Agent-Skills.md

Agent Skills: Search intent coverage (SERP → outline)

Determine what to cover to satisfy search intent by analyzing SERPs (preferred) or extracting competitor pages to Markdown.

UncategorizedID: younesbenallal/seo-skills/search-intent-coverage

Repository

younesbenallal/seo-skills
younesbenallal
1

Install this agent skill to your local

pnpm dlx add-skill https://github.com/younesbenallal/seo-skills/tree/HEAD/search-intent-coverage

Skill Files

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search-intent-coverage/SKILL.md

Skill Metadata

Name
search-intent-coverage
Description
Determine what to cover to satisfy search intent by analyzing SERPs (preferred) or extracting competitor pages to Markdown.

Search intent coverage (SERP → outline)

You help the user build an outline that matches what Google is ranking and adds differentiated value.

Shared context first

Before asking repeated discovery questions, check whether .agents/seo-context.md exists.

If it does:

  • read it first
  • reuse the saved market, audience, topic, and competitor context
  • ask only for the query or draft-specific details that are still missing

Inputs to collect

  • Primary query
  • Market (language + country)
  • Optional: the user’s current URL/draft

Tooling & credentials

  • Auth mode: mcp
  • Requires: SERP API MCP
  • Fallback: Browser MCP or agent-browser for reading competitor pages, plus user-provided competitor URLs or outlines if browsing is unavailable
  • If missing: stop, ask the user to install or configure a SERP API MCP, and continue only after they confirm it is ready

Follow the shared setup and missing-access rules in docs/credentials-and-tooling.md.

Tools (adaptive)

Required: SERP API MCP. If it’s missing, stop and ask the user to install one.

For reading competitor URLs:

  • Use a Browser MCP if available.
  • Else use agent-browser CLI (install if needed).

Agent-browser install (if missing):

npm i -g agent-browser

Exact agent-browser commands to open a competitor page and extract common sections/headings fast:

agent-browser open "<url>"
agent-browser wait --load networkidle
agent-browser get title
agent-browser snapshot -c --json
# In the JSON, find the node with role="main" to get its @ref, then:
agent-browser get text @eMAIN
agent-browser snapshot -c -d 3 --json
# Scan the compact tree for role="heading" nodes to capture the H2/H3 pattern.

Fallback: ask the user to provide 3–5 competitor URLs and/or paste outlines.

Workflow

  1. Fetch the SERP for the query (top 10).
  2. Identify dominant intent type:
    • informational / commercial / transactional / navigational
  3. Extract patterns:
    • repeated H2 topics
    • repeated definitions/FAQs
    • common “proof” elements (examples, templates, calculators)
  4. Build a MECE outline:
    • above-the-fold “answer fast”
    • core explanation
    • decision help (when commercial)
    • pitfalls, FAQs, examples
  5. Add 1–2 differentiated elements (proprietary angle):
    • checklist, interactive component idea, original data, teardown, template

Output

  • “What people want” (1 paragraph)
  • MECE outline (H1 → H2 → H3)
  • “Differentiators to win” (3 bullets)