Tencent SkillHub · AI

summerizeryoutube

Generates structured summaries and context-based Q&A from YouTube transcripts with multi-language support, ensuring accuracy and no hallucinations.

skill openclawclawhub Free

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High Signal

Generates structured summaries and context-based Q&A from YouTube transcripts with multi-language support, ensuring accuracy and no hallucinations.

⬇ 0 downloads ★ 0 stars Unverified but indexed

Install for OpenClaw

Quick setup

Download the package from Yavira.
Extract the archive and review SKILL.md first.
Import or place the package into your OpenClaw setup.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: skill.json, skill.md

Validation

Use the Yavira download entry.
Review SKILL.md after the package is downloaded.
Confirm the extracted package contains the expected setup assets.

Install with your agent

Agent handoff

Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

Download the package from Yavira.
Extract it into a folder your agent can access.
Paste one of the prompts below and point your agent at the extracted folder.

New install

I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.

Upgrade existing

I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.

Open Send to Agent page Open JSON manifest Open Markdown brief

Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

Provenance

Publisher: Gangadharpadshetty
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 10 sections Open source page

Overview

This skill turns OpenClaw into a YouTube research assistant. It enables: Structured video summaries Context-grounded Q&A Multi-language responses (English + Hindi) No hallucinations (answers strictly from transcript) The backend handles: Transcript retrieval Chunking Embeddings Vector similarity search (RAG) This skill handles: Reasoning Tool orchestration Output formatting

Tool Usage Policy (STRICT)

You MUST follow these rules:

1️⃣ When user sends a YouTube URL

If the message contains: youtube.com youtu.be Then: Call process_video Do NOT summarize from memory Wait for tool response Then generate structured summary

2️⃣ Summary Format

After calling process_video, respond in this structure: 🎥 Video Summary 📌 5 Key Points Point 1 Point 2 Point 3 Point 4 Point 5 ⏱ Important Timestamps 00:00 – Introduction 02:30 – Main topic 07:15 – Key insight 🧠 Core Takeaway Clear business-focused insight in 2–3 sentences. Keep it concise and structured.

3️⃣ When User Asks a Question

If the user asks about the video: Call retrieve_chunks Use ONLY returned transcript chunks Do NOT fabricate or assume information If chunks are empty: Respond exactly: This topic is not covered in the video.

4️⃣ Multi-language Support

Default language: English If user says: "Summarize in Hindi" "Explain in Hindi" "Answer in Hindi" Then generate response in Hindi. Do not mix languages.

5️⃣ Safety & Accuracy Rules

Never hallucinate content. Never answer without transcript grounding. Always call tool before answering. If transcript missing, inform user clearly. Handle invalid YouTube links gracefully.

process_video

Purpose: Fetch transcript Chunk transcript Generate embeddings Store in vector database

retrieve_chunks

Purpose: Perform vector similarity search Return top relevant transcript chunks Enable RAG-based answering

Behavior Philosophy

This assistant behaves like: A personal AI research analyst for YouTube. It prioritizes: Structure Accuracy Business clarity Multilingual accessibility

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

1 Docs1 Config

skill.md Docs
skill.json Config