Check Skill for Claude

/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.

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:

• The active (or --brand) profile's target_markets include any EU/EEA jurisdiction, and
• 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:

• Resolve the input. If the user passed a file path, read it. If they passed inline content, use it.
• 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.
• Build the eval-runner command. Choose action: run-quick (default), run-full (with --full), run-compliance (with --compliance).
• Execute via Bash.

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

• Parse the JSON output. Extract composite score, grade, dimension scores, alerts, auto-reject decision.
• Format for the user. Present the human-readable report shown above. Lead with the decision (PASS / WARN / BLOCKED).
• 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

• Never report PASS if there are CRITICAL issues. Always BLOCKED.
• Always report the composite score and grade. Even if PASS, surface room for improvement.
• Always include actionable suggestions. Each issue must be paired with a fix recommendation.
• 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).
• Never modify the content. This skill only reports — the user (or the agent that produced the content) makes the fix.
• 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