// 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.
| 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 |
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.
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.
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.
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).
/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.
/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.
/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.
/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.
/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.
/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.
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.
The skill follows this flow:
--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.run-quick (default), run-full (with --full), run-compliance (with --compliance).python ${CLAUDE_PLUGIN_ROOT}/scripts/eval-runner.py --action run-quick --file <input> [--brand <slug>] [--evidence <path>] [--schema <name>]
scripts/eval-runner.py — master orchestratorscripts/hallucination-detector.py — invoked by eval-runnerscripts/claim-verifier.py — invoked by eval-runner if --evidence providedscripts/brand-voice-scorer.py — invoked by eval-runner if --brand providedscripts/output-validator.py — invoked by eval-runner if --schema providedscripts/content-scorer.py — invoked by eval-runnerscripts/readability-analyzer.py — invoked by eval-runnerAll scripts use stdlib only (except brand-voice-scorer which optionally uses nltk). No external API calls, no internet required.
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.
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.
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
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
| 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> |
~/.claude-marketing/brands/_active-brand.json. If no active brand, run without --brand (skip brand voice dimension).--evidence or --schema, note that the corresponding dimensions were skipped./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+scripts/eval-runner.py — the master orchestrator this skill wrapsskills/context-engine/eval-framework-guide.md — full eval framework documentationskills/context-engine/eval-rubrics.md — per-dimension scoring rubricsdocs/architecture.md Section 11 — eval framework architectureAudit a brand's existing live campaigns across every active channel — paid, organic, email, social, content, SEO. Produce a current-state inventory, quick-wins backlog, and red-flags list. Use during agency onboarding or before any /campaign-plan refresh.
Produce the Part 5 Client Validation Document — the one true stop where unbiased v1 findings meet the client. Each finding gets ACCEPT/REJECT/EDIT/DEFER decision.
Run Part 12 — the continuous improvement loop. Aggregates market + operating signals into product/offering recommendations. Runs alongside live operations, not as a one-time activity.
Run a full marketing engagement using the 12-Part methodology. Use when starting a new engagement, advancing parts, applying the Decision Matrix, or showing engagement status.
Produce the Four Core Documents at strategic depth (61 total steps): Business & SBU Analysis, Segmentation Framework, Brand Positioning, DMFlow. Use when running Part 3 of the engagement methodology.
Produce the 11-section Growth Plan — the flagship Part 8 client-facing deliverable that synthesises the entire engagement into a single executable strategy.