本文へ移動
cccskills
無料GitHub で公開

seo-content-brief

Generate a 2026-grade content brief for a target query — combines classic SERP analysis with AI Overview / Perplexity / ChatGPT citation analysis, entity recommendations (target ≥15 recognized entities), topical authority gap map, intent-mapped heading structure, internal linking targets.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md12.2 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

SEO Content Brief Generator (2026)

You generate a content brief that ranks AND gets cited by AI. Modern briefs are no longer "keyword + word count + headings." They are entity-aware, intent-mapped, E-E-A-T-aware, and AI-citation-targeted.

Why this differs from a 2023-era brief:

  • AI engines synthesize from MANY sources, weighting entity relationships and citation depth → a brief must list entities to mention, not just keywords.
  • AI Overviews pull from the first 80 words of pages with strong structure → the brief must specify the answer-first opener.
  • Entity density > keyword density. 15+ recognized entities ≈ 4.8× AI citation probability.
  • Topical authority compounds — a brief is also a vote on which existing pages to internal-link to/from.

============================================================ === PRE-FLIGHT ===

Verify:

  • Target query explicit ("best CRM for small business", "how to file 1099-NEC", etc.).
  • Intent classification: informational / transactional / navigational / commercial-investigation. Different briefs per intent.
  • Site context: domain URL + existing top 10 pages (so internal linking suggestions are real, not generic).
  • Author available: at least one person with credible Author Entity setup (or note that author setup is a prerequisite — without it, the content won't get cited regardless of quality).
  • Output format: markdown brief (default), Notion-flavored markdown, or Google Doc upload (export as .docx via pandoc).

Recovery:

  • If query intent is ambiguous, default to informational (most common) and surface the disambiguation in the brief header.
  • If existing site pages aren't accessible, generate a brief without internal linking suggestions and flag this gap.

============================================================ === PHASE 1: SERP + AI CITATION ANALYSIS ===

For the target query, gather signals from THREE surfaces:

Classic SERP (Google top 10)

  • Title, URL, content type (article/listicle/tool/video/forum), word count, last-updated date.
  • People Also Ask (PAA) questions.
  • Featured snippet structure (paragraph / list / table) — note: AI Overviews are eating featured snippet territory.

AI Overview / Generative results

  • Capture the AI Overview / Gemini answer for the query if available.
  • List the 3–10 URLs cited by the AI Overview.
  • Note which citations overlap with the organic top 10 (typically 60–80% overlap).

Conversational AI engines

  • Run the query through ChatGPT (with browsing), Perplexity, Claude (with search).
  • Capture: which sources each engine cited, recurring entities mentioned, any consensus claims.

Output brief/01_competitive_landscape.md with side-by-side citation tables.

VALIDATION: Per-engine citation list has ≥ 3 URLs. Overlap with organic top-10 noted.

FALLBACK: Without Perplexity/ChatGPT API access, capture manually via the user's browser (provide them a copy-paste prompt). Less precise but unblocks the brief.

============================================================ === PHASE 2: ENTITY EXTRACTION ===

From the top 10 organic + AI-cited sources, extract every recognized entity (proper noun mapped to a Knowledge Graph node).

Bucket entities by type:

  • People: founders, authors, public figures
  • Organizations: companies, agencies, certifying bodies
  • Products / Tools: named software, hardware, services
  • Places: countries, cities, regions, neighborhoods
  • Concepts / Standards: ISO numbers, frameworks, methodologies, laws
  • Events: conferences, releases, historical milestones
  • Times / Dates: years, eras, quarters

Output brief/02_entities.md ranked by co-occurrence frequency across sources.

Identify:

  • Must-mention entities (in ≥ 50% of top 10 sources) — the brief MUST include these.
  • Differentiator entities (in top 3 only, or unique to AI Overview citations) — the brief SHOULD include if relevant.
  • Stale entities (in top 10 from 2022 sources but not in fresh AI citations) — likely safe to omit; flag for review.

VALIDATION: Entity list has ≥ 25 candidates. Must-mention list has ≥ 10 entities.

============================================================ === PHASE 3: INTENT-MAPPED HEADING STRUCTURE ===

Generate the H2/H3 outline. Each heading is a question the reader (or AI engine) is trying to answer.

Default outline patterns by intent:

Informational ("what is X", "how does X work"):

  1. H1: {Definitive query phrasing}
  2. Lead paragraph (answer in first 60 words — AI Overview target)
  3. H2: What is {X}? (definition with key entity)
  4. H2: How {X} works (mechanism, 3-5 steps)
  5. H2: Types of {X} (taxonomy)
  6. H2: When to use {X} (decision criteria)
  7. H2: Common mistakes / misconceptions
  8. H2: Tools / examples (entity-rich)
  9. H2: FAQ (from PAA + Reddit + AI engines)

Commercial-investigation ("best X", "X vs Y", "X review"):

  1. H1: {Best X for Y} or {X vs Y}
  2. Lead paragraph (top recommendation in first 60 words)
  3. H2: Quick comparison table (entity-dense)
  4. H2: {Tool 1} — pros/cons/best-for
  5. H2: {Tool 2} — pros/cons/best-for
  6. ...
  7. H2: How we evaluated (E-E-A-T signal)
  8. H2: Decision framework (which to pick when)
  9. H2: FAQ

Transactional ("buy X", "X pricing", "X near me"):

  1. H1: Direct match
  2. Lead with offer / pricing / availability
  3. H2: Features
  4. H2: Pricing tiers
  5. H2: How to get started
  6. H2: Customer outcomes / case studies (E-E-A-T)
  7. H2: FAQ

Output brief/03_outline.md.

VALIDATION: Outline is intent-matched. Each H2 maps to a specific user question or buyer-journey stage.

============================================================ === PHASE 4: FAQ MINING ===

Compile the FAQ section from multiple sources:

  1. Google People Also Ask — top 5 from SERP scrape.
  2. Reddit / forums — search for the target query + "reddit" / "stackoverflow" / "quora"; extract recurring questions.
  3. AI engine outputs — note any follow-up suggestions ChatGPT/Perplexity/Claude offer after the main answer.
  4. Comments / reviews — if the user has G2 / Trustpilot / Amazon reviews on the product/topic, mine for unanswered questions.

Dedupe, rank by frequency, output as a 5–10 question FAQ section with answer drafts (1–3 sentences each).

VALIDATION: FAQ has ≥ 5 questions, each with a draft answer ≤ 60 words.

============================================================ === PHASE 5: INTERNAL LINKING TARGETS ===

From the user's existing top pages, identify:

  • 3–5 pages to link TO from this new content (topically relevant, higher in funnel).
  • 3–5 pages to link FROM (back to this content) (existing high-traffic pages where this new content would be a natural reference).

Format each as:

- [{Anchor text}](https://example.com/path) — Why: {one-sentence rationale}

VALIDATION: Each suggested link has a specific anchor text and rationale, not just a URL.

FALLBACK: Without site access, suggest internal-link templates ("link to your pillar page on {X} topic") and let the writer fill specifics.

============================================================ === PHASE 6: E-E-A-T REQUIREMENTS ===

Specify what makes this content citable, not just rankable:

  • Author: Named, byline links to /author/{slug}, Person schema, ≥ 3 sameAs.
  • First-party data: original screenshot / chart / quote / experiment / dated metric.
  • Primary source citations: ≥ 2 outbound links to authoritative sources (gov, academic, established trade publication).
  • Last-updated discipline: page must include dateModified schema and visible "Updated YYYY-MM-DD" in DOM.
  • Original quote / interview: if topic warrants, include a quote from a recognized practitioner or vendor.

Mark each as REQUIRED or RECOMMENDED based on competitiveness of the query (high-competition → all REQUIRED).

VALIDATION: List is specific to this brief's topic, not generic boilerplate.

============================================================ === PHASE 7: THE FINAL BRIEF ===

Combine all phases into brief/{slug}.md:

# Content Brief — {Target Query}

**Generated:** {date}  
**Intent:** {informational / commercial-investigation / transactional}  
**Target length:** {word count based on competitor median ±10%}  
**Author requirement:** {Person entity profile complete: yes/no — must be yes to publish}

## TL;DR for the writer

- Lead with the direct answer in the first 60 words.
- Cover these MUST-MENTION entities: {list of 10}
- Hit ≥ 15 total recognized entities (see entities.md for the full pool).
- Internal-link to: {3 pages}
- FAQ section is required (5+ questions from FAQ.md).
- Author must be {Name}; if not, set up the Person entity first.

## Outline

{from Phase 3}

## Entities to include

{from Phase 2, must-mention bucket}

## FAQ section

{from Phase 4}

## Internal Linking

{from Phase 5}

## E-E-A-T requirements

{from Phase 6}

## Competitor benchmarks

- Median word count: {N}
- Median entity count: {N}
- Median schema types: {Article, FAQPage, ...}

## Schema to ship with the page

- Article (required)
- FAQPage (if FAQ section is present)
- BreadcrumbList
- {anything else specific to this topic — HowTo, Product, etc.}

VALIDATION: Brief is ≤ 6 pages of markdown. A competent writer should be able to produce the article from the brief alone without further research.

FALLBACK: If brief is too thin (insufficient SERP signal — emerging topic), say so explicitly and recommend supplementing with subject-matter-expert interview.

============================================================ === SELF-REVIEW ===

Score 1–5:

  • Complete: All 7 phases? Entity list ≥ 25 candidates? Outline intent-matched? E-E-A-T section specific?
  • Robust: Handled missing API access? Internal linking targets defaulted gracefully when site context unavailable?
  • Clean: Brief is ≤ 6 pages, scannable, actionable?
  • AI-citation-ready: A writer following this brief produces content that hits ≥ 15 entities, lead-with-answer structure, schema-ready, and has named-author E-E-A-T?

Common gap: brief loses entity discipline at writing time. Cap the brief by reminding the writer in the TL;DR to count entities before publishing.

============================================================ === LEARNINGS CAPTURE ===

Append to ~/.claude/skills/seo-content-brief/LEARNINGS.md:

<YYYY-MM-DD> — <target query, intent, vertical>

  • What worked: <approach that produced a tight brief>
  • What was awkward: <retry/manual fix needed>
  • Suggested patch: <concrete improvement>
  • Verdict: [Smooth / Minor friction / Major friction]

============================================================ === STRICT RULES ===

  • Never produce a brief with only keywords and word count. That's a 2018 brief.
  • Never recommend exact-match keyword stuffing. Modern engines penalize and AI engines ignore.
  • Never omit the entity must-mention list. It's the highest-leverage section.
  • Never skip the E-E-A-T requirements. A brief that gets published without author entity setup will not get cited.
  • If the brief is for a YMYL topic (health, finance, legal), upgrade E-E-A-T REQUIRED items to include credentialed author + primary medical/legal/financial source citations.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

508-audit

無料

Run a full Section 508 + WCAG 2.2 Level AA accessibility audit on a federal / .gov / public-sector website or ICT product. Triggers: "Section 508", "508 audit", "WCAG", "VPAT", "ACR".

日本語の概要は準備中です。原文の説明を表示しています。

tinh2/skills-hub-registry192026年9月5日 更新

Analyzes actuarial modeling systems for loss reserving accuracy, premium pricing methodology, mortality/morbidity tables, stochastic modeling, and capital adequacy per SOA and Solvency II standards..

日本語の概要は準備中です。原文の説明を表示しています。

tinh2/skills-hub-registry192026年9月5日 更新

Analyzes advertising yield across programmatic and direct-sold inventory including CPM/CPC/CPA performance, header bidding efficiency, fill rate optimization, inventory monetization strategies, and compliance with IAB standards and OpenRTB protocols..

日本語の概要は準備中です。原文の説明を表示しています。

tinh2/skills-hub-registry192026年9月5日 更新

adr

無料

Create and manage Architecture Decision Records using the Michael Nygard format. Triggers: you need to document architecture decisions, record technical choices, explain why a technology was chosen.

日本語の概要は準備中です。原文の説明を表示しています。

tinh2/skills-hub-registry192026年9月5日 更新

Analyzes affordable housing management software for unit allocation algorithms, waitlist management, income verification workflows, Fair Housing compliance, LIHTC and Section 8 program tracking, inspection scheduling, and tenant reporting..

日本語の概要は準備中です。原文の説明を表示しています。

tinh2/skills-hub-registry192026年9月5日 更新

Rewrites existing landing pages, blog posts, and documentation so AI search engines (Google AI Overviews, ChatGPT, Perplexity, Claude, Gemini) cite them. Triggers: an existing page underperforms in AI answers despite ranking well in classic SERPs.

日本語の概要は準備中です。原文の説明を表示しています。

tinh2/skills-hub-registry192026年9月5日 更新

tinh2 のスキルをすべて見る

このスキルの問題を報告する