The problem

Manual content repurposing often leads to hallucinations, lost context, or generic fluff that ignores the original material's nuance. Creators struggle to maintain traceability between their source assets and new derivative formats, leading to inconsistent quality and brand drift.

What it does

• Extracts atomic facts and constraints from source material while assigning stable IDs for full traceability.
• Generates an evidence-linked derivative-content matrix including hooks, formats, and reuse boundaries.
• Identifies and preserves conflicts or unknowns rather than guessing, ensuring humans make critical editorial decisions.
• Executes automated quality gates to verify every claim against the provided evidence ledger.
• Maintains an update protocol that allows for batch processing and reconciliation as new sources are added.

Why this beats prompting it yourself

Generic prompts often hallucinate facts or ignore source constraints to create "creative" output. This skill enforces a structured logic gate that prioritizes accuracy and traceability, ensuring every hook or post variant is grounded in actual data rather than LLM guesswork.

Use cases

• Transforming a long-form podcast or video script into a multi-platform social media campaign.
• Mapping technical documentation into a series of educational threads with evidence backing.
• Managing music workflows that require strict adherence to rights, formats, and reuse boundaries.
• Reconciling conflicting information across multiple source assets for a unified content strategy.

Known limitations

Requires an initial source asset to be effective. It will not manufacture testimonials or misrepresent content, and cannot take external actions like posting to social media directly.