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: ```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. ## 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