check

name	check
description	Run the unified pre-publish quality gate on marketing content — hallucination detection, claim verification, brand voice scoring, structure validation. Use before publishing any marketing copy.
user-invocable	true
triggers	["check this content before publishing","run the eval suite on this draft","validate this marketing copy","pre-publish quality gate","hallucination check","dm check","eval my content","is this safe to publish"]
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 ${CLAUDE_PLUGIN_DATA}/<brand>/ 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

In v3.0 and earlier, a global PreToolUse hook auto-ran a hallucination + brand-compliance check on every Write/Edit operation in every project. v3.1 removed that hook 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)

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.

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

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 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 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 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 — older legacy alias that will route to this skill in v3.2+

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 11 — eval framework architecture