RRepoGEO

REPOGEO REPORT · LITE

modelscope/FunClip

Default branch main · commit eaa67d1a · scanned 6/26/2026, 2:23:03 AM

GitHub: 5,860 stars · 705 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
40 /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
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 modelscope/FunClip, 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 paragraph to highlight LLM-powered clipping for content creators

    Why:

    CURRENT
    FunClip is a fully open-source, locally deployed automated video clipping tool. It leverages Alibaba TONGYI speech lab's open-source FunASR Paraformer series models to perform speech recognition on videos. Then, users can freely choose text segments or speakers from the recognition results and click the clip button to obtain the video clip corresponding to the selected segments (Quick Experience Modelscope⭐ HuggingFace🤗).
    COPY-PASTE FIX
    FunClip is an open-source, locally deployed **AI video clipping tool** that empowers content creators to effortlessly generate engaging short clips. It integrates **LLM-based smart clipping** with state-of-the-art speech recognition (FunASR Paraformer) to automatically identify and extract key video segments based on speech content, topics, or user-defined prompts.
  • mediumreadme#2
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    ### Why FunClip? (vs. ASR APIs & ML Frameworks)

Unlike raw ASR APIs (e.g., AssemblyAI, Deepgram) which primarily provide transcription, FunClip offers a complete, integrated solution for smart video clipping. While general LLM frameworks (e.g., LlamaIndex, LangChain) require significant development to build applications, FunClip provides an out-of-the-box, user-friendly application specifically designed for content creators to automate video segment extraction.
  • lowreadme#3
    Expand the 'Usage' or 'Examples' section with specific LLM clipping scenarios

    Why:

    COPY-PASTE FIX
    Expand the 'Usage' or create an 'Examples' section to include specific, copy-pasteable examples or screenshots demonstrating how to use the LLM-based clipping feature. For instance, show how to use prompts like 'Clip all segments discussing [topic X]' or 'Generate a highlight reel of [speaker Y]'s key points' to leverage the LLM for intelligent video segment extraction.

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 modelscope/FunClip
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AssemblyAI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. AssemblyAI · recommended 2×
  2. FFmpeg · recommended 2×
  3. Deepgram · recommended 1×
  4. Google Cloud Speech-to-Text API · recommended 1×
  5. AWS Transcribe · recommended 1×
  • CATEGORY QUERY
    How to automatically clip video segments based on speech content using AI?
    you: not recommended
    AI recommended (in order):
    1. AssemblyAI
    2. FFmpeg
    3. Deepgram
    4. Google Cloud Speech-to-Text API
    5. AWS Transcribe
    6. Azure Cognitive Services Speech
    7. Veed.io

    AI recommended 7 alternatives but never named modelscope/FunClip. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an open-source tool for video transcription and smart clipping with LLM capabilities.
    you: not recommended
    AI recommended (in order):
    1. Whisper (openai/whisper)
    2. FFmpeg
    3. LlamaIndex (llamaindex/llamaindex)
    4. LangChain (langchain-ai/langchain)
    5. Hugging Face Transformers (huggingface/transformers)
    6. Vosk (alphacep/vosk-api)
    7. AssemblyAI
    8. Llama 2
    9. Mistral
    10. PyTorchVideo (pytorch/pytorchvideo)
    11. Wav2Vec2
    12. HuBERT
    13. DeepSpeech (mozilla/DeepSpeech)

    AI recommended 13 alternatives but never named modelscope/FunClip. 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 modelscope/FunClip?
    pass
    AI named modelscope/FunClip explicitly

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

  • If a team adopts modelscope/FunClip in production, what risks or prerequisites should they evaluate first?
    pass
    AI named modelscope/FunClip 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 modelscope/FunClip solve, and who is the primary audience?
    pass
    AI named modelscope/FunClip explicitly

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite