ai-content-collaboration

What it does

The skill provides a framework for how humans and AI collaborate across the entire content workflow, outlining where AI can accelerate work and where humans must own decisions such as editorial judgment, voice, fact verification, and final approval. It emphasizes hybrid workflow patterns, voice ownership preservation, disclosure and transparency, team calibration, and ethical considerations around AI-assisted content production. It also targets triggers related to AI content workflow, AI-assisted writing, hybrid production, AI in editorial, AI slop, AI disclosure, AI usage policy, AI content ethics, voice preservation with AI, and team AI calibration, including scenarios where content feels generic or team AI usage drifts in consistency. Concrete guidance covers participation boundaries, patterns for AI-enabled drafting and editing, and governance around disclosure and policy.

How it works

The skill delineates where AI legitimately participates (e.g., research synthesis, outline generation, first-draft generation, alternative phrasings, copy edit suggestions, summaries, transcription, translation drafts, automated QA) and where humans must own (editorial judgment, voice, fact verification, ethical decisions, reader empathy, tone on hard topics, narrative arc, final approval). It proposes five hybrid workflow patterns: (1) AI-first with heavy human editing; (2) human outline and research plus AI draft with human rewriting; (3) AI as research assistant with human writing; (4) human writing with AI as editor; (5) AI-generated at scale with human sampling. It emphasizes voice preservation through prompt context, voice anchors, mid-draft checks, and final human voice passes. It advises strong briefs, explicit disclosure, calibration via policy and sessions, and clear ownership so AI is accelerating but not deciding content publishability. It references governance steps like policy documentation, calibration sessions, voice library updates, and quality benchmarks to maintain on-voice output.

When to use it

Use when building or refining an AI-content workflow, calibrating team AI usage, addressing generic-looking output, designing disclosure policies, or handling ethics in trust-sensitive or regulated contexts.

What it can touch

Tools and prompts are discussed in abstract terms; the framework addresses when to employ AI for research synthesis, outline and drafting, editing, QA, and disclosure. It does not list specific commands beyond the described patterns and governance steps, and it does not endorse any particular tool as mandatory.

Caveats

The skill describes ownership, disclosure, and calibration requirements and highlights risks of AI slop and loss of brand voice if not managed. It notes that AI cannot replace human decisions on what to publish, who is quoted, or brand voice, and stresses that readers may detect AI influence when overused without human defense against claims. It emphasizes that the framework is tool-agnostic and relies on human oversight and policy enforcement.