Check

/digital-marketing-pro:check — Unified Pre-Publish Quality Gate

This skill is the canonical pre-publish gate for marketing content. It wraps the evaluation suite (scripts/eval-runner.py) and produces a single pass/fail decision with actionable issues.

Context efficiency

Heavy skill. Grep before Read any referenced file, then Read only matched ranges with offset + limit. List the brand's workspace at ~/.claude-marketing/brands/{slug}/ (or $CLAUDE_PLUGIN_DATA/digital-marketing-pro/brands/{slug}/ when that env var is set) before opening files. On re-invocation mid-session, skip files already in context.

Use this skill before publishing any marketing content — blog posts, ad copy, emails, social posts, landing pages, press releases, or any branded copy.

Why this skill exists

An earlier version shipped a global PreToolUse hook that auto-ran a hallucination + brand-compliance check on every Write/Edit operation in every project. That hook was removed because it fired globally across all plugins and projects (Slack writes, GitHub PRs, code edits — all of it), causing friction in non-marketing work.

/digital-marketing-pro:check replaces that automatic gate with an explicit user-invoked gate. The work is the same; the trigger is intentional.

What the check evaluates

The check delegates to scripts/eval-runner.py (the master eval orchestrator) which calls four sibling scripts:

Plus content quality and readability scoring (always run).

Subcommands and modes

Default (run-quick)

/digital-marketing-pro:check <file-path-or-content>

Runs the quick eval: hallucination detection + content quality + readability. Fast (~2 seconds), zero external dependencies. Use this for routine checks.

Full eval (run-full)

/digital-marketing-pro:check <file-path-or-content> --full

Runs all 6 dimensions: hallucination + claims (if evidence provided) + brand voice (if brand provided) + structure (if schema provided) + content quality + readability. Use before publishing anything client-facing or external.

Compliance-focused (run-compliance)

/digital-marketing-pro:check <file-path-or-content> --compliance --brand <slug> [--evidence <path>] [--schema <name>]

Runs hallucination + claims + brand voice + structure. Best for regulated industries (healthcare, financial services, alcohol, cannabis, gambling) where claim substantiation and brand-voice fidelity matter most.

With evidence file

/digital-marketing-pro:check <file-path> --evidence <evidence-file.json>

When the content makes specific claims you want to substantiate, provide a JSON evidence file:

{
  "evidence": [
    {
      "claim": "50% increase in conversions",
      "source": "GA4 Q4 report",
      "date": "2025-12-31",
      "verified": true
    },
    {
      "claim": "Trusted by Fortune 500 companies",
      "source": "Customer roster (internal)",
      "date": "2026-04-01",
      "verified": true
    }
  ]
}

The check will extract every claim from the content and flag any that don't match an evidence entry.

With schema validation

/digital-marketing-pro:check <file-path> --schema blog_post

Validates the content matches the structural requirements of the named schema. Available schemas: blog_post, email, ad_copy, social_post, landing_page, press_release, content_brief, campaign_plan. Use --schema list to see all schemas with their requirements.

With brand voice check

/digital-marketing-pro:check <file-path> --brand acme

Scores the content against the brand voice profile at ~/.claude-marketing/brands/acme/profile.json. Reports per-dimension breakdown (formality, energy, humor, authority) plus deviation from prefer/avoid word lists.

Output format

The check returns a unified report:

DM CHECK REPORT — <file or content snippet>
=============================================

Composite Score: 73.4 / 100  (Grade: B-)
Auto-Reject: NO

Dimensions:
  Hallucination ............ 96/100  PASS  (weight 0.40)
  Content Quality .......... 78/100  PASS  (weight 0.35)
  Readability .............. 65/100  PASS  (weight 0.25)

Issues Found:
  CRITICAL: None
  WARNING (2):
    - Line 14: Unattributed statistic "76% of buyers prefer..."
      Suggestion: cite source or rephrase as observation
    - Line 22: Superlative "best in class" without substantiation
      Suggestion: replace with measurable claim or proof point

Decision: PASS — safe to publish but address WARNINGs first

If any CRITICAL issue is found, decision = BLOCKED and the user is asked to fix before publishing.

Decision rules

• PASS — no CRITICAL issues and the composite score is above the auto-reject threshold (auto_reject_threshold, default 40)
• WARN — no CRITICAL issues but at least one WARNING; the user should address it before publishing
• BLOCKED — at least one CRITICAL issue (e.g. placeholder URL, fabricated statistic in a headline, missing required disclaimer for a regulated industry, missing C2PA provenance manifest on an AI-generated asset in an EU-targeted campaign); the content cannot publish until fixed

AI-tell scans (advisory section, never scored)

Alongside the eval-runner scorers, run both tell scans and report them as a single ADVISORY section of the check output:

python "${CLAUDE_PLUGIN_ROOT}/scripts/ai-tell-scan.py"          --file <input>   # Tier 1: surface
python "${CLAUDE_PLUGIN_ROOT}/scripts/structural-tell-scan.py"  --file <input>   # Tier 2: structure

• Tier 1 (surface) — LLM-favored vocabulary, significance markers, soft-adverb clusters, connective and participial openers, em-dash density, ungrounded one-liners. Report the overall LOW/MODERATE/HIGH rating and the flagged sentences with their suggested fix. Significance markers are reported with "fix": "Delete this sentence; do not reword it." — pass that through verbatim, because rewording is the wrong remedy.
• Tier 2 (structure) — the overall OK/NOTE/ATTENTION band plus each NOTE/ATTENTION finding with its spans (moralizing, section symmetry, parallel headings, specificity, stance, paragraph evenness, entity development). For entity_development, always carry through that the fix is to develop an existing specific, never to delete specifics.

This whole section NEVER affects the PASS/WARN/BLOCKED decision. Both scripts keep their thresholds inside themselves, deliberately outside the eval config, because these are editorial judgment calls for a human editor, not publish gates — and because a detector proxy has a real false-positive rate on genuinely human writing. (The one place a tell scan does gate is the content-engine's humanize_passed, and only on the two tells precise enough to gate on: significance_marker and soft_adverb_cluster. llm_favored_word was dropped from that set on 2026-08-15 after it was measured firing only on prose published before ChatGPT existed and never on model prose. That gate is a density floor — measured, it fails no published human writing and catches no unedited model prose — so never report a pass as evidence that a piece reads human.) Both scans measure visible text only; neither can see, and neither has any relationship to, any statistical watermark.