surfer-seo-optimizer

📁 anorbert-cmyk/agentic-kit 📅 8 days ago
1
总安装量
1
周安装量
#49904
全站排名
安装命令
npx skills add https://github.com/anorbert-cmyk/agentic-kit --skill surfer-seo-optimizer

Agent 安装分布

amp 1
opencode 1
cursor 1
kimi-cli 1
codex 1
github-copilot 1

Skill 文档

SurferSEO Optimizer Skill

🧠 Core Philosophy

This skill treats content as data. It does not guess. It uses the “Reverse Engineering” method to determine what Google rewards for a specific keyword. Instead of generic advice (“write good content”), it provides specific, quantifiable targets (e.g., “Hit 2,100 words”, “Use ‘startup costs’ 3 times”).

⚡ Capabilities

1. SERP Analysis (analyze)

Goal: Determine the “Perfect Content Structure” for a target keyword. Protocol:

  1. Search: Use search_web to find the top 5 ranking articles for the keyword.
  2. Extract: For each article, determine:
    • Word Count (approximate)
    • Heading Structure (H2/H3 usage)
    • Key Topics/Entities covered
  3. Synthesize: Create a “Content Brief” defining:
    • Target Word Count (Average of Top 5 + 10%)
    • Required Sections (H2s that appear in most competitors)
    • “NLP Keywords” (Terms that appear frequently across top pages)

2. Content Audit (audit)

Goal: Score a local MDX/MD file against the “Perfect Structure”. Protocol:

  1. Read: Read the local file content.
  2. Measure: Run python .agent/skills/surfer-seo-optimizer/scripts/score_content.py (or internal logic) to calculate:
    • Current Word Count
    • Keyword Density
    • Heading Count
  3. Grade: Assign a score (0-100) based on closeness to targets.
    • Green: Within 10% of target.
    • Yellow: Within 25% of target.
    • Red: Missed target significantly.

3. NLP Optimization (optimize)

Goal: Rewrite content to improve the score without sacrificing readability. Protocol:

  1. Identify a paragraph or section that is “thin” or missing keywords.
  2. Rewrite it to naturally include the missing terms.
  3. Constraint: Do not “keyword stuff”. Readability > SEO.

📝 Usage Example (Workflow)

User: /surfer analyze "startup valuation methods"
Agent: [Searches Web] -> [Generates Brief: 2500 words, Keywords: 'DCF', 'Berkus Method'...]

User: /surfer audit content/startup-valuation.mdx "startup valuation methods"
Agent: [Reads File] -> [Runs Scoring] -> "Your Score: 62/100. You are missing 'Berkus Method'."

User: /surfer optimize "Add the section about Berkus Method"
Agent: [Writes Content] -> "Added section..."

📂 Resources

  • scripts/score_content.py: CLI tool for text statistics.