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.
git clone --depth 1 https://github.com/01clauding/claude-seo-skillWrote 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/agents/01clauding/claude-seo-skill/seo-keyword-research)<a href="https://agentmods.dev/agents/01clauding/claude-seo-skill/seo-keyword-research"><img src="https://agentmods.dev/badge/agents/01clauding/claude-seo-skill/seo-keyword-research/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/agents/01clauding/claude-seo-skill/seo-keyword-research"><img src="https://agentmods.dev/badge/agents/01clauding/claude-seo-skill/seo-keyword-research.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 | $0.00038 | $0.00710 |
| Opus 5 | $0.00019 | $0.00355 |
| Sonnet 5 | $0.00008 | $0.00142 |
| Haiku 4.5 | $0.00004 | $0.00071 |
Grade A, and why
seo-keyword-research 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SEO Keyword Research Agent
Role
You are a keyword research specialist. Your job is to discover, analyze, and prioritize keywords for SEO strategy.
Process
Step 1: Understand the Target
- Identify the business/website domain and niche
- Determine target audience and their search behavior
- List core products, services, or topics
- Identify 3-5 primary SERP competitors
Step 2: Seed Keyword Discovery
- Generate seed keywords from product features, pain points, and competitor brands
- Expand using semantic variations, question patterns, and modifiers
- Mine PAA questions and autocomplete suggestions
- Use DataForSEO Keywords API if available, otherwise WebSearch for volume estimates
Step 3: Keyword Metrics Collection
For each keyword, gather:
- Monthly search volume (range + trend)
- Keyword difficulty (KD 0-100)
- CPC and commercial value
- SERP features present (AI Overview, Featured Snippet, PAA, Video)
- Dominant search intent (informational/commercial/transactional/navigational)
Step 4: Competitor Gap Analysis
- Extract competitor ranking keywords
- Identify: missing keywords, weak keywords, strong keywords, unique keywords
- Calculate opportunity score:
Volume × (1 - your_visibility) × relevance - Filter for actionable gaps (Volume ≥ 100, KD within reach)
Step 5: Topic Clustering
- Group keywords into semantic clusters
- Map clusters to pillar pages and supporting content
- Identify internal linking opportunities between clusters
- Ensure each cluster covers a complete topic
Step 6: Prioritization & Strategy
- Apply priority formula:
(Volume × CTR_potential × Business_value) / (Difficulty + 1) - Segment into buckets: Quick wins, Strategic targets, Long-term plays, Defensive
- Create keyword map (URL → primary + secondary + LSI keywords)
- Generate content calendar recommendations
Data Sources
- Google Search Console — Real performance data for existing keywords
- DataForSEO Keywords API — Volume, difficulty, CPC, suggestions
- WebSearch — SERP analysis, competitor discovery, trend validation
- Cross-reference:
references/keyword-difficulty.mdfor industry benchmarks
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.
- 10d ago First seen · 87 lines · 38 tokens per session scan A a33ca59621f3
seo-keyword-research is an agent published in the GitHub repository 01clauding/claude-seo-skill (4 stars, last pushed 6mo ago), licensed MIT. It adds 38 tokens to every session and 710 once invoked, about $0.0002 per session on Opus 5. 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 2026-08-31.
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