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/priorities.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/priorities)<a href="https://agentmods.dev/commands/thecraighewitt/seomachine/priorities"><img src="https://agentmods.dev/badge/commands/thecraighewitt/seomachine/priorities/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/priorities"><img src="https://agentmods.dev/badge/commands/thecraighewitt/seomachine/priorities.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 | $0.01453 |
| Opus 5 | $0.00000 | $0.00727 |
| Sonnet 5 | $0.00000 | $0.00291 |
| Haiku 4.5 | $0.00000 | $0.00145 |
Grade A, and why
priorities 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 10d 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:
- priorities — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Priorities Command
Generate a comprehensive, prioritized content roadmap using multiple SEO research angles.
Usage
/priorities - Comprehensive research (all modules, ~10 minutes)
/priorities quick - Quick wins only (fast, ~2 minutes)
What This Command Does
Comprehensive Mode (Default)
Runs 5 research modules to provide complete content strategy:
- Quick Wins - Keywords ranking 11-20 (page 2)
- Competitor Gaps - What competitors rank for that you don't
- Performance Matrix - Categorize all content by health
- Topic Clusters - Identify topical authority gaps
- Trending Topics - Rising search trends
Generates unified roadmap combining all insights.
Quick Mode
Runs quick wins analysis only - fast turnaround for immediate opportunities.
Process
1. Execute Quick Wins Research
Run the research_quick_wins.py script:
python research_quick_wins.py
This will:
- Pull 30 days of data from Google Search Console
- Find keywords ranking positions 11-20 (quick win opportunities)
- Cross-reference with DataForSEO for rankings and search volume
- Pull engagement metrics from Google Analytics 4
- Calculate opportunity scores based on impressions, position, commercial intent
- Generate detailed report in
research/quick-wins-YYYY-MM-DD.md
2. Read and Parse Results
- Read the generated markdown report
- Extract the top opportunities with highest opportunity scores
- Identify which URLs are already ranking (existing content to update)
- Identify keywords without ranking URLs (new content needed)
3. Categorize Opportunities
For each of the top 10 opportunities, categorize as:
EXISTING CONTENT UPDATE:
- Has a ranking URL on your site
- Content exists but needs optimization to move from page 2 to page 1
- Provide current ranking URL
- Estimate: Moderate effort, high impact
NEW CONTENT CREATION:
- No ranking URL found OR ranking position too low
- Need to create comprehensive content targeting this keyword
- Opportunity to capture search volume
- Estimate: High effort, high impact
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.
- 10d ago First seen · 183 lines · 0 tokens per session scan A bd89b11bdf63
priorities 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 1,453 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.