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My workflow for making videos with Obsidian thumbnail

My workflow for making videos with Obsidian

Nicole van der Hoeven·
5 min read

Based on Nicole van der Hoeven's video on YouTube. If you like this content, support the original creators by watching, liking and subscribing to their content.

TL;DR

Use a single Obsidian video template as the hub for metadata, embedded video, and a pre-/post-production checklist so every new idea starts with the same structure.

Briefing

A repeatable Obsidian-based workflow is what makes high-volume video production sustainable: ideas become structured notes, those notes turn into a filming plan, and a Kanban board tracks every stage from thumbnail to captions to publishing. With 122 videos across four channels in a year (plus the one currently being watched), the core claim is straightforward—without a system to track decisions and tasks, output collapses. The setup matters because it reduces “blank page” work: most topics start from existing notes, so production focuses on presentation rather than reinventing research.

The process begins with a template that acts as a video command center. Each video note stores searchable metadata (including tags and—when used—Fantasy Calendar fields), auto-generates headings from the filename, and embeds the YouTube player via an iframe (chosen because typical link embeds don’t handle Shorts or live streams well). A Dataview inline field can list related plugins or guests, then the note becomes a checklist split into pre-production and post-production.

Pre-production is anchored by two algorithm-sensitive choices: thumbnail and title. Thumbnails are created in Canva using a minimalist approach rather than complex design tools. Titles are tested through TubeBuddy’s keyword scoring: the creator avoids clickbait and keyword stuffing, but uses small title tweaks to pick the version that best matches what the video actually delivers. Content planning then focuses on the most performance-critical segments—the hook and outro—written word-for-word and rehearsed without a teleprompter. The middle of the script is handled with a limited set of bullet points to prevent reading from the screen; filming includes room for ad-libbing, with edits later.

Editing and delivery are treated as a pipeline. After filming, videos are sent to a Dropbox folder for an editor (Eve), who reviews and revises using Frame.io’s timestamped commenting so feedback lands on the exact moment in the timeline. The creator keeps back-and-forth minimal, prioritizing “done” over perfection. Once edited, the creator uploads to YouTube and completes post-production tasks: adding cards at referenced timestamps, pinning a comment that answers recurring questions (like plugins and themes), generating captions through Rev (paid human captions for better translation quality), and using the captions to create chapters for easier navigation.

For tracking at scale, the workflow uses the Kanban plugin with columns such as ideas, on deck, research/plan, film, editing/prepping/previewing, and published—plus a process column for completion. Cards are generated from the video template, automatically creating notes in a designated content folder. A Dataview “video database” note then surfaces all cards/videos in one place, with optional breakdowns by tags, date, or publication status.

Finally, the system extends into scheduling and cross-promotion (Patreon previews, YouTube scheduling, playlists, and promotion on Mastodon and LinkedIn). Fantasy Calendar organizes dates and categories, with an announced migration path to Calendarium. The result is a structured, searchable archive that not only produces videos, but also reconnects them back into the creator’s broader knowledge base.

Cornell Notes

The workflow turns video production into a structured knowledge-management pipeline inside Obsidian. A video template stores metadata, embeds the YouTube link via iframe, and provides a pre-/post-production checklist. Hook and outro are scripted word-for-word, while the middle relies on a small bullet-point outline to allow natural ad-libbing. A Kanban board (ideas → on deck → research/plan → film → editing/prepping/previewing → published) tracks progress, and Dataview queries create a searchable “video database.” Captions and chapters are generated with Rev, and Frame.io timestamp comments streamline editing collaboration. This matters because it prevents research and planning from starting from scratch each time and keeps high output manageable.

How does the workflow prevent video planning from becoming “research from zero” every time?

It relies on a rigorous note-taking process and a template-driven structure. Most topics start from existing notes, so production focuses on how to present and explain rather than discovering everything anew. The template also turns the note into a checklist—metadata, embedded video, related plugins/guests, and step-by-step tasks—so ideation feeds directly into execution.

Why are thumbnails and titles treated as early steps rather than afterthoughts?

Thumbnail and title are described as the most important elements for algorithm performance. The creator starts there: thumbnails are made in Canva with a minimalist style, and titles are tested using TubeBuddy’s keyword score. The goal is not keyword stuffing; it’s choosing a title variant that still matches the actual video content while improving the score.

What’s the scripting approach, and how does it balance structure with spontaneity?

The hook and outro are written word-for-word and rehearsed without a teleprompter, because reading from a screen feels robotic. The middle uses bullet points for reminders rather than a full script, allowing ad-libbing during filming. Later editing removes or refines the improvised parts.

How does collaboration with an editor work without losing context?

Videos are uploaded to Dropbox for editing. The editor (Eve) uses Frame.io (an Adobe product) to leave comments tied to exact timestamps, so feedback is anchored to the moment in the timeline. The creator keeps revisions lightweight—minor adjustments, re-render, then the updated file returns to Dropbox for YouTube upload.

Why pay for human captions when automatic captions are free?

Human captions are paid through Rev to improve quality of life for viewers and to make translation easier. The creator is specifically frustrated by bad translations, so human captions are treated as a quality and accessibility upgrade. Captions also double as a tool for generating chapters and timestamps without rewatching everything from scratch.

How does the Kanban board connect to actual video notes and tracking?

The Kanban plugin creates a board with columns like ideas, on deck, research/plan, film, editing/prepping/previewing, and published (with a process column for completion). Board settings specify which template to use and which note folder to store outputs. Creating a card then generates a note automatically in the content folder, and Dataview queries later pull those cards/notes into a searchable video database.

Review Questions

  1. If you wanted to adapt this workflow, which parts would you keep as “must-have” (template, Kanban, captions, title testing), and which would you simplify? Why?
  2. How does timestamp-based feedback in Frame.io change the editing loop compared with general comments in a document or chat?
  3. What tradeoffs does the workflow make by scripting only the hook and outro word-for-word rather than the entire video?

Key Points

  1. 1

    Use a single Obsidian video template as the hub for metadata, embedded video, and a pre-/post-production checklist so every new idea starts with the same structure.

  2. 2

    Treat thumbnail and title as early, testable decisions: create thumbnails in Canva and use TubeBuddy keyword scoring to choose non-clickbait title variants that still match the content.

  3. 3

    Script the hook and outro word-for-word without a teleprompter, then rely on a small bullet outline for the middle to preserve natural delivery.

  4. 4

    Run editing as a pipeline: send drafts via Dropbox, use Frame.io timestamp comments for precise feedback, then upload the edited file to YouTube.

  5. 5

    Generate paid human captions with Rev to improve translation quality and to derive chapters/timestamps for easier viewer navigation.

  6. 6

    Track production stages with the Kanban plugin (ideas → on deck → research/plan → film → editing/prepping/previewing → published) and use Dataview to maintain a searchable video catalog.

  7. 7

    Use Fantasy Calendar fields (and plan for migration to Calendarium) to keep dates and categories consistent across notes and scheduling.

Highlights

The workflow’s template turns each video note into a searchable production checklist, not just a place to store ideas.
TubeBuddy keyword scoring is used for small title tweaks, with an explicit refusal to chase scores via keyword-heavy clickbait.
Frame.io’s timestamped comments reduce editing back-and-forth by anchoring feedback to the exact moment in the timeline.
Rev captions serve two jobs at once: better translation quality and faster chapter/timestamp creation.
A Kanban board in Obsidian provides the “assembly line” for video work, while Dataview turns completed cards into a live video database.

Topics

  • Obsidian Video workflow
  • Kanban Tracking
  • Fantasy Calendar
  • Captions and Chapters
  • YouTube Title Testing