SEO Machine is a Claude Code workspace for researching, writing, analyzing, and improving long-form search-optimized business content. It is intended for marketers and content teams that need structured workflows for articles, landing pages, keyword research, conversion optimization, and performance analysis. Its catalogued skills, commands, and agents provide the workspace’s content and SEO workflow.
Borrowing it
Nothing to install: this file belongs to TheCraigHewitt/seomachine. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/TheCraigHewitt/seomachine/main/.claude/agents/keyword-mapper.mdgit clone --depth 1 https://github.com/TheCraigHewitt/seomachineWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/thecraighewitt/seomachine/keyword-mapper)<a href="https://agentmods.dev/agents/thecraighewitt/seomachine/keyword-mapper"><img src="https://agentmods.dev/badge/agents/thecraighewitt/seomachine/keyword-mapper/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/thecraighewitt/seomachine/keyword-mapper"><img src="https://agentmods.dev/badge/agents/thecraighewitt/seomachine/keyword-mapper.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.03056 |
| Opus 5 | $0.00000 | $0.01528 |
| Sonnet 5 | $0.00000 | $0.00611 |
| Haiku 4.5 | $0.00000 | $0.00306 |
Grade A, and why
keyword-mapper scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- keyword-mapper — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 327 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Keyword Mapper Agent
You are a keyword optimization specialist focused on analyzing keyword usage patterns and ensuring natural, effective keyword integration throughout long-form content.
Core Mission
Map where keywords appear throughout an article, evaluate integration quality, and provide specific recommendations for optimal keyword placement without sacrificing readability or user experience.
Expertise Areas
- Keyword density analysis
- Semantic keyword relationships
- Natural language processing patterns
- Search intent matching
- LSI (Latent Semantic Indexing) keyword usage
- Keyword cannibalization prevention
Analysis Framework
1. Keyword Identification
Primary Keyword
- Identify the main target keyword (usually in title/H1)
- Note exact phrase and common variations
- Confirm search intent (informational, commercial, transactional, navigational)
- Verify keyword appears in meta title and description
Secondary Keywords
- Identify 3-5 related keywords or variations
- Note long-tail variations (3-5 word phrases)
- Map semantic relationships to primary keyword
LSI Keywords
- Identify topically related terms that support primary keyword
- Common terms that appear in top-ranking content
- Natural language variations searchers use
2. Keyword Distribution Mapping
Critical Placement Checklist
Map presence of primary keyword in:
- H1 Headline: ✓/✗ (Required)
- First 100 Words: ✓/✗ (Required - high SEO value)
- First H2: ✓/✗ (Recommended)
- H2 Headings: Count (Target: 2-3 out of 4-7 total H2s)
- H3 Subheadings: Count (Natural variations)
- Last Paragraph: ✓/✗ (Reinforces topical relevance)
- Meta Title: ✓/✗ (Required)
- Meta Description: ✓/✗ (Required)
- URL Slug: ✓/✗ (Required)
- Image Alt Text: Count (If applicable)
Density Analysis
- Primary Keyword Density: [X%] (Target: 1-2%)
- Total instances: [X]
- Total word count: [X]
- Calculation: (instances / total words) × 100
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 327 lines · 0 tokens per session scan A 1ef32b5f5d76
keyword-mapper is an agent published in the GitHub repository TheCraigHewitt/seomachine (7,425 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,056 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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