nunit/nunit · recommended 1×
  5. cypress-io/cypress · recommended 1×
  • CATEGORY QUERY
    How to ensure generated code from AI agents is reliable and follows domain models?
    you: not recommended
    AI recommended (in order):
    1. JUnit
    2. Pytest (pytest-dev/pytest)
    3. Jest (facebook/jest)
    4. NUnit (nunit/nunit)
    5. Cypress (cypress-io/cypress)
    6. Selenium (SeleniumHQ/selenium)
    7. SonarQube (SonarSource/sonarqube)
    8. Pylint (PyCQA/pylint)
    9. ESLint (eslint/eslint)
    10. Checkstyle (checkstyle/checkstyle)
    11. JSON Schema
    12. OpenAPI/Swagger
    13. Pydantic (pydantic/pydantic)
    14. Zod (colinhacks/zod)
    15. Apollo Server (apollographql/apollo-server)
    16. GraphQL Yoga (dotansimha/graphql-yoga)
    17. SpecFlow (SpecFlowOSS/SpecFlow)
    18. Cucumber (cucumber/cucumber)
    19. Dafny (dafny-lang/dafny)
    20. Coq (coq/coq)
    21. GitHub Pull Requests
    22. GitLab Merge Requests

    AI recommended 22 alternatives but never named teaql/teaql-agent-kit. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks help AI coding agents generate type-safe code for Java applications?
    you: not recommended
    AI recommended (in order):
    1. OpenAPI Generator
    2. JHipster
    3. Spring Initializr
    4. MapStruct
    5. Immutables
    6. Lombok
    7. QueryDSL

    AI recommended 7 alternatives but never named teaql/teaql-agent-kit. 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 teaql/teaql-agent-kit?
    pass
    AI did not name teaql/teaql-agent-kit — 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 teaql/teaql-agent-kit in production, what risks or prerequisites should they evaluate first?
    pass
    AI named teaql/teaql-agent-kit 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 teaql/teaql-agent-kit solve, and who is the primary audience?
    pass
    AI named teaql/teaql-agent-kit explicitly

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

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teaql/teaql-agent-kit — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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