blog-write

# Blog Writer: New Article Generation

Writes complete blog articles from a topic, brief, or outline. Every article
follows the 6 pillars of dual optimization (Google rankings + AI citations).

**Key references** (paths relative to repo root; references live in the
main `blog` skill's references directory, not in `blog-write/`):

- `skills/blog/references/synthesis-contract.md`: 6 LAWs for synthesis output (v1.8.0; applies whenever the article embeds research-synthesis prose)
- `skills/blog/references/content-templates.md`: Template selection guide and usage
- `skills/blog/references/quality-scoring.md`: 5-category scoring (Content 30, SEO 25, E-E-A-T 15, Technical 15, AI Citation 15)
- `skills/blog/references/eeat-signals.md`: Experience, expertise, authority, trust markers
- `skills/blog/references/internal-linking.md`: Linking strategy and anchor text rules
- `skills/blog/references/visual-media.md`: Image sourcing and chart styling

## Workflow

### Phase 0: Surface Targeting (do this BEFORE research)

Decide which of the FLOW 5 surfaces this post is meant to win. The choice
shapes structure, length, citation density, and call-to-action. The 5 surfaces
in 2026:

1. Owned site (organic Google ranking)
2. SERP including AI Overviews
3. AI assistant citations (ChatGPT, Perplexity, Claude, Gemini, Copilot, You.com)
4. Local pack (out of scope for blog content; use claude-seo for local)
5. Communities and video (Reddit, YouTube, LinkedIn, Quora, niche forums)

Most posts target surfaces 1, 2, and 3 by default. If the same query also
surfaces in a community (Reddit thread, YouTube comment), apply dual-surface
thinking: optimize the post for extraction AND plan a community echo (covered
in `/blog repurpose`).

For a deeper surface-by-surface workflow, see
`skills/blog/references/flow-alignment.md` and `/blog flow find`.

### Phase 1: Topic Understanding

1. **Clarify the topic** - If the user provides just a topic, ask:
   - Target audience (who is this for?)
   - Primary keyword / search intent
   - Desired word count (default: 2,000-2,500 words)
   - Platform/format (MDX, markdown, HTML - auto-detect if in a project)
2. **If a brief exists** - Load it and skip to Phase 1.5

### Phase 1.5: Template Selection

Select the appropriate content template from the 12 templates in
`skills/blog/templates/` (the main `blog` skill owns the templates directory).

1. **Auto-detect content type** from the topic and search intent:
   | Signal | Template |
   |--------|----------|
   | "How to...", process, steps | `how-to-guide` |
   | "Best X", "Top N", list format | `listicle` |
   | Client result, before/after, metrics | `case-study` |
   | "X vs Y", comparison, alternatives | `comparison` |
   | Broad topic, comprehensive guide | `pillar-page` |
   | "Is X worth it", product evaluation | `product-review` |
   | Opinion, prediction, industry take | `thought-leadership` |
   | Expert quotes, multi-source collection | `roundup` |
   | Code walkthrough, tool demo, technical | `tutorial` |
   | Breaking news, algorithm update, event | `news-analysis` |
   | Survey results, experiment, original data | `data-research` |
   | Q&A, knowledge base, "What is X" | `faq-knowledge` |

2. **Load the matching template**: Read from `skills/blog/templates/<type>.md`
3. **Adapt the outline** - Use the template's section structure, heading patterns,
   and word count guidance to shape Phase 3's outline
4. **Fallback** - If no template clearly fits, use the generic outline structure
   in Phase 3 below. Inform the user which template was selected (or that none matched).

See `skills/blog/references/content-templates.md` for detailed selection criteria and intent mapping.

### Phase 2: Research

Spawn a `blog-researcher` agent (or do inline research with WebSearch):

1. **Find 8-12 current statistics** (2025-2026 data preferred)
   - Search: `[topic] study 2025 2026 data statistics`
   - Prioritize tier 1-3 sources (see `skills/blog/references/quality-scoring.md`)
   - Record: statistic, source name, URL, date, methodology
2. **Find a cover image** (wide, high-quality, topic-relevant):
   - Search: `site:pixabay.com [topic] wide banner` (preferred)
   - Alternative: `site:unsplash.com [topic] wide`
   - Fallback: `site:pexels.com [topic] wide banner`
   - Target dimensions: 1200x630 (OG-compatible) or 1920x1080
   - Or generate a custom SVG cover via `blog-chart` (text-on-gradient with key stat)
   - Or generate a custom AI image via `blog-image` sub-skill (if nanobanana-mcp configured)
   - See `skills/blog/references/visual-media.md` for cover image sizing details
3. **Find 3-5 inline images** from open-source platforms:
   - **Pixabay** (preferred): Search `site:pixabay.com [topic keywords]`
     - Extract image URL from page
     - Direct URLs: `https://cdn.pixabay.com/photo/YYYY/MM/DD/HH/MM/filename.jpg`
     - Verify with `curl -sI "<url>" | head -1` returns HTTP 200
   - **Unsplash** (alternative): Search `site:unsplash.com [topic keywords]`
     - Build URL: `https://images.unsplash.com/photo-<id>?w=1200&h=630&fit=crop&q=80`
   - **Pexels** (fallback): Search `site:pexels.com [topic keywords]`
4. **Plan 2-4 data visualizations** from researched statistics
   - Select diverse chart types (see `skills/blog/references/visual-media.md`)
   - Map data points to chart formats
5. **AI image generation** (optional, if nanobanana-mcp configured):
   - If stock photo results are insufficient (< 3 good matches) or topic is too niche
   - Generate custom hero image and/or inline illustrations via `blog-image` sub-skill
   - Stock photos remain default - AI generation supplements, never replaces
6. **NotebookLM research** (optional, if user has relevant notebooks):
   - If the user mentions a NotebookLM notebook or the topic aligns with a configured notebook
   - Query via `blog-notebooklm` for source-grounded data from user-uploaded documents
   - Treat NotebookLM responses as Tier 1 sources (user's own primary documents)