By Isaac Arnold (isaac@ghostlines.net)
A complete, original content-operations strategy and reference model: a working design for how a content organization should discover topics, plan and produce content, apply human quality gates, distribute what it publishes, measure results, and decide what to refresh, all as one connected system rather than a series of disconnected habits.
Read it directly: content-operating-system.md or content-operating-system.pdf.
Most content teams run on judgment calls made one piece at a time, with no shared record of what was tried, why, or whether it worked. This model treats discovery, planning, production, validation, distribution, and measurement as one connected pipeline with real state, so the system's own performance stays visible enough to improve on purpose instead of by accident.
Content systems thinking, not generic copywriting advice: a coherent operating model covering topic discovery, audience/intent mapping, knowledge architecture, a named production workflow, a specific boundary for where AI-assisted production actually helps, two distinct human quality gates, a distribution plan built at the brief stage, an intent-specific measurement approach, and an explicit, checkable refresh-priority formula (Refresh Priority = (Value × Staleness Risk) ÷ Estimated Fix Effort) with a simple system-flow diagram tying it all together.
It's short by design (about 1,080 words) and organized as ten numbered components of one operating model, followed by a system diagram and a short section on where automation genuinely helps versus where it doesn't. Start at the top and read straight through; each section builds on the one before it.
This is an original strategy and reference model, not a case study. It does not claim any prior client or employer outcomes, and no metrics, thresholds, or results in it are pulled from an actual deployment. It exists to demonstrate how the author thinks about building a content system end to end.