RRepoGEO

REPOGEO REPORT · LITE

LLM-Red-Team/kimi-cc

Default branch main · commit 6a4a9544 · scanned 5/14/2026, 8:38:05 AM

GitHub: 1,666 stars · 119 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 LLM-Red-Team/kimi-cc, 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
    Clarify repo's core function in README's opening

    Why:

    CURRENT
    # Kimi CC

**中文** | [English](README_EN.md) | [日本語](README_JA.md) | [한국어](README_KO.md) | [Français](README_FR.md) | [Deutsch](README_DE.md) | [Español](README_ES.md) | [Русский](README_RU.md)

使用Kimi的最新模型（kimi-k2-0711-preview）驱动您的Claude Code.
    COPY-PASTE FIX
    # Kimi CC: Integrate Kimi-k2-0711-preview as a Backend for Claude Code

This project provides a simple, cost-effective way to use Kimi's latest model (kimi-k2-0711-preview) to power your Claude Code environment. It is an LLM integration tool, not a red-teaming dataset.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm-integration, code-assistant, kimi-llm, claude-code, ai-tools, llm-backend, cost-optimization, developer-tools
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Add a LICENSE file to the repository root, choosing an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that reflects your intentions for the project's use and distribution.

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 LLM-Red-Team/kimi-cc
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPT-3.5 Turbo
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-3.5 Turbo · recommended 1×
  2. Claude 3 Haiku · recommended 1×
  3. Llama 3 8B Instruct · recommended 1×
  4. Mixtral 8x7B Instruct · recommended 1×
  5. Google Gemini 1.5 Flash · recommended 1×
  • CATEGORY QUERY
    How can I use a different large language model to power my AI code assistant more affordably?
    you: not recommended
    AI recommended (in order):
    1. GPT-3.5 Turbo
    2. Claude 3 Haiku
    3. Llama 3 8B Instruct
    4. Mixtral 8x7B Instruct
    5. Google Gemini 1.5 Flash
    6. Cohere Command R

    AI recommended 6 alternatives but never named LLM-Red-Team/kimi-cc. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help integrate alternative LLM backends into existing developer coding environments?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. LiteLLM
    4. OpenAI Python Library
    5. Hugging Face Transformers
    6. Instructor

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

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

Embed your GEO score

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MARKDOWN (README)
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LLM-Red-Team/kimi-cc — 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