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/commands/research.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/commands/thecraighewitt/seomachine/research)<a href="https://agentmods.dev/commands/thecraighewitt/seomachine/research"><img src="https://agentmods.dev/badge/commands/thecraighewitt/seomachine/research/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/commands/thecraighewitt/seomachine/research"><img src="https://agentmods.dev/badge/commands/thecraighewitt/seomachine/research.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 | $0.01247 |
| Opus 5 | $0.00000 | $0.00624 |
| Sonnet 5 | $0.00000 | $0.00249 |
| Haiku 4.5 | $0.00000 | $0.00125 |
Grade A, and why
research 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.
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Command
Use this command to conduct comprehensive SEO keyword research and competitive analysis before writing new content.
Usage
/research [topic]
What This Command Does
- Performs keyword research for your industry-related topics
- Analyzes top-ranking competitor content
- Identifies content gaps and opportunities
- Develops unique angle for your company perspective
- Creates detailed research brief for writing
Process
Keyword Research
- Primary Keyword: Identify main target keyword for the topic
- Search Volume & Difficulty: Research estimated monthly searches and competition level
- Keyword Variations: Find semantic variations and long-tail opportunities
- Related Questions: Discover what people are actually asking (People Also Ask, forums, Reddit)
- Search Intent: Determine if intent is informational, navigational, commercial, or transactional
- Topic Cluster: Identify how this topic fits into your company content clusters
Competitive Analysis
- Top 10 SERP Review: Analyze the top 10 ranking articles for target keyword
- Content Length: Note word count of top-performing articles (benchmark target)
- Common Themes: What topics/sections do all top articles cover?
- Content Gaps: What's missing from competitor coverage?
- Unique Angles: What perspectives or insights are underexplored?
- Featured Snippets: Identify if there's a featured snippet opportunity
- Domain Authority: Note which competitors rank (indie blogs vs. major publications)
Context Integration
- your company Advantage: How can your company product features naturally enhance this content?
- Brand Alignment: Check @context/brand-voice.md for messaging fit
- Existing Content: Review @context/internal-links-map.md for related your company articles
- Target Keywords: Cross-reference with @context/target-keywords.md priority list
- SEO Guidelines: Ensure research aligns with @context/seo-guidelines.md requirements
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 · 131 lines · 0 tokens per session scan A 206386dbb37a
research is a command 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 1,247 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.