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Content quality and E-E-A-T analysis with AI citation readiness assessment. Use when user says "content quality", "E-E-A-T", "content analysis", "readability check", "thin content", or "content audit".
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Maintain a CHANGELOG.md following the Keep a Changelog standard. Use after commits, on /commit, when the user asks to update the changelog, or when releasing a version — maps conventional-commit types to Added/Changed/Fixed/Security categories. Archives old releases into per-major files (changelog/CHANGELOG-1.x.md) so the main file stays small no matter how many versions accumulate.
Multi-language code quality standards and review for TypeScript, Python, Go, and Rust. Enforces type safety, security, performance, and maintainability. Use when writing, reviewing, or refactoring code. Includes review process, checklist, and Python PEP 8 deep-dive.
Run OpenAI Codex CLI for coding tasks and second-opinion audits. Use when a user asks to run/ask/use Codex, says "codex prompt", or wants Claude to delegate a logic/code review to OpenAI models. Covers direct `codex` CLI invocation (exec, review, resume, apply, doctor, mcp), the six reasoning-effort levels (none/minimal/low/medium/high/xhigh), sandbox + dangerous flags, background execution, rate-limit safety, and when to defer to the official OpenAI Codex Claude Code plugin (`codex:rescue`) instead. Preflights with `codex doctor` to read the current default model + surface available updates; never hardcodes model/effort, letting Codex pick its own current best default unless the user explicitly names one.
Production-grade tabular data manipulation using pandas & openpyxl. This skill should be used when editing, creating, filtering, sorting, merging, pivoting, deduplicating, validating, or transforming CSV, Excel (xlsx/xls), JSON, Parquet, or TSV files. Supports 18 operations via CLI scripts, advanced Excel formatting (multi-sheet, freeze, auto-filter, validation, styling), and file-converter integration for format pipelines.
Efficiently navigate codebase documentation during Research phase. Use instead of Grep/Glob for finding architectural decisions, feature specs, and technical docs. Maps topics to doc locations for fast context retrieval. If codebase lacks documentation structure, provides patterns to establish one.
Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks
| name | seo-content |
| description | Content quality and E-E-A-T analysis with AI citation readiness assessment. Use when user says "content quality", "E-E-A-T", "content analysis", "readability check", "thin content", or "content audit". |
| user-invokable | true |
| argument-hint | [url] |
| license | MIT |
| metadata | {"author":"AgriciDaniel","version":"2.0.0","category":"seo"} |
Before scoring E-E-A-T sub-factors, every page audit should pass Google's own three-question heuristic from the helpful-content guide:
| Question | What to look for |
|---|---|
| Who created it? | Visible byline, author bio page, professional credentials. Required where readers expect it; non-negotiable for YMYL. |
| How was it created? | Process disclosure where readers would reasonably ask — especially for AI-assisted content. Original research / first-hand evidence / lived experience. |
| Why does it exist? | "To help people" rather than "to attract search clicks." Watch for niche entry without expertise, content churn for freshness signals, content written to a word-count target. |
Primary source: https://developers.google.com/search/docs/fundamentals/creating-helpful-content
When all three answers are weak, the page is at risk under the core ranking system's helpfulness signals (formerly the standalone Helpful Content System, merged into core during the March 2024 update).
Read skills/seo/references/eeat-framework.md for full criteria.
Compare against page type minimums:
| Page Type | Minimum |
|---|---|
| Homepage | 500 |
| Service page | 800 |
| Blog post | 1,500 |
| Product page | 300+ (400+ for complex products) |
| Location page | 500-600 |
Important: These are topical coverage floors, not targets. Google has confirmed word count is NOT a direct ranking factor. The goal is comprehensive topical coverage; a 500-word page that thoroughly answers the query will outrank a 2,000-word page that doesn't. Use these as guidelines for adequate coverage depth, not rigid requirements.
Note: Flesch Reading Ease is a useful proxy for content accessibility but is NOT a direct Google ranking factor. John Mueller has confirmed Google does not use basic readability scores for ranking. Yoast deprioritized Flesch scores in v19.3. Use readability analysis as a content quality indicator, not as an SEO metric to optimize directly.
Google's raters now formally assess whether content appears AI-generated.
Helpful Content System (March 2024): The Helpful Content System was merged into Google's core ranking algorithm during the March 2024 core update. It no longer operates as a standalone classifier. Helpfulness signals are now weighted within every core update. The same principles apply (people-first content, demonstrating E-E-A-T, satisfying user intent), but enforcement is continuous rather than through separate HCU updates.
Optimize for AI search engines (ChatGPT, Perplexity, Google AI Overviews):
Google AI Mode launched publicly in May 2025 as a separate tab in Google Search, available in 180+ countries. Unlike AI Overviews (which appear above organic results), AI Mode provides a fully conversational search experience with zero organic blue links, making AI citation the only visibility mechanism.
Key optimization strategies for AI citation:
Generative Engine Optimization (GEO):
Per Google's AI optimization guide, "AEO" and "GEO" are rebranded labels for SEO — AI Overviews and AI Mode are grounded in the same ranking and quality systems as classic Search. The optimization signals that matter (quotability, attribution, heading hierarchy, freshness) are SEO fundamentals applied to AI-search surfaces, not a separate discipline. Cross-reference the seo-geo skill for detailed workflows; both surfaces share the primary-source synthesis in skills/seo-geo/references/google-ai-optimization-guide.md.
| Factor | Score | Key Signals |
|---|---|---|
| Experience | XX/25 | ... |
| Expertise | XX/25 | ... |
| Authoritativeness | XX/25 | ... |
| Trustworthiness | XX/25 | ... |
If DataForSEO MCP tools are available, use kw_data_google_ads_search_volume for real keyword volume data, dataforseo_labs_bulk_keyword_difficulty for difficulty scores, dataforseo_labs_search_intent for intent classification, and content_analysis_summary for content quality analysis.
| Scenario | Action |
|---|---|
| URL unreachable (DNS failure, connection refused) | Report the error clearly. Do not guess page content. Suggest the user verify the URL and try again. |
| Content behind paywall (402/403, login wall) | Report that the content is not publicly accessible. Analyze only the visible portion (meta tags, headers) and note the limitation. |
| Thin content (fewer than 100 words retrievable) | Report the findings as-is rather than guessing. Flag the page as potentially JavaScript-rendered or gated, and suggest the user provide the full text directly. |
For prompt-guided content optimization, use /seo flow optimize <url> and /seo flow win <url> — FLOW's optimize and win prompts provide structured E-E-A-T improvement and BOFU conversion workflows.
