Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Developer Tools
Bilibili Up To Kb
Convert Bilibili (B站) videos into a searchable text knowledge base. Supports single videos and batch processing of entire UP主 channels. Uses local whisper.cp...
skill openclawclawhub Free
Convert Bilibili (B站) videos into a searchable text knowledge base. Supports single videos and batch processing of entire UP主 channels. Uses local whisper.cp...
⬇ 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.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/dependencies.md, scripts/batch_channel.sh, scripts/batch_clean.sh, scripts/clean_transcript.sh, scripts/generate_index.sh
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.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- shanjiaming
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Bilibili UP to KB
Convert B站 videos (single or entire channels) into cleaned, structured text knowledge bases.
Design Principle
Agent orchestrates, scripts execute. The agent's job is to decide WHAT to do and kick off the right script. All mechanical, repetitive work (downloading, transcribing, cleaning) is handled by shell scripts with built-in parallelism. The agent NEVER loops through videos one by one — it runs ONE command and the script handles concurrency internally.
Output Structure
kb/UP主名_UID/ ├── BV号_视频标题.txt # Cleaned transcript (user-facing) ├── BV号_视频标题.meta.json # Video metadata ├── index.md # Summary index └── .raw/ # Hidden: whisper transcripts (if any) └── BV号_视频标题.txt Key decisions: File names include title for readability (BV1xxx_标题.txt) Folder includes UP主 name (UP主名_UID/) Raw transcripts hidden in .raw/ No _clean suffix — clean files are the main files Per-video .meta.json with title, uploader, duration, etc.
Step 1: Download AI subtitles (fast, high concurrency OK)
# 30-50 concurrent is fine — B站 CDN handles it scripts/batch_channel.sh "https://space.bilibili.com/UID/" ./kb/output zh 0 30
Step 2: For videos without AI subtitles, run whisper (LOW concurrency!)
# Metal GPU can only handle 1-4 parallel whisper instances # More = slower total (GPU saturation) scripts/batch_channel.sh "https://space.bilibili.com/UID/" ./kb/output zh 0 2 --whisper-only
Step 3: Clean + Index
# Clean whisper transcripts (AI subtitles skip automatically) scripts/batch_clean.sh ./kb/UP主名_UID/ scripts/generate_index.sh ./kb/UP主名_UID/
Concurrency Guide
Critical: Different stages need different concurrency! StageBottleneckRecommendedWhyAI subtitle downloadNetwork30-50B站 CDN handles high parallelWhisper transcribeMetal GPU1-4GPU饱和，多了反而慢Transcript cleaningAPI rate limitALL (0)Network I/O only
Quick Start — Single Video
scripts/transcribe.sh "https://www.bilibili.com/video/BV..." ./output zh
Transcript Cleaning
AI subtitles are clean enough — skipped by default. SourceCleaning needed?B站 AI subtitlesNo — directly usablewhisper fallbackYes — goes through cleaning Cleaning uses opencode/minimax-m2.5-free: Fix homophones and garbled words Add punctuation Output MUST be Simplified Chinese Keep uncertain proper nouns unchanged Never substitute one real term for another Chunk size: 80 lines. Retry: 3 attempts with 3s delay.
⚠️ Long-running tasks
Use nohup to avoid session compaction killing processes: nohup bash scripts/batch_clean.sh ./kb/UP主名_UID/ 0 80 > /tmp/clean.log 2>&1 & batch_clean.sh is resumable — safe to re-run after interruption.
⚠️ Large Channel Handling (1000+ videos)
Script auto-detects large channels (>800 videos) and fetches in chunks to avoid timeout. # Auto-chunked, just re-run to resume nohup bash scripts/batch_channel.sh "https://space.bilibili.com/UID/" ./kb/output > /tmp/batch.log 2>&1 & If still fails, manually fetch URL list: for i in $(seq 1 500 2000); do yt-dlp --flat-playlist --playlist-start $i --playlist-end $((i+499)) \ --print url "https://space.bilibili.com/UID/" >> /tmp/urls.txt done cat /tmp/urls.txt | xargs -P 20 -I {} bash scripts/transcribe.sh {} ./kb/OUTPUT zh
⚠️ Thermal & Fan Warning
Keep system cool — avoid fan spin! StageRiskMitigationWhisper (GPU)HIGHKeep concurrency ≤2, monitor tempsAI subtitle downloadLowCan run 30-50 concurrentCleaning (API)NonePure network I/O, no local load If fans start spinning: Stop whisper processes immediately Wait for cooldown Resume with lower concurrency (1-2) # Check GPU temp (if using CUDA) nvidia-smi # Check Mac CPU/GPU temp sudo powermetrics --sample-rate 1000 -i 1 -n 1 | grep -E "CPU|GPU"
Dependencies
Required: yt-dlp, ffmpeg, whisper.cpp (+ model), opencode CLI Optional: Browser cookies for member-only content (--cookies-from-browser chrome)
Environment Variables
VariableDefaultDescriptionWHISPER_CLIwhisper-cliPath to whisper.cppWHISPER_MODEL~/.whisper-cpp/ggml-small.binWhisper modelOPENCODE_BIN~/.opencode/bin/opencodeopencode CLICLEAN_MODELopencode/minimax-m2.5-freeCleaning model
Tips
China users: Use hf-mirror.com for whisper model Long videos (1h+): Auto-segmented into 10-min chunks Resumable: All batch scripts skip already-processed files
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Scripts2 Docs
- SKILL.md
- references/dependencies.md
- scripts/batch_channel.sh
- scripts/batch_clean.sh
- scripts/clean_transcript.sh
- scripts/generate_index.sh