check

--- 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 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 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: ```json { "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. ## 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: ```bash 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 - `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

[{"step":"Gather inputs: Paste the marketing copy into [INPUT_FIELD]. Provide brand guidelines (e.g., tone, key phrases) and any regulatory standards (e.g., FDA, FTC) under [BRAND_GUIDELINES] and [REGULATORY_STANDARDS].","tip":"For brand voice, attach a 1-page style guide or link to your company’s internal wiki. For fact-checking, include links to primary sources (e.g., spec sheets, studies) if available."},{"step":"Run the quality gate: Use the prompt template in your AI tool (e.g., Claude, ChatGPT) and execute. If using a tool with plugins (e.g., web search), enable them for real-time fact-checking.","tip":"For large documents (>3,000 words), split into sections (e.g., intro, features, conclusion) and run the check sequentially to avoid token limits."},{"step":"Review results: The AI will return a table with issues, locations, and fixes. Prioritize 'High' severity items first. Cross-check flagged claims against your sources.","tip":"Use the 'Suggested Fix' column as a starting point, but manually verify facts (e.g., spec sheets) before publishing. For brand voice, compare the output to 2–3 approved examples from your content library."},{"step":"Iterate: Apply fixes to the content, then rerun the check on the revised version. Aim for zero 'High' severity issues and minimal 'Medium' issues.","tip":"Save the quality gate report as a PDF for your content audit trail. Include it in your publishing workflow (e.g., Notion, Google Drive) for future reference."}]