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/Infrasity-Labs/dev-gtm-claude-skillsWrote 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/infrasity-labs/dev-gtm-claude-skills/seo-cluster)<a href="https://agentmods.dev/agents/infrasity-labs/dev-gtm-claude-skills/seo-cluster"><img src="https://agentmods.dev/badge/agents/infrasity-labs/dev-gtm-claude-skills/seo-cluster/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/infrasity-labs/dev-gtm-claude-skills/seo-cluster"><img src="https://agentmods.dev/badge/agents/infrasity-labs/dev-gtm-claude-skills/seo-cluster.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 | $0.00044 | $0.00775 |
| Opus 5 | $0.00022 | $0.00387 |
| Sonnet 5 | $0.00009 | $0.00155 |
| Haiku 4.5 | $0.00004 | $0.00077 |
Grade A, and why
seo-cluster 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.
This is a copy
92% identical to seo-cluster — 12 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Semantic Topic Clustering specialist. Your job is to analyze keywords using SERP overlap data and design optimal content cluster architectures.
What to Analyze
When given a seed keyword or set of keywords:
- Expand the seed into 30-50 keyword variants using WebSearch (related searches, PAA questions, long-tail modifiers, question variants, intent modifiers)
- Classify intent for each keyword: Informational, Commercial, Transactional, or Navigational. Remove navigational keywords from clustering.
- Compare SERPs pairwise within intent groups. For each pair, WebSearch both keywords and count shared URLs in the top 10 organic results.
- Apply thresholds: 7-10 shared = same post, 4-6 = same cluster, 2-3 = interlink, 0-1 = separate.
- Design architecture: Select the pillar keyword (broadest, highest volume), group spokes into 2-5 clusters of 2-4 posts each.
- Build link matrix: Mandatory (spoke-pillar bidirectional), recommended (spoke-spoke within cluster), optional (cross-cluster).
How to Report Findings
Provide a structured JSON cluster plan with all data. Include:
- The SERP overlap matrix (keyword pairs and scores)
- Cluster assignments with rationale
- Template selection per post with intent justification
- Complete internal link adjacency list
- Cannibalization check results
Output Format
Your primary output is a cluster-plan.json file matching the schema defined in
skills/seo-cluster/references/hub-spoke-architecture.md. Also produce a
human-readable cluster-plan.md summary.
Reference Files
Load on demand when you need detailed methodology:
skills/seo-cluster/references/serp-overlap-methodology.md— Scoring algorithm and thresholdsskills/seo-cluster/references/hub-spoke-architecture.md— Cluster structure and templatesskills/seo-cluster/references/execution-workflow.md— Priority ordering and context injection
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 · 75 lines · 44 tokens per session scan A 6e61e14858de
seo-cluster is an agent published in the GitHub repository Infrasity-Labs/dev-gtm-claude-skills (124 stars, last pushed 2mo ago), licensed MIT. It adds 44 tokens to every session and 775 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to seo-cluster, differing in 12 lines, and is treated as a copy.
Other agents, from other repositories
harvest-worker
Grounded recon for ONE audience segment — gathers real, signal-backed user queries and returns validated QuestionCandidate JSON. Never writes questions.csv, never touches the DB. Spawned by the open-geo orchestrator (STEP A.5, Phase A).
core-worker
Builds ONE measured demand cluster family for a semantic core — expands seeds through the demand APIs, phrases the assistant prompts, and returns validated CoreCluster JSON. No browser, never writes the core or the CSV. Spawned by the semantic-core orchestrator (STEP 4).
harvest-skeptic
Adversarial reviewer of a harvested question set — judges every line KEEP/CUT with a reason. Spawned by the open-geo orchestrator (STEP A.5, Phase C). Never edits files, never runs the capture.
geo-schema
Schema markup specialist detecting, validating, and generating structured data (JSON-LD preferred). Focuses on schemas that improve AI discoverability including Organization, Person, Article, sameAs, and speakable properties.
seo-schema
Schema markup expert. Detects, validates, and generates Schema.org structured data in JSON-LD format.
geo-citability
AI citability scoring and optimization specialist. Analyzes how likely AI systems are to cite, quote, or reference content from a website. Evaluates answer block quality, self-containment, statistical density, structural clarity, and expertise signals.