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-performance.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-performance)<a href="https://agentmods.dev/commands/thecraighewitt/seomachine/research-performance"><img src="https://agentmods.dev/badge/commands/thecraighewitt/seomachine/research-performance/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-performance"><img src="https://agentmods.dev/badge/commands/thecraighewitt/seomachine/research-performance.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 | $0.00469 |
| Opus 5 | $0.00000 | $0.00234 |
| Sonnet 5 | $0.00000 | $0.00094 |
| Haiku 4.5 | $0.00000 | $0.00047 |
Grade A, and why
research-performance 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- research-performance — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Performance Command
Categorize all content by traffic and rankings to prioritize optimization.
Usage
/research-performance
What This Command Does
Analyzes ALL your blog content and categorizes into 4 performance quadrants:
- ⭐ Stars - High traffic + Good rankings → Maintain & expand
- 🚀 Overperformers - High traffic + Poor rankings → Learn why, improve SEO
- ⚠️ Underperformers - Low traffic + Good rankings → Fix CTR (title/meta)
- 📉 Declining - Low traffic + Poor rankings → Refresh or redirect
For each piece:
- Traffic trends (rising/stable/declining)
- Expected vs actual traffic
- Specific action recommendations
- Priority level
Process
Execute the performance matrix analysis:
python3 research_performance_matrix.py
This will:
- Fetch all pages from GA4 (last 90 days)
- Filter to content pages only
- Enrich with GSC ranking data
- Calculate traffic trends (180-day comparison)
- Categorize into performance quadrants
- Generate report:
research/performance-matrix-YYYY-MM-DD.md
Output
The report includes:
- Distribution across 4 quadrants
- Top performers in each category
- Specific action steps per article
- Expected traffic calculations
- Priority recommendations
Key Insights
Stars: Your best content - keep fresh, expand with clusters Underperformers: QUICK WINS - rewrite titles/meta for better CTR Declining: Content losing traction - needs refresh or redirect Overperformers: Getting traffic despite poor rankings - improve SEO
Integration
After running /research-performance:
- Use
/analyze-existing [URL]for detailed content analysis - Fix underperformer titles/meta first (low effort, high impact)
- Refresh declining stars to prevent traffic loss
Time & Requirements
Time: 2-4 minutes Requirements: GA4 required, GSC recommended Cost: Free
When to Run
- Monthly: Monitor content health
- After major updates: Track impact
- When traffic drops: Identify declining content
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 · 72 lines · 0 tokens per session scan A 49cdb9cc8faf
research-performance 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 469 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.