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-topics.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-topics)<a href="https://agentmods.dev/commands/thecraighewitt/seomachine/research-topics"><img src="https://agentmods.dev/badge/commands/thecraighewitt/seomachine/research-topics/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-topics"><img src="https://agentmods.dev/badge/commands/thecraighewitt/seomachine/research-topics.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 | $0.00812 |
| Opus 5 | $0.00000 | $0.00406 |
| Sonnet 5 | $0.00000 | $0.00162 |
| Haiku 4.5 | $0.00000 | $0.00081 |
Grade A, and why
research-topics 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 13d 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-topics — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Topics Command
Analyze topical authority by clustering keywords into related topics.
Usage
/research-topics
What This Command Does
Groups all your ranking keywords into topic clusters and identifies:
- Strong Authority Topics: Where you dominate (maintain & expand)
- Moderate Authority Topics: Partial coverage (strengthen)
- Weak Authority Topics: BIGGEST OPPORTUNITY (build comprehensive clusters)
- Coverage Gaps: Related keywords within each topic you don't rank for
For each topic:
- Authority score (0-100) based on coverage, position, demand
- Number of keywords ranking
- Average position
- Total impressions and clicks
- Coverage gaps to fill
Process
Execute topic cluster analysis:
python3 research_topic_clusters.py
This will:
- Fetch all ranking keywords from GSC (90 days)
- Cluster keywords into topics using:
- ML clustering (TF-IDF + K-means) if sklearn available
- Pattern-based clustering as fallback
- Calculate authority score for each cluster
- Identify coverage gaps using DataForSEO
- Prioritize weak clusters with high demand
- Generate report:
research/topic-clusters-YYYY-MM-DD.md
Output
The report includes:
Authority Distribution
- Strong Authority: Topics you dominate
- Moderate Authority: Partial coverage
- Weak Authority: OPPORTUNITIES
- Minimal Authority: Major gaps
Weak Authority Topics (FOCUS HERE!)
For each weak cluster:
- Authority score and level
- Current keyword count
- Average position
- Total impressions
- Top 5 current keywords
- 8-10 coverage gaps with search volume
- Recommended action (build 8-12 article cluster)
Strong Authority Topics (MAINTAIN)
For each strong cluster:
- Performance metrics
- Top performing keywords
- Expansion opportunities
- Maintenance recommendations
Key Insight
Weak clusters with high demand = Your biggest opportunity
Example: "Content Marketing"
- Only 3 keywords ranking
- Average position 28
- 5,000 impressions/month (HIGH DEMAND!)
- 15+ related keywords you don't rank for
- Action: Build comprehensive 10-article cluster
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.
- 13d ago First seen · 132 lines · 0 tokens per session scan A b8f5e3a000d6
research-topics is a command published in the GitHub repository TheCraigHewitt/seomachine (7,434 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 812 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.