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

Script location. If your host does not set ${CLAUDE_PLUGIN_ROOT}, the scripts are in this plugin's scripts/ folder, next to skills/. Run them with Python 3.10 or newer: python, or python3 where python is not found (macOS, many Linux systems).

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:

Dimension	Script	What it checks
Hallucination	hallucination-detector.py	Unattributed statistics, placeholder URLs (example.com / your-site.com), unsupported superlatives ("best", "#1", "leading"), fabricated citations
Claims	claim-verifier.py (when --evidence provided)	Cross-checks specific claims against a user-provided evidence file
Brand voice	brand-voice-scorer.py (when --brand provided)	Scores content against the active brand's voice profile (formality, energy, humor, authority, prefer/avoid words)
Structure	output-validator.py (when --schema provided)	Validates content matches expected schema (blog_post, email, ad_copy, social_post, landing_page, press_release, content_brief, campaign_plan)
C2PA provenance (compliance)	embed-c2pa.py (presence check)	When the brand's target_markets include an EU/EEA jurisdiction AND an accompanying asset is AI-generated: verifies a C2PA provenance manifest is present and valid. Missing or invalid manifest → CRITICAL / BLOCKED (EU AI Act Article 50, applies from 2 Aug 2026)

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.

EU AI Act Article 50 — C2PA provenance gate

The check gains a compliance dimension for AI-generated assets in EU-targeted campaigns. It fires when both conditions hold:

1. The active (or --brand) profile's target_markets include any EU/EEA jurisdiction, and
2. An accompanying asset is declared AI-generated — either the file metadata says so, or the --evidence JSON declares ai_generated: true for it.

When both hold, the gate checks the asset read-only with python "${CLAUDE_PLUGIN_ROOT}/scripts/embed-c2pa.py" --verify <asset>: exit 0 = manifest present and valid (a self-signed dev signer is reported as untrusted_signer, not failed), 6 = no manifest, 7 = manifest fails validation, 2 = c2pa-python not installed (report the dimension as SKIPPED with the printed install command; never PASS it). A missing or invalid C2PA provenance manifest (exit 6 or 7) is a CRITICAL issue → decision = BLOCKED. Article 50 applies from 2 Aug 2026 (penalty up to EUR 15M or 3% of global turnover). To embed a compliant manifest, run /digital-marketing-pro:c2pa-metadata.

If embed-c2pa.py is not present in the script inventory or the asset cannot be resolved, surface the dimension as SKIPPED with a warning (never silently PASS an EU AI-asset check).

How the skill operates

The skill follows this flow:

1. Resolve the input. If the user passed a file path, read it. If they passed inline content, use it.
2. Resolve options. If --brand not specified, attempt to load from active brand at ~/.claude-marketing/brands/_active-brand.json. If --schema not specified, infer from content type if obvious (blog markdown → blog_post, etc.) or skip structure check.
3. Build the eval-runner command. Choose action: run-quick (default), run-full (with --full), run-compliance (with --compliance).
4. Execute via Bash.

   python "${CLAUDE_PLUGIN_ROOT}/scripts/eval-runner.py" --action run-quick --file <input> [--brand <slug>] [--evidence <path>] [--schema <name>]
   

5. Parse the JSON output. Extract composite score, grade, dimension scores, alerts, auto-reject decision.
6. Format for the user. Present the human-readable report shown above. Lead with the decision (PASS / WARN / BLOCKED).
7. If BLOCKED, refuse to recommend publishing. Always require the user to address CRITICAL issues before they proceed.

Scripts called

• scripts/eval-runner.py — master orchestrator
• scripts/hallucination-detector.py — invoked by eval-runner
• scripts/claim-verifier.py — invoked by eval-runner if --evidence provided
• scripts/brand-voice-scorer.py — invoked by eval-runner if --brand provided
• scripts/output-validator.py — invoked by eval-runner if --schema provided
• scripts/content-scorer.py — invoked by eval-runner
• scripts/readability-analyzer.py — invoked by eval-runner
• scripts/embed-c2pa.py — presence/verify check for the EU AI Act Article 50 C2PA gate (only when an EU-targeted brand has an AI-generated asset)

All scripts use stdlib only (except brand-voice-scorer which optionally uses nltk). No external API calls, no internet required.

Examples

Example 1: Quick check on a draft

User: /digital-marketing-pro:check drafts/q2-launch-blog.md

Skill:
1. Read drafts/q2-launch-blog.md
2. Run python "${CLAUDE_PLUGIN_ROOT}/scripts/eval-runner.py" --action run-quick --file drafts/q2-launch-blog.md
3. Parse JSON output:
   composite_score: 81.2, grade: B+, auto_rejected: false
   hallucination: 92/100 pass, content_quality: 76/100 pass, readability: 84/100 pass
   alerts: 1 warning ("unattributed stat in line 14")
4. Format report:

DM CHECK REPORT — drafts/q2-launch-blog.md
============================================
Composite Score: 81.2 / 100  (Grade: B+)
Decision: PASS

Dimensions:
  Hallucination ......... 92/100  pass
  Content Quality ....... 76/100  pass
  Readability ........... 84/100  pass

Issues Found:
  WARNING (1):
    - Line 14: Unattributed statistic "76% of marketers say..."
      Suggestion: cite source or rephrase as observation

Decision: PASS — safe to publish; recommend addressing the WARNING first.

Example 2: Full eval with brand + evidence + schema

User: /digital-marketing-pro:check drafts/healthcare-ad.md --full --brand healthfirst --evidence facts/q2-claims.json --schema ad_copy

Skill:
1. Read drafts/healthcare-ad.md
2. Run python "${CLAUDE_PLUGIN_ROOT}/scripts/eval-runner.py" --action run-full --file drafts/healthcare-ad.md --brand healthfirst --evidence facts/q2-claims.json --schema ad_copy
3. Parse JSON output. Composite: 58.4, grade: D+, auto_rejected: true
4. Format report with CRITICAL issues highlighted
5. Decision: BLOCKED. Two unattributed health claims need substantiation before this can publish.

Example 3: Compliance check on regulated content

User: /digital-marketing-pro:check drafts/financial-services-landing.md --compliance --brand finadvisor --evidence facts/finra-disclosures.json

Skill:
1. Read content
2. Run python "${CLAUDE_PLUGIN_ROOT}/scripts/eval-runner.py" --action run-compliance --file drafts/financial-services-landing.md --brand finadvisor --evidence facts/finra-disclosures.json
3. Output prioritises hallucination + claim verification + brand voice + structure
4. Returns decision with FINRA-relevant issues highlighted

Example 4: Quick check on inline content

User: /digital-marketing-pro:check "Our amazing product boosts conversion by 347% — visit example.com today!"

Skill:
1. Detect inline content (not a file path)
2. Write content to a temp file
3. Run quick eval
4. Report:
   CRITICAL: 2
     - Placeholder URL "example.com" — replace with real URL before publishing
     - Unattributed statistic "347%" — fabricated stat or missing citation
   Decision: BLOCKED

When to use which mode

Scenario	Recommended mode
Routine content check during drafting	/digital-marketing-pro:check <file> (quick)
Before publishing any external content	/digital-marketing-pro:check <file> --full --brand <slug>
Regulated industry content (healthcare / financial / alcohol / cannabis / gambling)	/digital-marketing-pro:check <file> --compliance --brand <slug> --evidence <facts>
Client-facing deliverable (Growth Plan, Yearly Planner, monthly report)	/digital-marketing-pro:check <file> --full --brand <slug>
Ad copy specifically	/digital-marketing-pro:check <file> --schema ad_copy --brand <slug>
Email specifically	/digital-marketing-pro:check <file> --schema email --brand <slug>
Blog post specifically	/digital-marketing-pro:check <file> --schema blog_post --brand <slug>

Behaviour rules

1. Never report PASS if there are CRITICAL issues. Always BLOCKED.
2. Always report the composite score and grade. Even if PASS, surface room for improvement.
3. Always include actionable suggestions. Each issue must be paired with a fix recommendation.
4. Resolve the active brand if not specified. Check ~/.claude-marketing/brands/_active-brand.json. If no active brand, run without --brand (skip brand voice dimension).
5. Never modify the content. This skill only reports — the user (or the agent that produced the content) makes the fix.
6. Surface skipped dimensions explicitly. If the user did not provide --evidence or --schema, note that the corresponding dimensions were skipped.

Related skills + commands

• /digital-marketing-pro:engagement growth-plan — produces Part 8 deliverable; should be checked with /digital-marketing-pro:check --full --schema content_brief before client delivery
• /digital-marketing-pro:content-engine — produces marketing content; recommended workflow is /digital-marketing-pro:content-engine → review → /digital-marketing-pro:check → publish
• /digital-marketing-pro:eval-content — legacy alias that routes to this skill

Related references

• scripts/eval-runner.py — the master orchestrator this skill wraps
• skills/context-engine/eval-framework-guide.md — full eval framework documentation
• skills/context-engine/eval-rubrics.md — per-dimension scoring rubrics
• docs/architecture.md Section 16 (Evaluation Layer) — eval framework architecture