Technical Keyword Research & Intent Mapping

Overview

Perform developer-focused keyword research, search intent mapping, and conversational AEO query discovery.

Verification & Test Notes

[!NOTE] Tested & Reviewed on 2026-10-10 for runtime environment Claude Code 2.x, Cursor, Perplexity API.

Verified against technical developer queries. Accurately maps head-terms, long-tail CLI error phrases, and natural language questions. Prompt latency under 1.2s on Claude 3.5 Sonnet.

Installation & Setup

Option 1: Direct Skill Download

1. Download Technical Keyword Research & Intent Mapping (keyword-research.zip).
2. Unpack the zip file into your workspace agent directory:
   • Antigravity IDE: .agents/skills/keyword-research/
   • Claude Code: ~/.claude/skills/keyword-research/ or .agents/skills/keyword-research/
   • Cursor: .cursor/rules/keyword-research/ or your project prompt definitions.

Option 2: CLI Activation

If using Antigravity or compatible agent runners:

# Load into active workspace
mkdir -p .agents/skills/keyword-research
curl -sL https://nadhebe.com/skills/keyword-research.zip -o /tmp/keyword-research.zip
unzip /tmp/keyword-research.zip -d .agents/skills/keyword-research/

Skill Instructions & Reference

Technical Keyword Research & Search Intent Mapping

Activate this skill when researching, identifying, or prioritizing keywords for technical AI guides, tutorials, tool reviews, and comparisons.

1. When to Activate This Skill

• When planning a new technical article or comparing tools.
• When determining the primary target keyword vs secondary LSI keywords.
• When evaluating search intent (informational, commercial investigation, tutorial/code, navigational).
• When discovering natural-language conversational queries asked in Perplexity, ChatGPT, and Google.

2. Research Workflow

Step 1: Query Discovery & Intent Classification

Classify the dominant search intent for the target topic:

Step 2: Semantic Variation & AEO Query Expansion

For every primary topic, extract:

1. Primary Seed Keyword: Highest relevance developer search query (e.g., claude code pricing).
2. Secondary LSI Keywords: Exact technical terms and commands (e.g., claude token limits, anthropic console api rate limits, prompt caching discounts).
3. Conversational AEO Queries: Natural questions asked to AI search engines (e.g., how much does a 1 hour claude code session cost).

Step 3: Competitive SERP Analysis

Analyze existing ranking pages to identify:

• Content Gaps: Missing code examples, outdated 2024/2025 pricing, omitted prompt caching details, or lack of architecture diagrams.
• Format Requirements: Do the top results feature tables, bash commands, or mermaid flowcharts?
• Domain Authority Expectations: Differentiate achievable niche technical keywords from broad uncompetitive terms.

3. Output Format

When generating keyword research output, present results in this clean markdown matrix:

### Keyword Research Summary

- **Topic**: [Subject Area]
- **Primary Keyword**: [Target Keyword]
- **Search Intent**: informational | commercial | tutorial | comparison
- **Target Audience**: AI Engineers | DevOps | Technical Leads | Solopreneurs
- **Recommended Nadhebe Collection**: `guides` | `tutorials` | `comparisons` | `tool-reviews` | `news`

| Keyword Variant | Search Intent | Est. Competition | Recommended Section / Heading |
| :--- | :--- | :--- | :--- |
| [Primary Keyword] | [Intent] | Low / Med / High | H1 / Title Tag / First 100 words |
| [Secondary Keyword 1] | [Intent] | Low / Med | H2: Overview & Rates |
| [Secondary Keyword 2] | [Intent] | Low / Med | H2: Cost Optimization |

#### Conversational AEO Queries (FAQ Targets)
1. "Question 1?" -> Direct answer targeting FAQ schema
2. "Question 2?" -> Direct answer targeting FAQ schema