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-gaps.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-gaps)<a href="https://agentmods.dev/commands/thecraighewitt/seomachine/research-gaps"><img src="https://agentmods.dev/badge/commands/thecraighewitt/seomachine/research-gaps/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-gaps"><img src="https://agentmods.dev/badge/commands/thecraighewitt/seomachine/research-gaps.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 | $0.00429 |
| Opus 5 | $0.00000 | $0.00215 |
| Sonnet 5 | $0.00000 | $0.00086 |
| Haiku 4.5 | $0.00000 | $0.00043 |
Grade A, and why
research-gaps 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 11d 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-gaps — 100% identical, 0 lines differ
What it actually says
Research Gaps Command
Identify content gaps where competitors rank but you don't.
Usage
/research-gaps
What This Command Does
Analyzes 7 competitors to find keywords they rank for (top 20) that you don't rank for at all:
- Direct Competitors: Configured in
config/competitors.jsonor passed as arguments - Content Competitors: Industry blogs and media sites in your niche
For each gap:
- Filters out branded/irrelevant keywords
- Scores opportunity based on volume, difficulty, and intent
- Determines content type needed (listicle, how-to, guide)
- Prioritizes by potential impact
Process
Execute the competitor gap analysis:
python3 research_competitor_gaps.py
This will:
- Fetch your current ranking keywords from GSC
- Analyze each competitor's top 20 ranking keywords
- Identify gaps (they rank, you don't)
- Enrich with search volume, difficulty, SERP features
- Score and prioritize opportunities
- Generate report:
research/competitor-gaps-YYYY-MM-DD.md
Output
The report includes:
- Top 20 content gap opportunities
- Priority level (CRITICAL/HIGH/MEDIUM)
- Competitor intel (who ranks, at what position)
- Keyword metrics (volume, difficulty, CPC)
- Search intent and content type needed
- Specific action steps for each gap
Integration
After running /research-gaps:
- Use
/research-serp [keyword]to analyze what ranks - Use
/write [keyword]to create content brief - Focus on CRITICAL/HIGH priority gaps first
Time & Cost
Time: 3-5 minutes API Cost: ~$1-3 (DataForSEO) - analyzes ~300-500 competitor keywords
When to Run
- Monthly: Full competitive landscape review
- When entering new topic: Find what's missing
- Before content planning: Identify proven opportunities
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.
- 11d ago First seen · 62 lines · 0 tokens per session scan A aea63b3c0549
research-gaps is a command published in the GitHub repository TheCraigHewitt/seomachine (7,427 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 429 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.