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-trending.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-trending)<a href="https://agentmods.dev/commands/thecraighewitt/seomachine/research-trending"><img src="https://agentmods.dev/badge/commands/thecraighewitt/seomachine/research-trending/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-trending"><img src="https://agentmods.dev/badge/commands/thecraighewitt/seomachine/research-trending.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 | $0.00934 |
| Opus 5 | $0.00000 | $0.00467 |
| Sonnet 5 | $0.00000 | $0.00187 |
| Haiku 4.5 | $0.00000 | $0.00093 |
Grade A, and why
research-trending 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-trending — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Trending Command
Identify topics gaining search interest NOW for time-sensitive content opportunities.
Usage
/research-trending
What This Command Does
Analyzes search trends to find keywords experiencing rapid growth:
- Compares last 7 days vs previous 30 days
- Identifies topics with significant impression increases
- Calculates urgency based on growth rate
- Prioritizes by opportunity score
- Shows your current position for each trend
⏰ TIME-SENSITIVE: These are hot trends - act quickly before they cool or competition increases.
Process
Execute trending analysis:
python3 research_trending.py
This will:
- Get trending queries from GSC (7d vs 30d comparison)
- Filter to minimum 20 impressions (avoid noise)
- Enrich with search volume from DataForSEO
- Analyze search intent
- Calculate opportunity score based on:
- Growth rate (40%)
- Search volume (30%)
- Current position advantage (30%)
- Determine urgency level
- Generate report:
research/trending-YYYY-MM-DD.md
Output
The report categorizes by urgency:
🔥 CRITICAL Urgency (+150% growth)
Act within 1 week - These topics are exploding NOW
Example:
- "ai content optimization" +429% growth
- Your position: 27
- Volume: 1,800/mo
- Action: Create comprehensive guide within 3-7 days
⚡ HIGH Urgency (+75% growth)
Act within 2 weeks - Strong upward trends
⏳ MODERATE Urgency (+30% growth)
Act within 1 month - Steady growth, monitor
For each trend:
- Growth percentage and trajectory
- Current position (your visibility)
- Search volume (if available)
- Opportunity score
- Specific action steps
- Timeline to act
Key Actions
If You Already Rank (Position ≤30)
Quick Win!
- Update existing content immediately
- Add trending angle/section
- Update title with current year
- Optimize for trending query
- Timeline: 3-5 days
If You Don't Rank (Position >30)
New Content Needed
- Create comprehensive 2000+ word guide
- Publish within 3-7 days
- Promote on social immediately
- Consider paid promotion to accelerate
- Timeline: 1 week max
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 · 146 lines · 0 tokens per session scan A 125a24a57229
research-trending 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 934 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.