Video Production Pack

The Bottleneck in Your Video Pipeline

We built the Video Production Pack because writing ffmpeg commands by hand is a trap. You're a software engineer. You treat video as data. But the data format is a minefield. You have raw footage in ProRes, assets in PNG sequences, and you need to output H.264 in an MP4 container. Or maybe H.265 for streaming. The codec matrix is infinite, and the filter graphs are opaque. You start with a simple color grade, and suddenly you're debugging colortemperature ranges that shift your skin tones into neon green. You add libx264 to fix a library error, and now the color space is wrong. You run the same command on your Mac and it works, but on the CI runner it fails because the hardware acceleration flags are different. You're spending hours debugging media pipelines instead of building features.

The pain isn't just the syntax; it's the lack of structure. You end up with a directory full of final_final_v2.mp4 files and no way to reproduce the build. There's no production plan, no schema validation, and no automated checks. You're guessing at metadata, hoping the captions align, and praying the render finishes before the launch window closes. The skill orchestrates the end-to-end process: scripting, filming, editing, color grading, and publishing. It forces discipline. You define a production-plan.json, and the agent follows the schema. If you're also managing audio assets, you might find the podcast production workflow useful for handling voice tracks, but video adds a layer of complexity with codecs, color spaces, and resolution constraints that audio-only tools don't touch.

What a Broken Pipeline Costs You

Every failed render is a tax on your team. If you're running renders on cloud GPUs, a single 4K job can burn $10 to $50 in compute time. Do a re-render because the audio drift is 50ms, and you've just doubled that cost. Worse, you risk publishing broken content. A video with the wrong aspect ratio, missing captions, or incorrect codec can trigger rejection from distribution platforms. You lose viewer trust instantly. A viewer who clicks a link and sees a black screen or out-of-sync audio bounces immediately. The damage to your channel authority is hard to measure but real.

SMPTE standards exist to prevent this chaos. ^[7] notes that SMPTE establishes standardized video and audio formats to ensure high-quality production and maintain broadcast integrity. Ignoring these principles in your automated pipeline means you're gambling with every upload. If you're also pushing content to live environments, the stakes are even higher; a glitch in a live streaming workflow can alienate an audience in real-time, and recovery is impossible. The cost isn't just dollars; it's momentum. Your team loses focus when the pipeline breaks. You have to context-switch to fix the render script. You miss the Friday deploy. You spend the weekend debugging. A deterministic pipeline eliminates this risk by validating assets and metadata before the render starts, failing fast and failing safely.

How a Team Eliminated Render Failures

Imagine a team building a product update series. They have 20 videos per quarter, need to support 5 languages, and have strict branding guidelines. Before the pack, their process was manual: drag clips into an editor, apply a preset, export, check the file, upload. It took three days per video. They adopted an automated approach inspired by modern broadcast architectures. Unlike traditional SDI workflows, SMPTE ST 2110 enables video, audio, and ancillary data to be transmitted separately over IP ^[3]. They applied this separation to their file pipeline. They moved assets, audio, and captions into distinct directories defined in the production plan. They used the skill's validator to check the JSON schema before any render started.

On the first automated run, the validator caught a missing subtitle file. The pipeline failed fast, saving a two-hour render job. The error message pointed to a scene with a missing asset reference. The team fixed the asset path in the production-plan.json and re-ran. The validation passed. Then they ran the render pipeline. The moviepy-edit.py script processed the scenes, extracted clips, scaled volume, and composited text overlays. The ffmpeg-color-grade.sh script applied the color grade with hardware acceleration. The output was verified by render-check.sh. The check confirmed the output had valid video and audio streams, met the minimum resolution, and matched the frame rate. The team uploaded the video. The metadata was correct. The captions were embedded. The video shipped. This workflow reduces production time from days to hours. It's the same rigor you'd apply when structuring online course production with rigorous asset management, but here we're dealing with the heavier constraints of video codecs and color grading.

What Changes Once the Pack Is Installed

Once the pack is installed, your video production becomes deterministic. The skill.md orchestrator guides the agent through every step, referencing templates and validators. You get a production-grade FFmpeg pipeline for color grading that supports hardware acceleration. The ffmpeg-color-grade.sh template uses colortemperature, vibrance, pseudocolor, and colormap filters with correct parameter ranges. The moviepy-edit.py script handles scene extraction, volume scaling, and compositing, rendering via FFmpeg backend. You no longer guess at codec compatibility. The references include curated guides on MoviePy patterns and FFmpeg filters, plus an overview of SMPTE and ITU-R standards ^[1]. This ensures your outputs adhere to interoperability principles.

The analyze-metadata.sh script runs ffprobe to verify codec compliance and frame rate consistency. The render-check.sh script verifies the output file contains valid video/audio streams and meets minimum resolution constraints, exiting non-zero on failure. This level of validation is critical when you're automating YouTube channel management, where metadata and file integrity directly impact discoverability and upload success. You can also integrate this with podcast workflows if you're handling audio-heavy content, ensuring consistency across your media library. The pack gives you a repeatable, auditable system. You define the plan, the agent executes, and the validators catch errors before they cost you compute or reputation.

What's in the Video Production Pack

• skill.md — Orchestrator skill definition. Guides the agent through the end-to-end video production workflow, referencing all templates, references, scripts, validators, and examples.
• templates/ffmpeg-color-grade.sh — Production-grade FFmpeg pipeline for color grading. Utilizes colortemperature, vibrance, pseudocolor, and colormap filters with hardware acceleration support.
• templates/moviepy-edit.py — Automated editing script using MoviePy. Handles scene extraction, volume scaling, text/image compositing, and final rendering with FFmpeg backend.
• templates/production-plan.json — Structured JSON template for project planning. Defines scenes, assets, color grading targets, and publishing metadata.
• templates/captions.srt — Standard SRT subtitle template with timing and formatting conventions for accessibility and distribution.
• references/ffmpeg-color-filters.md — Curated reference of FFmpeg color grading filters. Embeds canonical parameters, ranges, and examples for colortemperature, vibrance, pseudocolor, colormap, and colorspace.
• references/moviepy-workflow.md — Curated reference of MoviePy editing patterns. Covers clip manipulation, non-destructive modification, compositing, text/image overlays, and rendering workflows.
• references/standards-overview.md — Overview of SMPTE and ITU-R standards relevant to video production. Covers codec interoperability, data encoding (ST 337), and workflow guidelines.
• scripts/validate-project.sh — Validates project directory structure, checks for required assets, verifies file types, and ensures the production plan JSON is present and non-empty.
• scripts/analyze-metadata.sh — Runs ffprobe on input/output files to extract stream info, verify codec compliance, and check resolution/frame rate consistency.
• validators/plan-schema.json — JSON Schema for validating production-plan.json. Enforces required fields for scenes, assets, and publishing targets, exiting non-zero on validation failure.
• examples/worked-example/README.md — Step-by-step walkthrough of a complete video production workflow. Demonstrates using the templates, running validators, and executing the render pipeline.
• examples/render-check.sh — Post-render validation script. Verifies the output file contains valid video/audio streams, meets minimum resolution constraints, and exits non-zero on failure.

Upgrade and Ship

Stop guessing at FFmpeg arguments. Start shipping video with validation, color grading, and publishing automation. Upgrade to Pro to install the Video Production Pack.