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.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/thecraighewitt/seomachine/optimizegit 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/optimize)<a href="https://agentmods.dev/commands/thecraighewitt/seomachine/optimize"><img src="https://agentmods.dev/badge/commands/thecraighewitt/seomachine/optimize.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 | $0.00000 | $0.02402 |
| Opus 5 | $0.00000 | $0.01201 |
| Sonnet 5 | $0.00000 | $0.00480 |
| Haiku 4.5 | $0.00000 | $0.00240 |
Grade A, and why
optimize 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 5d 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:
- optimize — 92% identical, 14 lines differ
How it starts
The opening of the file, as written. The whole thing — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimize Command
Use this command to perform a final SEO optimization pass on completed articles before publishing.
Usage
/optimize [article file]
What This Command Does
- Performs comprehensive SEO audit of article
- Fine-tunes keyword placement and density
- Optimizes meta elements for SERP performance
- Validates internal and external links
- Ensures all SEO best practices are met
Process
Content Audit
Keyword Analysis
- Primary Keyword Density: Check 1-2% density throughout article
- Keyword Placement Check:
- In H1 headline
- In first 100 words
- In at least 2-3 H2 headings
- In meta title
- In meta description
- In URL slug
- Semantic Variations: Verify related keywords are used naturally
- Keyword Stuffing: Ensure no over-optimization or unnatural usage
- LSI Keywords: Confirm latent semantic keywords are present
Heading Structure
- H1: Only one H1, includes primary keyword
- H2s: 4-7 H2 sections, at least 2-3 with keyword variations
- H3s: Proper nesting under H2s, descriptive and keyword-rich
- Hierarchy: Logical progression, no skipped levels (H1→H3)
- Length: Headings are descriptive but concise
Content Quality
- Word Count: Minimum 2000 words (2500-3000+ preferred)
- Paragraph Length: Average 2-4 sentences, no walls of text
- Sentence Length: Varied, averaging under 25 words
- Readability Score: 8th-10th grade level (Flesch-Kincaid)
- Active Voice: Predominantly active voice usage
- Transition Words: Smooth flow between sections
- List Usage: Bullets/numbers for scannability
- Formatting: Bold, italics used appropriately for emphasis
Link Optimization
Internal Links (3-5+ required)
- Quantity: Count current internal links to your company content
- Quality: Verify links are contextually relevant
- Anchor Text: Check for keyword-rich, descriptive anchor text
- Placement: Natural integration within body content
- Variety: Links to different page types (pillar, blog, product, resources)
- Reference: Cross-check @context/internal-links-map.md for opportunities
- Broken Links: Verify all internal links work
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.
- 5d ago First seen · 269 lines · 0 tokens per session scan A 47fa2537a688
optimize is a command published in the GitHub repository TheCraigHewitt/seomachine (7,400 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,402 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.