# Blog Outline

SERP-informed outline generation with H2/H3 heading hierarchy, competitive content gap...

- Repository: https://github.com/AgriciDaniel/claude-blog (2608 stars)
- Category: Writing
- License: MIT
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add AgriciDaniel/claude-blog --skill blog-outline -g -a claude-code -y`
Codex: `npx skills add AgriciDaniel/claude-blog --skill blog-outline -g -a codex -y`
Cursor: `npx skills add AgriciDaniel/claude-blog --skill blog-outline -g -a cursor -y`
Gemini CLI: `npx skills add AgriciDaniel/claude-blog --skill blog-outline -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add AgriciDaniel/claude-blog`

## Description

SERP-informed outline generation with H2/H3 heading hierarchy, competitive content gap analysis, section-by-section word count targets, chart and image placement markers, optional FAQ question planning, and internal linking zones. Skeleton only: structure, H2/H3 hierarchy, word counts, optional FAQ slots. Use blog-brief instead if you need full competitive analysis, statistics research, and image suggestions. Lighter than a full content brief, generates article skeleton and structure only, ready for /blog write to consume. Use when user says "outline", "blog outline", "content outline", "structure blog", "plan sections", "article skeleton", "heading structure", "SERP analysis", "competitive outline", "plan article".

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Blog Outline Generator: SERP-Informed Structure Planning

Generates skeletal blog post outlines informed by SERP analysis. A lighter
alternative to a full content brief - produces heading hierarchy, section
targets, and content gap notes without deep statistics research or full
competitive analysis.

## Cross-reference

For evidence-led topical-relevance and content-planning prompts upstream of outlining, see `/blog flow find`. The blog-post-outline-prompt under `/blog flow optimize` is a complementary structural reference.

## Workflow

### Step 1: Topic & Intent

Gather from the user:
1. **Topic or target keyword** (required)
2. **Target keyword** - the exact phrase to rank for (if different from topic)
3. **Search intent** - Informational, commercial, or transactional

If only a topic is given, infer the keyword and intent from context.

### Step 2: SERP Analysis

Use WebSearch to analyze the full visible search surface for the target keyword, not just classic blue links:

1. Search for the target keyword
2. Scan classic top results plus AI Overviews, AI Mode where available, People Also Ask, featured snippets, and visible citation/source surfaces.
3. For each of the top 5 classic results, note:
   - **Heading structure** - H2/H3 topics covered
   - **Content length** - Approximate word count
   - **Visual elements** - Charts, images, videos, infographics
   - **Questions** - Any FAQ sections, People Also Ask coverage, or AI Overview/AI Mode prompt variants
   - **Unique angles** - What makes each result distinct
   - **Gaps** - What's missing or weak

4. For AI Overviews, AI Mode, and other citation surfaces, record cited publishers, repeated entities, answer formats, and sources that do not overlap with the classic top 5.

The rest: https://github.com/AgriciDaniel/claude-blog/blob/HEAD/brain/.raw/sources/claude-blog-skill/skills/blog-outline/SKILL.md