RRepoGEO

REPOGEO REPORT · LITE

LLM-Red-Team/kimi-cc

Default branch main · commit 6a4a9544 · scanned 6/24/2026, 9:03:05 PM

GitHub: 1,664 stars · 120 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
30 /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
3 / 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
    Reposition the README's opening to clarify its purpose as a cost-optimized Claude Code backend

    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

**Kimi CC allows you to use Kimi's latest models (like kimi-k2-0711-preview) as a cost-effective alternative backend for your existing Claude Code projects.**

**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。
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm, kimi, claude, api-proxy, cost-optimization, code-assistant, llm-backend
  • highlicense#3
    Add a LICENSE file to clarify usage terms

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0).

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
LiteLLM
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LiteLLM · recommended 1×
  2. LangChain · recommended 1×
  3. LlamaIndex · recommended 1×
  4. OpenRouter.ai · recommended 1×
  5. Marvin · recommended 1×
  • CATEGORY QUERY
    Are there tools to use different LLMs as a backend for existing code assistants?
    you: not recommended
    AI recommended (in order):
    1. LiteLLM
    2. LangChain
    3. LlamaIndex
    4. OpenRouter.ai
    5. Marvin
    6. Guidance
    7. Instructor

    AI recommended 7 alternatives but never named LLM-Red-Team/kimi-cc. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for ways to optimize expenses when utilizing premium AI coding models.
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. GPT-4
    3. GPT-3.5 Turbo
    4. Anthropic Claude
    5. Claude 3 Haiku
    6. Google Gemini API
    7. Gemini 1.5 Flash
    8. Mistral AI
    9. Mistral Large
    10. Mixtral 8x7B
    11. Anyscale Endpoints
    12. Hugging Face Inference Endpoints
    13. CodeLlama
    14. StarCoder
    15. DeepSeek Coder
    16. Ollama
    17. vLLM

    AI recommended 17 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 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?

  • 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

Drop this badge into the README of LLM-Red-Team/kimi-cc. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/LLM-Red-Team/kimi-cc.svg)](https://repogeo.com/en/r/LLM-Red-Team/kimi-cc)
HTML
<a href="https://repogeo.com/en/r/LLM-Red-Team/kimi-cc"><img src="https://repogeo.com/badge/LLM-Red-Team/kimi-cc.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

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