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An open-source workflow for turning a single long-form podcast into 20–30 searchable, publishable, and conversion-ready video assets.
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Updated on Jul 17, 2026
How Can One Podcast Be Turned into 30 Video Assets That Keep Generating Leads?
Many companies do not lack video footage.
Podcasts, livestreams, webinars, customer interviews, product demonstrations, and course recordings often add up to dozens of hours sitting on hard drives. The real problem is that, after being published once, this content quickly stops generating value.
Teams spend money on planning, filming, and editing, only to end up with one long-form video. It may receive some views on the day it is published, but after a few days, it is not broken down into more content, it does not create search entry points around customer questions, and it does not continue supporting sales, the company website, or brand-led customer acquisition.
So the question has never been, “Can we edit a few more short videos?” The real question is: how can the one-time production cost of a long-form video be converted into a collection of content assets that can continue to be searched, clicked, understood, and ultimately used to address customer needs over the long term?
That is why we built an open-source workflow: youtube-video-clip-seo-workflow. It is not a “one-click video editing tool.” It is a production pipeline for turning video content into durable assets.
Project URL: https://github.com/dageno-agents/youtube-video-clip-seo-workflow
It does not begin by cutting the video. It begins by understanding the content: analyzing what target customers are searching for, identifying complete ideas within the long-form video that can independently answer those questions, and then planning each clip around those ideas. Once the plan has been confirmed, Codex can use MCP to enter the actual Palmier Pro video timeline and directly create, trim, and organize the clips. The final deliverable is not merely a collection of video files, but a complete publishing asset package containing titles, descriptions, thumbnail copy, pinned comments, and SEO optimization information. Before delivery, every piece of content also passes through a quality check to ensure that it can be understood independently and has clear search value.
The full workflow is:
Long-form video footage → Search-intent map → Complete-idea clip plan → MCP-driven editing in the actual timeline → YouTube publishing assets → Google video SEO pages → Performance review and the next optimization cycle
Each step in this workflow solves a different problem:
Its core value is not merely improving editing efficiency. It is:
Turning a long-form video that would otherwise end after a single publication into a collection of content assets that can continue to be searched, watched, and used to generate customers.
A single recording can be broken into multiple independent topics, each covering a different customer question or search need. Every video also includes a title, thumbnail, description, and CTA. It is no longer just an edited fragment, but a potential lead-generation entry point.
After publication, click-through rate, audience retention, search queries, and inquiry data can be used to determine which questions are genuinely worth continuing to cover, making the next round of content production more precise.
Put simply: previously, one video was published once; now, one recording can continuously generate content, traffic, and customer leads.
The official version of Palmier Pro requires macOS 26 (Tahoe) running on Apple Silicon. The core video editor and MCP server can be used for free without signing in; generative AI features require an account and subscription. Tool capabilities, system requirements, and commands may change, so the official documentation should be checked again before formal deployment.
http://127.0.0.1:19789/mcp.Why start with a short test video? During the first connection, the main things that need to be verified are permissions, media recognition, timeline access, and the export pipeline—not content quality. Testing directly with formal client footage mixes technical issues, prompt issues, and content issues together, while also increasing the risk of accidental changes.
Codex CLI can be installed through the official installation script, npm, or Homebrew. The following is a common npm installation method:
npm install -g @openai/codex
codexOpenAI’s current documentation recommends placing general-purpose personal Skills in $HOME/.agents/skills. The repository can be cloned directly into that directory:
mkdir -p ~/.agents/skills
git clone https://github.com/dageno-agents/youtube-video-clip-seo-workflow.git \
~/.agents/skills/youtube-video-clip-seo-workflowCodex will normally detect Skill changes automatically. Use /skills to view available Skills, or enter $youtube-video-clip-seo-workflow in the prompt to invoke it explicitly. If it does not appear, first check whether SKILL.md is located directly inside the target directory, and then restart Codex.
The most common mistake here
Do not add another folder with the same name. The correct structure is
~/.agents/skills/youtube-video-clip-seo-workflow/SKILL.md, not~/.agents/skills/youtube-video-clip-seo-workflow/youtube-video-clip-seo-workflow/SKILL.md.
Keep Palmier Pro open, and then run the following command in the terminal:
codex mcp add palmier-pro --url http://127.0.0.1:19789/mcpAfter the connection succeeds, do not immediately ask AI to make bulk changes to the timeline. Begin with read-only verification: ask Codex to list the current project, media assets, timeline duration, existing subtitles, resolution, frame rate, and track information. Only proceed to actual editing after it can accurately describe the current project.
client-project/
├── source/
│ ├── webinar_master.mp4
│ └── webinar_clean.srt
├── brand/
│ ├── brand_rules.md
│ └── forbidden_claims.md
├── keyword_targets.csv
├── output/
└── notes/First, create video_brief.json to determine which clips are worth producing, how they should be packaged, where they should be published, and what action viewers should ultimately be guided to take.
{
"target_clip_count": 30,
"platforms": ["youtube"],
"format_mix": {
"shorts": 24,
"standard": 6
},
"audience": "B2B SaaS marketing leaders targeting European and North American markets",
"primary_goal": "Generate product demo bookings and email subscriptions",
"language": "English",
"primary_keywords": ["AI video workflow", "video repurposing"],
"secondary_keywords": ["YouTube SEO", "webinar clips"],
"brand_terms": ["YourBrand"],
"forbidden_claims": ["guaranteed ranking", "zero cost"],
"subtitle_source": "sidecar"
}The next step is to have AI read the subtitles or transcript for the entire video, understand what the podcast actually discusses, and then identify every segment that could potentially stand on its own as an independent video.
When high-quality subtitles are available, this step is faster and more accurate. First check whether the video already includes subtitles, such as external SRT or VTT files, embedded subtitle tracks, or a transcript supplied by the client. Speech-to-text transcription is only necessary when no usable text is available at all.
During the analysis, AI should not merely divide the video into several broad chapters. It should actively search for content that could become an independent video, such as:
For example, the system can first identify 50–80 candidate clips from a podcast, and then select the final 20–30 clips based on content completeness, search value, repetition, and brand relevance.
This approach is more reliable than “watching and editing at the same time until 30 clips have been produced.” It reviews the complete body of content before making selections, so incomplete or low-value segments are not forced into production merely to reach a target number.
The previous step identifies candidate content. This step selects the clips that are genuinely worth producing and generates clip_plan.csv.
Each record only needs to specify:
For example:
| ID | Time | Topic | Search Question | Opening Hook | CTA |
|---|---|---|---|---|---|
clip_001 |
08:42–10:06 | Choosing a cold-start channel for SaaS | How should a SaaS company choose a cold-start channel? | Early failure is often not caused by the channel being too small | View the channel diagnostic checklist |
clip_002 |
16:10–17:28 | Why content gets no traffic | Why does content get no organic traffic? | Publishing frequency is not the primary problem | Read the SEO audit case study |
Click the image to view the sheet.
During review, focus on only three things: whether the content is repetitive, whether it can be understood outside the original video, and whether the idea is complete.
Do not generate 30 clips in bulk on the first attempt. Start by selecting one opinion-based clip, one tutorial clip, and one product-demonstration clip, and have Codex edit them in Palmier Pro through MCP.
Focus on checking:
After the sample clips have been approved, apply the same standards to the remaining clips so that mistakes are not duplicated at scale.
The problem with these steps is that technical filenames, quality metrics, and commercial value are mixed together, making it difficult for readers to follow the main thread.
It is recommended that the main sequence be tightened to:
Complete the editing → Remove unqualified content → Generate publishing assets → Organize the delivery package → Use data to optimize the next round.
After the sample-clip standards have been confirmed, Codex can connect to Palmier Pro through MCP and process the remaining clips in batches according to the editing list.
It will automatically locate the footage, trim the beginning and end, handle pauses, adjust horizontal and vertical framing, add subtitles, and export each video separately.
However, human review is still required after the automated editing is complete. Focus on three things:
The purpose of MCP is to allow AI to enter the actual video timeline and execute operations. It does not guarantee that every finished clip can be published directly without review.
A finished edit is not necessarily worth publishing. Every completed clip needs to pass a quality check that answers five questions:
Clips that fail to meet the standard should either be revised or discarded.
The purpose of a quality gate is to distinguish between “production completed” and “genuinely worth publishing.” Rather than publishing low-quality content in bulk, it is better to retain only the clips that can establish expertise, attract views, and address customer needs.
After a clip passes the quality check, the workflow generates a complete set of publishing assets for it, including:
For content with higher commercial value, a supporting web page can also be generated to embed the video, provide additional written explanation, and capture Google search traffic.
This step is not simply “writing a few lines of copy while you are at it.”
The title and thumbnail determine whether users click. The description helps the platform understand the video topic. The pinned comment and call to action are responsible for turning views into website visits, subscriptions, or inquiries.
Therefore, the final deliverable is not just a video file, but a content asset package that can be published and promoted directly.
After the project is complete, all content should be organized into a clear delivery package containing:
With this package, the client does not need to reorganize the files and can clearly understand:
The specific folder structure can be placed in an appendix rather than displayed in full within the main article, where it could interrupt the reading flow.
The workflow does not end when the videos are published.
The next step is to monitor several core metrics:
This data will tell you:
As a result, the next round of content production no longer depends entirely on experience. It can be continuously optimized using real search, viewing, and conversion data.
At this point, a long-form video has completed the full closed loop: content analysis, clip editing, quality filtering, SEO packaging, client delivery, and performance review.
Assume that a marketing services firm helping companies expand internationally records a 70-minute podcast titled “How B2B SaaS Companies Can Enter Overseas Markets.”
The traditional approach is usually to publish the full podcast and then edit 5–10 short clips featuring memorable quotes.
With this workflow, the team first reorganizes the content around the questions customers are searching for, such as:
These are not merely “ten video titles.” They are ten different customer entry paths. Each path can link to the full podcast, a related article, a case study, a product page, or an inquiry page.
The most valuable part of this workflow is not that “AI can edit videos automatically,” nor is it that “one podcast can be cut into 30 clips.”
What it actually establishes is a complete value chain: first understand what customers are searching for, then identify complete answers within the long-form video; plan each clip around an independent idea and use MCP to implement the plan in the actual timeline; finally, deliver the videos together with titles, descriptions, thumbnails, pinned comments, playlists, and Google video SEO pages.
The final output is not 30 files. It is 30 potential customer entry points.
They can appear in YouTube search, recommendation feeds, Google search, the company website, sales emails, product pages, case-study articles, and social media. A single recording no longer serves only one publication. It becomes a content database that can be continuously broken down, recombined, and optimized.
For companies that already own large libraries of long-form video, this may be more important than continuing to increase production budgets: first turn the content that has already been produced into assets that can actually be discovered and converted.
Updated by
Dageno
Dageno is the research and insights team at Dageno AI, publishing industry reports and expert analysis on AI Search Visibility, Generative Engine Optimization (GEO), and AI-powered search discovery.
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