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/repurpose.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/repurpose)<a href="https://agentmods.dev/commands/thecraighewitt/seomachine/repurpose"><img src="https://agentmods.dev/badge/commands/thecraighewitt/seomachine/repurpose.svg" alt="Measured on agentmods" 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 | $0.01532 |
| Opus 5 | $0.00000 | $0.00766 |
| Sonnet 5 | $0.00000 | $0.00306 |
| Haiku 4.5 | $0.00000 | $0.00153 |
Grade A, and why
repurpose 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 8d 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repurpose Command
Take a published or drafted article and generate platform-specific versions for distribution across multiple content surfaces, maximizing AI citation potential.
Usage
/repurpose [path-to-article]
Examples:
/repurpose drafts/project-management-guide-2026-04-10.md/repurpose published/start-a-blog.md
Why This Matters
AI search engines (ChatGPT, Perplexity, Gemini) pull recommendations from many surfaces beyond your website: Medium, LinkedIn, Reddit, Quora, YouTube transcripts. The more surfaces your content appears on with attribution back to your site, the higher the probability of being cited in AI-generated answers.
One article should become 4-5 pieces of distributed content. This command automates the adaptation, not just copy-pasting.
Process
1. Read and Analyze Source Article
- Read the full article
- Identify the primary keyword, core thesis, and key claims
- Note the article's URL slug (for linking back)
- Extract 3-5 key takeaways
- Identify the strongest data points, quotes, and insights
2. Generate Platform-Specific Versions
For each platform, generate an adapted version that fits the platform's norms, audience expectations, and format. Each version must link back to the original article.
LinkedIn Article (LinkedIn Pulse)
Format: Long-form article (800-1,200 words) Tone: Professional, insight-driven, slight thought-leadership angle Structure:
- Opening hook (2-3 sentences, conversational)
- 3-5 key insights from the article, reframed for a business/professional audience
- Personal perspective or key insight
- Clear takeaway or lesson
- Link to full article: "I wrote a deeper breakdown on this: [link]"
- No bullet-heavy formatting (LinkedIn penalizes listicle-style posts)
Adaptation notes:
- Reframe for business value (ROI, productivity, growth) when possible
- Remove niche-specific jargon that a general LinkedIn audience won't know
- Add a "why this matters for your business" angle if the original is practitioner-focused
- Do NOT just copy the intro + "read more at [link]"
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.
- 8d ago First seen · 172 lines · 0 tokens per session scan A f1e8ebb9cfcb
repurpose is a command published in the GitHub repository TheCraigHewitt/seomachine (7,407 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,532 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.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.