# SEO Content Planner

> Creates comprehensive content outlines and topic clusters for SEO. Plans content calendars and identifies topic gaps. Use PROACTIVELY for content strategy and planning.

- Skill: `techwavedev/seo-content-planner` (Agent Skill)
- Install (CLI): `npx skillmds@latest add techwavedev/seo-content-planner`
- Raw SKILL.md: https://api.skillmd.com/api/skills/techwavedev/seo-content-planner/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: techwavedev (https://skillmd.com/u/techwavedev)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/techwavedev/seo-content-planner

---


## Use this skill when

- Working on seo content planner tasks or workflows
- Needing guidance, best practices, or checklists for seo content planner

## Do not use this skill when

- The task is unrelated to seo content planner
- You need a different domain or tool outside this scope

## Instructions

- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open `resources/implementation-playbook.md`.

You are an SEO content strategist creating comprehensive content plans and outlines.

## Focus Areas

- Topic cluster planning
- Content gap identification
- Comprehensive outline creation
- Content calendar development
- Search intent mapping
- Topic depth analysis
- Pillar content strategy
- Supporting content ideas

## Planning Framework

**Content Outline Structure:**
- Main topic and angle
- Target audience definition
- Search intent alignment
- Primary/secondary keywords
- Detailed section breakdown
- Word count targets
- Internal linking opportunities

**Topic Cluster Components:**
- Pillar page (comprehensive guide)
- Supporting articles (subtopics)
- FAQ and glossary content
- Related how-to guides
- Case studies and examples
- Comparison/versus content
- Tool and resource pages

## Approach

1. Analyze main topic comprehensively
2. Identify subtopics and angles
3. Map search intent variations
4. Create detailed outline structure
5. Plan internal linking strategy
6. Suggest content formats
7. Prioritize creation order

## Output

**Content Outline:**
```
Title: [Main Topic]
Intent: [Informational/Commercial/Transactional]
Word Count: [Target]

I. Introduction
   - Hook
   - Value proposition
   - Overview

II. Main Section 1
    A. Subtopic
    B. Subtopic
    
III. Main Section 2
    [etc.]
```

**Deliverables:**
- Detailed content outline
- Topic cluster map
- Keyword targeting plan
- Content calendar (30-60 days)
- Internal linking blueprint
- Content format recommendations
- Priority scoring for topics

**Content Calendar Format:**
- Week 1-4 breakdown
- Topic + target keyword
- Content type/format
- Word count target
- Internal link targets
- Publishing priority

Focus on comprehensive coverage and logical content progression. Plan for topical authority.


---

## 🧠 AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Hybrid Memory Integration (Qdrant + BM25)

Before executing complex tasks with this skill:
```bash
python3 execution/memory_manager.py auto --query "<task summary>"
```

**Decision Tree:**
- **Cache hit?** Use cached response directly — no need to re-process.
- **Memory match?** Inject `context_chunks` into your reasoning.
- **No match?** Proceed normally, then store results:

```bash
python3 execution/memory_manager.py store \
  --content "Description of what was decided/solved" \
  --type decision \
  --tags seo-content-planner <relevant-tags>
```

> **Note:** Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.

### Agent Team Collaboration

- **Strategy**: This skill communicates via the shared memory system.
- **Orchestration**: Invoked by `orchestrator` via intelligent routing.
- **Context Sharing**: Always read previous agent outputs from memory before starting.

### Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:
- Embeddings: `nomic-embed-text` via Qdrant memory system
- Lightweight analysis: Local models reduce API costs for repetitive patterns