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powerczukpowerczuk/seo-machine/skills/research/SKILL.md

research

Skill0 starsChanged 2 months agoMITView on GitHub

Keyword research, SERP analysis, competitive gaps, recommended content architecture, and draft meta elements for a topic.

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  • LambenthanSkill

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  • LambenthanSkill

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  • truongtv22Skill

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  • liufeng1976Skill

    setup-ts-deep-modules

    Wire dependency-cruiser into a TypeScript repo so each package is a deep module — implementation hidden in subfolders, reachable only through its entry-point files. User-invoked.

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  • skyllwtSkill

    exp-pilot-eval

    Pilot result evaluation — read pilot results, apply success criteria, update idea page (pilot_result, failure_reason if failed), generate PILOT_VERDICT_REPORT. Called by /ideate Phase 5 after /exp-pilot-run.

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  • skyllwtSkill

    exp-pilot-run

    Pilot experiment execution — read Pilot Spec YAML, write pilot code, run experiment(Confirm with the user before operation and require the applicant to conduct manual inspection), return results. Called by /ideate Phase 5. Does NOT modify wiki pages or judge pass/fail.

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  • +5 more from Lambenthan
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The file

Files
SKILL.md

/research — Keyword research & SERP analysis

Target: $ARGUMENTS

Method: ${CLAUDE_PLUGIN_ROOT}/skills/seo-fundamentals/references/keyword-research-framework.md. Numbers and targets: ${CLAUDE_PLUGIN_ROOT}/skills/seo-fundamentals/references/scoring-spec.md. If SEO tools are connected (Ahrefs, Semrush, Similarweb, DataForSEO, GSC via MCP), pull volume, difficulty, and positions from them and say which tool the numbers came from; otherwise label every volume/difficulty as an estimate.

Process

  1. Keyword discovery — variations, long-tail (3–5 words), questions (how/what/why), commercial modifiers (best/vs/review), semantically related terms. Identify the primary entity behind the keyword (the Knowledge Graph entity the page will be about).
  2. SERP analysis (top 10 for the primary keyword) — content types, length range and median, topics every result covers (must-haves), topics some cover (differentiators), topics none cover (information-gain candidates), SERP features present (snippet format, PAA, images, video, AI Overview) and who owns them.
  3. Competitive gaps — underexplored angles, outdated data in competitors, missing formats (tables, templates, tools), weak sections, missing schema. For the rigorous entity-level version run /entity-coverage.
  4. Intent classification — informational → guide/tutorial; commercial investigation → comparison/review; transactional → landing page; navigational → brand page.

Output

  1. Keyword opportunity table (15–25 rows): keyword · volume (source or "est.") · difficulty · intent · opportunity (high/med/low) · recommended content type.
  2. SERP landscape — feature ownership, length median, structural patterns, differentiation opportunities, whether an AI Overview appears and what it cites.
  3. Competitive gaps — what is missing, why it matters, approach, effort/impact.
  4. Recommended architecture — primary entity, 3 H1 options, H2 outline tagged MUST-COVER / DIFFERENTIATOR / SNIPPET TARGET, target length (SERP median +20–30%), snippet strategy, schema types.
  5. Meta preview — 3 title options (50–60 chars, entity front-loaded, click copy) and 3 descriptions (150–160 chars, value + CTA); final polish belongs to /meta-tags.

Offer next: /entity-coverage (entity matrix + information gain), /content-brief, /write-seo, /topic-clusters.

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