Skills for Real Engineers. Straight from my .agents directory.
ai-content-collaboration
Agent skill fromrampstackco/claude-skills
How humans and AI compose in content workflows. Where AI legitimately participates, where humans must own, hybrid workflow patterns, voice ownership preservation, the AI slop problem, disclosure and transparency, team calibration, and the ethics of intellectually honest AI-assisted content production. Triggers on AI content workflow, AI-assisted writing, hybrid content production, AI in editorial, AI slop, AI disclosure, AI usage policy, AI content ethics, voice preservation with AI, team AI calibration. Also triggers when content feels generic despite quality tools, when team AI usage has drifted into inconsistency, or when a regulated or trust-sensitive context requires explicit AI policy.
How to install ai-content-collaboration
npx skills add rampstackco/claude-skills# or add only this skill to Claude Code: copy the folder
skills/ai-content-collaboration/
# into
~/.claude/skills/ai-content-collaboration/ (all projects)
.claude/skills/ai-content-collaboration/ (one project)
1# AI Content Collaboration23A senior editorial leader's playbook for how humans and AI compose in content workflows. Pragmatic, tool-agnostic, honest about both what AI in the loop enables and what it threatens.45Most content programs in 2026 use AI somewhere in the workflow. Pretending otherwise is dishonest; treating AI as a magic content factory is the failure mode this skill exists to prevent. The discipline is in between: knowing where AI legitimately accelerates, where humans must own, what hybrid patterns produce work that earns reader trust, and what crosses the line into AI slop or intellectual dishonesty.67This skill is the WORKFLOW layer that composes with every other content skill. Briefs can be AI-assisted; hub architectures can be AI-assisted; programmatic SEO is almost always AI-involved; editorial QA now includes AI-content audit by necessity. The collaboration discipline applies to all production stages, not to a single artifact type.89The voice is pragmatic and tool-agnostic deliberately. The methodology applies whether the AI in your loop is one of the major commercial models, an open-source model, or whatever ships next quarter. What stays constant is the workflow shape, the participation boundaries, the voice ownership question, and the ethical frame. What changes is which specific tool you reach for, which is implementation work that varies by team and budget.10…
Excerpt shown under the repository's MIT license.
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