#Content Operations

How to diagnose pillar quality across three layers—structure, vocabulary, and prose density—and when to reclassify a cluster.

No inline code and business-decision subjects: two must_avoid constraints that preserve prose density in business-cluster pillars.

Adding sister_pillar_slug to brief YAML separates sister pillars from spokes, aligning graph generation and cross-link skills.

Cluster owns distribution/KPI; persona owns narrative and terminology. Separate both in SSOT and run through the pipeline.

Implement cluster SEO by using pillar primary keywords as the cluster axis and passing them down to spokes as secondary keywords.

Prevent writing without defining personas using blog-gate G11. Learn how machine detection ensures strategic quality assurance.

Narrowing a pillar's audience.primary to one reveals excluded personas as cluster design gaps, clarifying sister pillar and spoke roles.

Prevent referring to Yakumo as a "company" with a context-aware gate. Learn to combine proximity analysis, allow-lists, and regex.

A pattern for extending blog-gate G-rules to structurally detect missing SEO, audience, and cluster information. This article outlines how to design systems that allow machines to catch architectural failures, such as missing pillar strategy layers, before content production begins.

A hybrid design for detecting corporate terms using forbiddenWords and proximity rules to protect the solo practitioner narrative.

AI-agent video at scale: costs, quality gates, review design, and mid-term ROI — a no-code guide for execs and marketing leads.

Fix false positives in quality gates using stripCodeContexts and proximity analysis to resolve self-contradictions.

How to centralize brand rule exception management in an SSOT. Defines allow-lists and reference patterns in brand.ts, with gate scripts referencing them.

Learn how to design quality gates that balance strictness and flexibility using 3-level analysis and a two-stage fail/warn system.

Full disclosure of mcluhan's design: a 4-state machine, 10-item pre-publish Gate, and SSOT-backed rules that mechanically stop AI content quality failures before human review. Built from the wreckage of 11 failures in Phase 0.

We published 48 articles in 5 days, then pulled them all. 11 failures, HUMAN_INPUT markers exposed, 76 internal links dead. Here is the complete record—from detecting the problem before Google indexed us, to rebuilding the pipeline.

In the AI-scale era, owned media differentiation comes from engine investment decisions. Using mcluhan as a case, this article organizes mechanical detection investment allocation, E-E-A-T strategy through transparency disclosure, and retro-accumulated organizational learning for marketing executives and business owners.