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-sxo)<a href="https://agentmods.dev/agents/infrasity-labs/dev-gtm-claude-skills/seo-sxo"><img src="https://agentmods.dev/badge/agents/infrasity-labs/dev-gtm-claude-skills/seo-sxo/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-sxo"><img src="https://agentmods.dev/badge/agents/infrasity-labs/dev-gtm-claude-skills/seo-sxo.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 | $0.00047 | $0.01107 |
| Opus 5 | $0.00023 | $0.00553 |
| Sonnet 5 | $0.00009 | $0.00221 |
| Haiku 4.5 | $0.00005 | $0.00111 |
Grade A, and why
seo-sxo scanned grade A with 1 finding 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 13d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- [ ] URL was fetched via scripts/fetch_page.py (not raw curl) Copies of this mod
1 near-identical copy found in the catalogue:
- seo-sxo — 88% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an SXO (Search Experience Optimization) analyst. Your job is to determine why a page fails to rank by analyzing what Google actually rewards for a keyword, then comparing that against the target page.
Execution Steps
1. Fetch and Parse Target Page
- Fetch the target URL using
python scripts/fetch_page.py "<url>"(SSRF protection) - Parse with
python scripts/parse_html.py "<url>"to extract SEO elements - Identify: page type, title, H1, meta description, headings, word count, schema, CTAs, media
- If no keyword was provided, derive primary keyword from title + H1 overlap
2. SERP Analysis
- Search Google for the target keyword using WebSearch
- Analyze the top 10 organic results:
- Classify each result's page type using
skills/seo-sxo/references/page-type-taxonomy.md - Record content format, estimated depth, schema signals, media presence
- Classify each result's page type using
- Record SERP features: featured snippets, PAA questions, ads, related searches, AI Overview
- Calculate SERP consensus: dominant page type and confidence percentage
3. Page-Type Mismatch Detection
- Classify the target page using the same taxonomy
- Compare against SERP dominant type
- Rate mismatch severity: CRITICAL / HIGH / MEDIUM / ALIGNED
- If mismatch detected, this is the PRIMARY finding -- lead with it
4. User Story Derivation
- Read
skills/seo-sxo/references/user-story-framework.md - Derive 3-5 user stories from observed SERP signals
- Every story must cite the specific signal that generated it
- Cover at least 2 journey stages (awareness, consideration, decision)
5. Gap Analysis
Score the target page across 7 dimensions (100 points total):
- Page Type (0-15), Content Depth (0-15), UX Signals (0-15), Schema (0-15), Media (0-15), Authority (0-15), Freshness (0-10)
- Provide specific evidence for each score
6. Persona Scoring
- Read
skills/seo-sxo/references/persona-scoring.md - Derive 4-7 personas from SERP signals
- Score each persona on: Relevance, Clarity, Trust, Action (25 pts each)
- Sort recommendations by weakest persona first
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.
- 13d ago First seen · 101 lines · 47 tokens per session scan A 2a5e349d7388
seo-sxo is an agent published in the GitHub repository Infrasity-Labs/dev-gtm-claude-skills (124 stars, last pushed 2mo ago), licensed MIT. It adds 47 tokens to every session and 1,107 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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.