Commit f61fc7b
feat(marketing): add weighted scoring systems to seo-audit + paid-ads
Phase 1 of the marketing skills improvement plan. Inspired by patterns
from claude-seo (4.7k stars) and claude-ads (2.4k stars) — the highest-
traction Claude Code plugins in the SEO/ads space. Adopts their best
patterns without replacing our existing skills.
seo-audit additions:
- scripts/seo_health_scorer.py — weighted 0-100 health score across 7
categories (Technical 22%, Content 23%, On-Page 20%, Schema 10%,
Performance 10%, AI Readiness 10%, Images 5%). Industry profiles
(SaaS/ecommerce/local/publisher) adjust weights. Severity-weighted
scoring with Critical/High/Medium/Low priority levels and Quick Wins
extraction. Demo mode included.
- references/cwv-thresholds.md — Core Web Vitals 2026 thresholds (LCP,
CLS, INP) with good/needs-improvement/poor ranges and common fixes
- references/eeat-framework.md — E-E-A-T audit checklist per Google's
Sept 2025 Quality Rater Guidelines, YMYL topic handling
- references/schema-types.md — active/deprecated JSON-LD types with
validation checklist and common mistakes
paid-ads additions:
- scripts/ad_health_scorer.py — multi-platform ad account scoring with
severity multipliers (Critical=5x, High=3x, Medium=1.5x, Low=0.5x).
Platform-specific category weights for Google (6 categories, 74 checks),
Meta (4 categories), LinkedIn (4), TikTok (4). Cross-platform aggregation
weighted by budget share. Quick Wins prioritization. Demo mode with
Google + Meta sample data.
- references/scoring-system.md — full scoring algorithm, severity
multipliers, platform weights, grade bands, quality gates (hard rules
like "never Broad Match + Manual CPC")
- references/copy-frameworks.md — 6 ad copy frameworks (PAS, BAB, AIDA,
FAB, 4P, Star-Story-Solution) with selection matrix by product type,
platform-specific character limits, and brand DNA extraction (7 voice
axes as JSON schema)
Key patterns adopted from claude-seo/ads:
- Weighted numeric scores replace binary pass/fail
- Severity multipliers make critical issues dominate the score
- Industry/platform auto-detection adjusts weights
- Quick Wins = high severity + partially working (warn not fail)
- Reference files are lazy-loaded, not inline
- Demo mode with realistic sample data
All scripts stdlib-only, --json + --help verified.
Also saved: documentation/implementation/marketing-skills-improvement-plan.md
covering all 3 phases (Phase 2: content scoring, Phase 3: AI detection).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>1 parent 47d458a commit f61fc7b
8 files changed
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File tree
- documentation/implementation
- marketing-skill
- paid-ads
- references
- scripts
- seo-audit
- references
- scripts
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