Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/seite-sh/seite/research-topicsgit clone --depth 1 https://github.com/seite-sh/seiteWrote 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/seite-sh/seite/research-topics)<a href="https://agentmods.dev/commands/seite-sh/seite/research-topics"><img src="https://agentmods.dev/badge/commands/seite-sh/seite/research-topics.svg" alt="Measured on agentmods" 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 6d 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.
This is a copy
100% identical to research-topics — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 6d ago First seen · 132 lines · 0 tokens per session scan A b8f5e3a000d6
research-topics is a command published in the GitHub repository seite-sh/seite (20 stars, last pushed 21d 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. It is 100% identical to research-topics, differing in 0 lines, and is treated as a copy.
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