Blog Strategy: Positioning & Content Architecture
Develops comprehensive blog strategies that build topical authority for Google rankings while establishing brand presence for AI citation platforms. Includes topic cluster architecture, AI citation surface strategy, content scoring targets, and GEO-specific optimization plans.
Research discipline references (v1.8.0):
skills/blog/references/research-quality.md- 5-dim rubric, pre-flight trap classes, cross-source clustering, freshness floorsskills/blog/references/synthesis-contract.md- 6 LAWs for synthesis output
Auto-loaded inputs (v1.8.0): when DISCOURSE.md exists at the project root (from /blog discourse), load it for cross-platform discourse signal alongside this skill's authority-source planning.
Cross-reference
Strategy planning should consider the FLOW 5-surface model (owned site, SERP plus AI Overviews, AI assistant citations, local pack, communities and video). Local-pack work is delegated to claude-seo; everything else lives inside claude-blog. Full mapping in skills/blog/references/flow-alignment.md.
For evidence-led audience-avatar, keyword-research, and content-prioritization prompts that feed strategic planning, see /blog flow find.
Workflow
Step 1: Discovery
Gather context through questions or project analysis:
- Business: What do you sell/do? Who are your customers?
- Blog goals: Traffic? Leads? Authority? AI citations?
- Current state: Existing blog content? (scan if project available)
- Competitors: Who are your 3-5 main competitors?
- Differentiator: What unique expertise or data do you have?
- Resources: Writing capacity (posts/week), budget for visuals?
Step 2: Competitive Landscape
Research competitors' blogs:
- WebSearch for competitor blog URLs
- For each competitor, assess:
- Publishing frequency
- Content types (guides, case studies, comparisons, news)
- Visual quality (images, charts, videos)
- Schema usage
- Social distribution (YouTube, Reddit, LinkedIn)
- AI citation presence (search ChatGPT/Perplexity for industry terms)
- Identify gaps no competitor covers well
Competitive AI Citation Analysis
Map competitor visibility across AI platforms. Use WebSearch to find how competitors appear in AI-generated responses for target keywords.
## Competitive AI Citation Map
| Query | ChatGPT Cites | Perplexity Cites | AI Overview Cites | Gap? |
|-------|--------------|-----------------|-------------------|------|
| [keyword] | [competitor/none] | [competitor/none] | [competitor/none] | [Yes/No] |
| [keyword] | [competitor/none] | [competitor/none] | [competitor/none] | [Yes/No] |
| [keyword] | [competitor/none] | [competitor/none] | [competitor/none] | [Yes/No] |Score each competitor's AI visibility:
- High: Cited in 3/3 platforms for multiple queries
- Medium: Cited in 1-2 platforms or for limited queries
- Low: Rarely cited, only in niche queries
- None: No AI citation presence detected
Identify AI citation gaps: queries where no competitor is cited. These represent the highest-opportunity targets for new content.
Note: only 12% overlap between platforms. A competitor strong on ChatGPT may be absent from Perplexity. Analyze each platform independently.
Step 3: Audience Mapping
Define 2-3 audience segments:
### Audience Segment: [Name]
- **Role**: [Job title / description]
- **Pain points**: [What problems do they have?]
- **Search behavior**: [What do they Google?]
- **AI behavior**: [What do they ask ChatGPT/Perplexity?]
- **Content preferences**: [Long guides? Quick answers? Video?]
- **Buying stage**: [Awareness / Consideration / Decision]Step 4: Content Pillar Design with Topic Cluster Architecture
Design 3-5 content pillars based on audience needs and competitive gaps. For each pillar, build the full hub-and-spoke cluster model.
### Pillar: [Topic Area]
- **Purpose**: Build authority in [topic]
- **Primary keywords**: [3-5 keywords]
- **Content types**: Pillar guide, supporting posts, comparisons, FAQ
- **Unique angle**: [What first-hand experience/data can you provide?]
- **Estimated posts**: [N] to achieve topic coverage
- **AI citation potential**: [High/Medium/Low] - [why]Cluster Architecture Design
For each pillar, design the complete hub-and-spoke structure:
### Cluster Architecture: [Pillar Topic]
┌──────────────────┐
│ Pillar Page │
│ 3,000-4,000w │
└────────┬─────────┘
│
┌─────────────────┼──────────────────┐
│ │ │
┌──────▼──────┐ ┌─────▼──────┐ ┌───────▼──────┐
│ Spoke #1 │ │ Spoke #2 │ │ Spoke #3 │
│ 1,500-2,500│ │ 1,500-2,500│ │ 1,500-2,500 │
└──────┬──────┘ └─────┬──────┘ └───────┬──────┘
│ │ │
└────────────────┼───────────────────┘
(cross-links between spokes)For each cluster, specify:
- 8-12 spoke topics per pillar, each targeting a specific long-tail keyword
- Internal linking plan between all cluster pages (every spoke links to pillar, pillar links to all spokes, spokes cross-link to related spokes)
- Content template assignment for each piece from the 12 available templates:
how-to-guide, listicle, case-study, comparison, pillar-page, product-review, thought-leadership, roundup, tutorial, news-analysis, data-research, faq-knowledge
### Cluster Build Plan: [Pillar Topic]
| # | Spoke Topic | Template | Target Keyword | Word Count | Internal Links |
|---|------------|----------|---------------|-----------|----------------|
| P | [Pillar title] | pillar-page | [keyword] | 3,000-4,000 | Links to all spokes |
| 1 | [Spoke title] | how-to-guide | [keyword] | 1,500-2,500 | Pillar + Spokes 2,3 |
| 2 | [Spoke title] | comparison | [keyword] | 1,500-2,500 | Pillar + Spokes 1,3 |
| 3 | [Spoke title] | listicle | [keyword] | 1,500-2,500 | Pillar + Spokes 1,2 |
| ... | ... | ... | ... | ... | ... |Reference: references/internal-linking.md for hub-and-spoke model and anchor text rules.
Step 5: Differentiation Strategy
The December 2025 Core Update rewards first-hand experience. Plan how to demonstrate genuine expertise:
| Signal Type | Implementation | |-------------|---------------| | Original data | Conduct surveys, analyze proprietary data, run experiments | | Case studies | Document real client/project results with metrics | | Build in public | Share process, learnings, and failures transparently | | Expert interviews | Feature practitioners with first-hand knowledge | | Tool reviews | Test products personally, share screenshots and results | | Industry analysis | Provide unique perspective on public data |
Step 5.5: AI Citation Surface Strategy
Plan how to maximize AI citation across platforms. 80% of LLM citations come from outside the top 100 organic results. Traditional SEO alone is insufficient.
On-Site Optimization
Structure every piece of content for AI citability:
- Every H2 opens with an answer-first paragraph (40-60 words with stat + source)
- Citation capsules: 40-60 word self-contained passages per H2 section
- Q&A format: 60-70% of H2 headings phrased as questions
- FAQ sections with schema markup on every post
- Entity clarity: consistent terminology throughout (no synonym variation for key concepts)
- Structured data: JSON-LD for Article, FAQ, HowTo, and Review schemas
Off-Site Presence (Critical: 88-92% of AI citations from off-site)
| Channel | AI Citation Impact | Priority Action | |---------|-------------------|-----------------| | YouTube | 0.737 correlation (strongest) | Companion videos for pillar posts | | Reddit | 450% citation surge | Authentic participation in 3-5 su