YouTube Channel Parse#

Overview#

This skill supports two workflows.

For an individual video:

• parse a specific video URL
• fetch or generate a transcript
• summarize the video
• extract structured notes or reusable outputs

For a YouTube channel:

• inventory channel videos
• select which videos to analyze by criteria such as topic, date range, speakers, or other metadata filters
• retrieve transcripts for the selected subset
• produce summaries and structured outputs for the filtered set

Inputs#

Accept these inputs:

• channel_url or video_url
• optional since_date
• optional until_date
• optional topic_filter
• optional speaker_filter
• optional metadata_filters
• output_prefix
• optional whisper_model
• optional preferred_language

Workflow Selection#

Choose the workflow from the provided URL:

• if the user provides video_url, run the individual video workflow
• if the user provides channel_url, run the channel workflow
• if the user provides both, use the video workflow for the specific video request and the channel workflow for the filtered channel request

Check environment access early.

If the environment blocks YouTube network access, stop retrying after confirming the restriction and tell the user how to enable access before continuing.

Individual Video Workflow#

1. Normalize the video URL and derive the video id.
2. Fetch video metadata.
3. Retrieve the transcript.
4. Produce a transcript file.
5. Produce a markdown summary or notes file when the user asks for analysis, findings, reusable notes, or a summary report.
6. When a summary report is requested, write an extended summary for each selected video that covers the main plot, notable facts, insights, opinions, and outcome rather than a one-line description.
7. Produce structured JSON output when the task calls for downstream reuse.

Channel Workflow#

1. Inventory the channel videos.
2. Normalize metadata for each video.
3. Apply selection criteria such as date range, topic, speakers, or metadata filters.
4. Write the filtered dataset before transcript work when the selection itself is part of the deliverable.
5. Retrieve transcripts for the selected subset.
6. Produce summaries, notes, and reports for the filtered set.
7. When the deliverable is a summary report, treat the script outputs as source material and write the final report as a synthesized research memo rather than a transcript-derived bullet list.
8. For conference, event, or multi-video reports, include a conference-wide or collection-wide throughline section before the per-video notes.
9. For each video in a full report, prefer a structure with Thesis, Insights, Facts, and Opinions and framing.

Filtering Rules#

Apply filters in this order:

1. date window
2. metadata filters
3. topic filter
4. speaker filter

Use metadata-based filtering before transcript retrieval when that narrows the set efficiently.

Use transcript-based filtering when the requested criteria depend on spoken content, such as speaker references, repeated themes, or terms not present in the title or description.

Read detailed filter semantics from references/filtering.md when the request depends on nuanced selection logic.

Transcript Workflow#

Retrieve direct transcripts first.

Use the languages exposed by the video metadata when subtitles or captions are available. Prefer the original track language ahead of translated tracks.

Detect the transcript language after retrieval and carry that language into phrase extraction, summaries, and report outputs.

When direct transcripts are unavailable, try subtitle retrieval before full audio transcription.

When subtitle retrieval is unavailable, generate transcripts from downloaded audio and continue the workflow.

Reuse existing transcript files and cached audio files when they already match the current request.

Use the multilingual Whisper models such as tiny or small by default. Use tiny.en only for explicitly English-only workflows.

Pass --preferred-language when the user asks for a specific transcript language or when the metadata exposes several useful caption tracks.

If yt-dlp is unavailable, rerun the workflow with uv run --with yt-dlp ... instead of raw python.

If caption, subtitle, or media requests fail with 429 Too Many Requests, retry a small number of times with backoff before treating the fetch as unavailable.

If metadata retrieval works but captions or audio fail with bot-detection, 403, or sign-in verification errors, treat that as a content-access restriction and explain the environment limits clearly.

Outputs#

• channel inventory in JSON and markdown when needed
• filtered video dataset in JSON, CSV, and markdown when needed
• transcript files per video
• filtered subsets by topic, speaker, date, or metadata criteria
• markdown report with findings, summaries, and caveats

Use markdown outputs for summaries, findings, reusable notes, and channel-level reports.

For report outputs, prefer extended summaries over one-line blurbs.

For high-quality research reports, use the generated JSON, transcripts, and script summaries as inputs to a cleaner synthesized markdown narrative.

Read naming and directory conventions from references/outputs.md when writing artifacts.

Capabilities#

WorkflowCapabilityResultChannelAnalyze the entire channelInventory, selection, transcripts, and channel-level outputsVideoAnalyze an individual videoTranscript, summary, notes, and structured outputsChannelFilter videos by date windowA narrowed candidate set before transcript workChannelFilter or group videos by topic, date range, speakers, or metadata criteriaA selected subset for analysis and reportingVideo or ChannelFetch direct transcripts when availableFaster transcript retrieval from existing sourcesVideo or ChannelGenerate transcripts from downloaded audio when neededContinued execution when direct transcripts are unavailableVideo or ChannelGenerate concise summaries and reusable artifactsMarkdown reports, notes, and structured data outputsVideo or ChannelGenerate extended report summaries on requestPer-video report entries that cover plot, facts, insights, opinions, and outcomeChannelGenerate synthesized research memosA report with collection-wide throughline plus per-video thesis, insights, facts, and framing

Resources#

Use these bundled scripts:

• fetch_channel.py for inventory, date filtering, and metadata normalization
• transcribe_with_fallback.py for transcript API attempts, yt-dlp audio download, and local Whisper transcription
• build_report.py for topic grouping, summaries, and markdown or CSV outputs

Use these reference files:

• references/dependencies.md
• references/filtering.md
• references/outputs.md

Dependencies#

This skill uses:

• yt-dlp for channel inventory and audio download
• youtube-transcript-api for direct transcript fetch
• faster-whisper for local transcription fallback

Use commands such as:

bash

uv run --with yt-dlp --with youtube-transcript-api --with faster-whisper python scripts/<script>.py ...

Pass --no-check-certificates to yt-dlp when the environment requires it.

Read references/dependencies.md for command patterns and dependency notes.