Claude Code + Blotato = Content Machine

A skill is analogous to a recipe: it defines the dish name, ingredients, steps, and expected output. When you ask Claude Code to perform a task—like writing a LinkedIn post—it consults the corresponding skill document to ensure the result is repeatable and aligned with your preferences. Because the workflow is codified, the agent can iterate and self-correct: if it encounters a bug (e.g., YouTube blocked by web fetch), it logs the issue in the skill document and tries alternative approaches.

Every time you run the workflow, the skill improves. The first run may produce text but fail on visuals; the second run fixes the bug and updates the skill. Over ten iterations with user feedback, the skill becomes battle-tested and ready for full automation. This means you can eventually set up triggers (e.g., «Every time I post a YouTube video, run this skill») and trust the output without manual intervention.

The demo skill, «Repurpose YouTube Video,» was built interactively: Claude Code asked clarifying questions (programming language, tone, visual style, review flow) until it was 95% confident it could execute. This conversational setup ensures the skill matches your specific needs from the outset.

The creator pasted a YouTube link (a video about building websites with Claude Code) and requested LinkedIn, X, and Instagram posts with custom visuals. Claude Code read the skill, executed Python scripts, and successfully generated text-based posts. However, the visual creation failed: Blotato rejected the profile image because it exceeded the API's size limit, and YouTube was blocked by the web fetch tool.

Claude Code logged these issues in a «Known Issues and Findings» section within the skill document. On the second run—prompted by «Try to create the visuals again. Make sure they are images, not videos»—the agent resized the profile picture, switched to an alternative transcript extraction method, and successfully generated all assets: a LinkedIn whiteboard infographic, an Instagram five-slide carousel with profile picture and blue verification badge, and an X quote card. Each asset was saved to a drafts folder for review.

The iterative refinement continued: the creator requested that Instagram carousels include the profile picture and blue check on every slide. Claude Code updated the skill, regenerated the visuals, and delivered improved output. This cycle—prompt, execute, diagnose, fix, update skill—demonstrates how agentic AI learns from failure and converges on a production-ready workflow.

The demo ran with minimal context: no brand guidelines, no past post examples, and only a profile picture for brand assets. The creator emphasizes that output quality will compound as you add more context—logo files, color palettes, sample posts, tone-of-voice documents—and iterate the skill over ten or more runs. Each refinement tightens the prompt, improves visual generation, and reduces manual editing.

To scale, build additional skills within the same Blotato project: inspiration sourcing (scraping competitor content or trending topics), TikTok video repurposing, or automated scheduling tied to YouTube upload triggers. The modular skill architecture means each workflow is self-contained and can be tested, refined, and eventually automated independently. The creator recommends joining the free School community for resources and the paid AI Automation Society Plus (3,000+ members) for deeper collaboration on production AI systems.