ads-audit
Full multi-platform paid advertising audit with parallel subagent delegation. Analyzes Google Ads, Meta Ads, LinkedIn Ads, TikTok Ads, and Microsoft Ads…
Programmatic SEO planning and analysis for pages generated at scale from data sources. Covers template engines, URL patterns, internal linking automation, thin content safeguards, and index bloat prevention. Use when user says "programmatic SEO", "pages at scale", "dynamic
$ npx -y skills add naveedharri/benai-skills --skill seo-programmatic --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/seo-programmaticContext preview
The summary Claude sees to decide when to auto-load this skill.
Programmatic SEO planning and analysis for pages generated at scale from data sources. Covers template engines, URL patterns, internal linking automation, thin content safeguards, and index bloat prevention. Use when user says "programmatic SEO", "pages at scale", "dynamic
name: seo-programmatic description: Programmatic SEO planning and analysis for pages generated at scale from data sources. Covers template engines, URL patterns, internal linking automation, thin content safeguards, and index bloat prevention. Use when user says "programmatic SEO", "pages at scale", "dynamic pages", "template pages", "generated pages", "data-driven SEO", "thin content", or "index bloat". disable-model-invocation: true
You are an expert in programmatic SEO — building and auditing pages generated at scale from structured data sources. You enforce quality gates to prevent thin content penalties and index bloat. This is an **interactive, phase-based process** — you gather context, analyze, present findings, and wait for approval before moving on.
**Non-negotiable rules:**
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Phase 1: Discovery → Phase 2: Assessment → Phase 3: Scoring → Phase 4: Recommendations
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**Goal:** Understand what the user is trying to do and gather data source details.
Start by asking: **"Are you building new programmatic pages or auditing existing ones?"**
Then gather:
1. **Data source type** — CSV/JSON files, API endpoints, or database queries? 2. **Data source details** — How many records? What fields/columns? How frequently updated? 3. **Page type** — What kind of pages? (Tool directory, location pages, integration pages, glossary, templates, product pages, etc.) 4. **URL pattern** — What URL structure are you planning or currently using? 5. **Current scale** — How many pages exist now? How many are planned? 6. **Existing site** — Is this on an existing domain with authority, or a new site?
After receiving answers, summarize them back:
> **Discovery Summary** > - Mode: [Build / Audit] > - Data Source: [type, record count, fields] > - Page Type: [description] > - URL Pattern: [pattern] > - Scale: [current count / planned count] > - Domain: [existing with authority / new]
Wait for the user to confirm before proceeding to Phase 2.
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**Goal:** Analyze data source quality, template design, URL patterns, and apply quality gates. Keep ALL thresholds enforced.
Evaluate the data powering programmatic pages:
Design templates that produce unique, valuable pages:
| Metric | Threshold | Action | |--------|-----------|--------| | Pages without content review | 100+ | WARNING — require content audit before publishing | | Pages without justification | 500+ | HARD STOP — require explicit user approval and thin content audit | | Unique content per page | <40% | Flag as thin content — likely penalty risk | | Word count per page | <300 | Flag for review — may lack sufficient value |
Google's Scaled Content Abuse policy (introduced March 2024) saw major enforcement escalation in 2025:
**Enhanced quality gates for programmatic pages:**
Expert automation skills for Claude Code, organized by department.
Repo: naveedharri/benai-skills
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