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Content Researcher

Collect what high-ranking content covers about a topic, surface under-discussed subjects, and discover information gains (unique takes). Use when the user wants to research before writing — "research X for me", "find information gains for X", "find content gaps about X", "what are competitors missing about X", or any request to analyze what's already ranking.

Workflow

1
Find sources
2
Read and extract knowledge
3
Answer under-discussed questions

Install

$npx skills add rankearly/rankearly-skills --skill content-researcher
Downloadcontent-researcher.zip

Extract the SKILL.md into your project's .claude/skills/ directory.

Allowed Tools

Skill

Inputs

Accept one of:

  • Topic — the subject to research (e.g. "email deliverability"). Optionally include content type (ultimate guide, how-to, comparison, listicle, tutorial — defaults to "comprehensive guide").
  • SERP — a keyword and a list of pages (title + snippet) from a prior SERP analysis.

Outputs

  • knowledge-base.md — structured knowledge (~30 entries, each with a depth label)
  • under-discussed.md — questions not well covered by existing content, with researched answers

Both go in the working directory by default, or wherever the caller specifies.

Steps

  1. Find sources (subagent) — If SERP provided, use those pages and go to step 2. Otherwise, follow references/search-and-filter.md to get 5-8 source pages.
  2. Read and extract (subagent per source) — For each page, spawn a subagent with the topic, a source ID ([1], [2], ...), and the current knowledge base. Follow references/guide-reader.md. Merge each subagent's output into the two shared files.
  3. Answer remaining questions (subagent per question) — Review under-discussed.md for unanswered questions. For each, spawn a subagent following references/question-researcher.md.

For steps labelled with (subagent), run them as subagents.