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name: check description: "Run the unified pre-publish quality gate on marketing content — wraps scripts/eval-runner.py to score hallucination risk, claim substantiation (with --evidence), brand-voice fit (with --brand), structure (with --schema), content quality, and readability, plus a C2PA provenance check for AI assets in EU-targeted campaigns; returns a composite score with a PASS / WARN / BLOCKED decision and per-issue fix suggestions. Reports only — it never edits the content. Triggers on "/digital-marketing-pro:check", "is this safe to publish", "run a hallucination check on this draft", "validate this copy against the brand voice", "pre-publish quality gate". Resolves the active brand profile automatically; pairs with /digital-marketing-pro:c2pa-metadata to fix missing manifests." user-invocable: true argument-hint: "<file-or-content> [--full|--compliance] [--brand <slug>] [--evidence <path>] [--schema <name>]" allowed-tools: Read Bash Glob Grep

/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:

| 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 runs a C2PA manifest presence check on the asset via embed-c2pa.py (presence/verify mode — it does not modify the asset). A missing or invalid C2PA provenance manifest 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