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98db898c0b
Completes the Windows-gating coverage for the built-in skills/ tree. Every
bundled SKILL.md now carries an explicit platforms: declaration so the
loader (agent.skill_utils.skill_matches_platform) can skip-load skills
that don't fit the current OS.
74 skills declared cross-platform (platforms: [linux, macos, windows]):
Creative (16): ascii-art, ascii-video, architecture-diagram, baoyu-comic,
baoyu-infographic, claude-design, creative-ideation, design-md,
excalidraw, humanizer, manim-video, p5js, pixel-art,
popular-web-designs, pretext, sketch, songwriting-and-ai-music,
touchdesigner-mcp
Autonomous agents: claude-code, codex, hermes-agent, opencode
Data/devops: jupyter-live-kernel, kanban-orchestrator, kanban-worker,
webhook-subscriptions, dogfood, codebase-inspection
GitHub: github-auth, github-code-review, github-issues,
github-pr-workflow, github-repo-management
Media: gif-search, heartmula, songsee, spotify, youtube-content
MCP / email / gaming / notes / smart-home: native-mcp, himalaya,
pokemon-player, obsidian, openhue
mlops (non-broken): weights-and-biases, huggingface-hub, llama-cpp,
outlines, segment-anything-model, dspy, trl-fine-tuning
Productivity: airtable, google-workspace, linear, maps, nano-pdf,
notion, ocr-and-documents, powerpoint
Red-teaming / research: godmode, arxiv, blogwatcher, llm-wiki,
polymarket
Software-dev: debugging-hermes-tui-commands, hermes-agent-skill-authoring,
node-inspect-debugger, plan, requesting-code-review, spike,
subagent-driven-development, systematic-debugging,
test-driven-development, writing-plans
Misc: yuanbao
5 skills gated from Windows (platforms: [linux, macos]):
mlops/inference/vllm (serving-llms-vllm)
vLLM is officially Linux-only; Windows requires WSL.
mlops/training/axolotl
Axolotl's flash-attn + deepspeed + bitsandbytes stack is Linux-first.
mlops/training/unsloth
Requires Triton + xformers + flash-attn — Linux only in practice.
mlops/models/audiocraft (audiocraft-audio-generation)
torchaudio ffmpeg backend + encodec dependencies are Linux-first.
mlops/inference/obliteratus
Research abliteration workflow; relies on Linux-focused pytorch
kernels and MLX — no first-class Windows path.
Same strict-over-lenient policy as the optional-skills sweep: when the
underlying tool's Windows support is rough, missing, or WSL-only, gate the
skill. Easier to un-gate after verified Windows support lands than to leak
partial support that manifests as mid-task failures.
Combined with prior commits in this branch, every bundled SKILL.md
(skills/ + optional-skills/) now has a platforms: declaration.
73 lines
3.1 KiB
Markdown
73 lines
3.1 KiB
Markdown
---
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name: youtube-content
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description: "YouTube transcripts to summaries, threads, blogs."
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platforms: [linux, macos, windows]
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---
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# YouTube Content Tool
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## When to use
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Use when the user shares a YouTube URL or video link, asks to summarize a video, requests a transcript, or wants to extract and reformat content from any YouTube video. Transforms transcripts into structured content (chapters, summaries, threads, blog posts).
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Extract transcripts from YouTube videos and convert them into useful formats.
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## Setup
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```bash
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pip install youtube-transcript-api
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```
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## Helper Script
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`SKILL_DIR` is the directory containing this SKILL.md file. The script accepts any standard YouTube URL format, short links (youtu.be), shorts, embeds, live links, or a raw 11-character video ID.
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```bash
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# JSON output with metadata
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python3 SKILL_DIR/scripts/fetch_transcript.py "https://youtube.com/watch?v=VIDEO_ID"
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# Plain text (good for piping into further processing)
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python3 SKILL_DIR/scripts/fetch_transcript.py "URL" --text-only
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# With timestamps
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python3 SKILL_DIR/scripts/fetch_transcript.py "URL" --timestamps
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# Specific language with fallback chain
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python3 SKILL_DIR/scripts/fetch_transcript.py "URL" --language tr,en
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```
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## Output Formats
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After fetching the transcript, format it based on what the user asks for:
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- **Chapters**: Group by topic shifts, output timestamped chapter list
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- **Summary**: Concise 5-10 sentence overview of the entire video
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- **Chapter summaries**: Chapters with a short paragraph summary for each
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- **Thread**: Twitter/X thread format — numbered posts, each under 280 chars
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- **Blog post**: Full article with title, sections, and key takeaways
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- **Quotes**: Notable quotes with timestamps
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### Example — Chapters Output
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```
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00:00 Introduction — host opens with the problem statement
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03:45 Background — prior work and why existing solutions fall short
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12:20 Core method — walkthrough of the proposed approach
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24:10 Results — benchmark comparisons and key takeaways
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31:55 Q&A — audience questions on scalability and next steps
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```
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## Workflow
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1. **Fetch** the transcript using the helper script with `--text-only --timestamps`.
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2. **Validate**: confirm the output is non-empty and in the expected language. If empty, retry without `--language` to get any available transcript. If still empty, tell the user the video likely has transcripts disabled.
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3. **Chunk if needed**: if the transcript exceeds ~50K characters, split into overlapping chunks (~40K with 2K overlap) and summarize each chunk before merging.
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4. **Transform** into the requested output format. If the user did not specify a format, default to a summary.
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5. **Verify**: re-read the transformed output to check for coherence, correct timestamps, and completeness before presenting.
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## Error Handling
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- **Transcript disabled**: tell the user; suggest they check if subtitles are available on the video page.
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- **Private/unavailable video**: relay the error and ask the user to verify the URL.
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- **No matching language**: retry without `--language` to fetch any available transcript, then note the actual language to the user.
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- **Dependency missing**: run `pip install youtube-transcript-api` and retry.
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