ai-content-audit

AI Content Audit Skill

Teams that scaled content with AI are discovering the bill: libraries full of fluent, structurally identical, information-free pieces that readers bounce off, search engines quietly demote, and — worst — that erode the trust the good content earned. This skill audits the library for slop with named signals, triages it, and installs the gate that stops the refill.

What This Skill Produces

• An audited inventory with per-piece verdicts: keep / enrich / rewrite / delete-and-redirect
• The detection signals found, quoted — so verdicts are checkable, not vibes
• A triage plan sequenced by traffic and trust impact
• A publishing quality gate for AI-assisted content going forward

Required Inputs

Ask for (if not already provided):

• The corpus — pieces or URLs to audit (or a sample; state the sampling), with publish dates
• Performance data if available — traffic, engagement, rankings over time (the audit works without it, but verdicts get sharper)
• What the content is for — SEO, docs, thought leadership, support deflection (the quality bar differs)
• Production context — when AI-assisted publishing started, at what volume (the before/after seam is diagnostic gold)

Detection Method

Slop isn't "AI wrote it" — it's content with nothing inside. Audit each piece for the signals, quoting instances:

1. Information density — the core test: delete every sentence that any competitor could have written, and measure what's left. Slop survives at <20%. Look for: zero proprietary data, zero named examples, zero opinions with an owner, zero specifics a reader could act on.
2. Structural monoculture — the same skeleton repeating across pieces (intro-restating-the-title → 5 H2s → "in conclusion"); listicles whose items are definitions, not judgments; FAQ sections answering questions nobody asked.
3. Hedged voicelessness — "it's important to note", "in today's fast-paced world", both-sides-ism on questions the brand should have a stance on; the absence of anything a lawyer would ever have flagged.
4. Fluency without grounding — claims with no source, stats with no year, "studies show" with no study; internally contradictory sections (the tell of stitched generations).
5. Reader evidence, where data exists — engagement collapse relative to the library's pre-AI baseline, rising pogo-sticking, ranking decay cohort-matched to the AI-volume era. Correlate verdicts with the seam from the production context.