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/scrub.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/scrub)<a href="https://agentmods.dev/commands/thecraighewitt/seomachine/scrub"><img src="https://agentmods.dev/badge/commands/thecraighewitt/seomachine/scrub/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/scrub"><img src="https://agentmods.dev/badge/commands/thecraighewitt/seomachine/scrub.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 | $0.01237 |
| Opus 5 | $0.00000 | $0.00619 |
| Sonnet 5 | $0.00000 | $0.00247 |
| Haiku 4.5 | $0.00000 | $0.00124 |
Grade C, and why
scrub scanned grade C with 1 finding 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
Content marketing is a powerful strategy—businesses can reach global audiences—and convert more customers. ``` (Contains zero-width spaces after words and em-dashes) **After:** ``` Content marketing is a powerful st Copies of this mod
1 near-identical copy found in the catalogue:
- scrub — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scrub Command
Use this command to remove invisible AI-generated watermarks and telltale patterns from markdown content files.
Usage
/scrub [file path]
What This Command Does
- Removes invisible Unicode watermarks commonly embedded by AI systems
- Replaces em-dashes with contextually appropriate punctuation
- Cleans up whitespace and formatting artifacts
- Makes content appear naturally human-written
- Provides statistics on changes made
Why This Matters
AI language models often embed invisible Unicode characters as watermarks or identifiers in generated content. Additionally, AI tends to overuse certain punctuation patterns like em-dashes. This command removes these telltale signs to make content appear more naturally written.
Process
1. Watermark Detection & Removal
The scrubber identifies and removes several types of invisible Unicode characters:
Invisible Characters Removed
- Zero-width spaces (U+200B): Often inserted between words
- Byte Order Marks (U+FEFF): BOM characters that shouldn't appear in content
- Zero-width non-joiners (U+200C): Invisible formatting characters
- Word joiners (U+2060): Non-breaking invisible characters
- Soft hyphens (U+00AD): Optional hyphenation points
- Narrow no-break spaces (U+202F): Special spacing characters
- All format-control characters: Unicode category Cf characters
2. Em-Dash Replacement
AI-generated content tends to overuse em-dashes (—). The scrubber intelligently replaces them based on context:
Contextual Rules
-
Attribution: Replaces with comma when used for quotes or attribution
- Example: "Text — Author Name" becomes "Text, Author Name"
-
Independent Clauses: Replaces with semicolon when joining complete thoughts
- Example: "First clause — second clause" becomes "First clause; second clause"
-
Strong Breaks: Replaces with period when separating distinct sentences
- Example: "Sentence one — Sentence two" becomes "Sentence one. Sentence two"
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 · 157 lines · 0 tokens per session scan C 859b30964a52
scrub 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 1,237 tokens. A static security scan graded it C with 1 finding (hidden instructions). 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.