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 skills add inhouseseo/superseo-skills --skill semantic-gap-analysisgit clone --depth 1 https://github.com/inhouseseo/superseo-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/skills/inhouseseo/superseo-skills/semantic-gap-analysis)<a href="https://agentmods.dev/skills/inhouseseo/superseo-skills/semantic-gap-analysis"><img src="https://agentmods.dev/badge/skills/inhouseseo/superseo-skills/semantic-gap-analysis/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/skills/inhouseseo/superseo-skills/semantic-gap-analysis"><img src="https://agentmods.dev/badge/skills/inhouseseo/superseo-skills/semantic-gap-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00054 | $0.01233 |
| Opus 5 | $0.00027 | $0.00616 |
| Sonnet 5 | $0.00011 | $0.00247 |
| Haiku 4.5 | $0.00005 | $0.00123 |
Grade A, and why
semantic-gap-analysis 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 7d 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Semantic Gap Analysis
Identifies the exact entities, subtopics, predicates, and relationships that are missing from your page but present in top-ranking competitors. This is the content brief for what to add — not a generic "write more depth" recommendation.
Google's NLP models (BERT, MUM, Gemini) build a semantic graph of your content. If you're missing nodes or edges that competitors have, your content reads as shallow to the algorithm. This skill finds the exact missing nodes.
Input
- URL of your page (required)
- Target keyword the page should rank for (required)
Role
You are a semantic SEO specialist in the tradition of Koray Tuğberk GÜBÜR. You think in entities, attributes, and relationships — not keywords.
Step 1: Read Your Page
Fetch the URL. Extract:
- Main topic and sub-topics
- All named entities (people, places, products, concepts, dates, organizations)
- All predicates (verbs that signal the contextual depth — for "coffee brewing", verbs like grind, extract, bloom, tamp)
- Internal structure: H2/H3 hierarchy
- What the page explicitly covers and what it implicitly assumes
Step 2: Read the Top 3 Competitors
Google the target keyword. Fetch the top 3 results in full. If one won't fetch, take the next result down and say so — a gap list built on an inferred page is worthless. For each:
- Extract entities, predicates, and structural elements the same way
- Note what they cover that your page doesn't
- Note the depth at which they discuss each entity (single mention vs. full section)
Step 3: Build the Semantic Inventory
Create three lists side by side:
| Your page covers | Competitors cover but you don't | Unique to your page |
|---|
Be specific. "Pricing models" is too generic. "Three-tier vs usage-based pricing with examples from Stripe and Twilio" is specific.
Step 4: Classify the Gaps
For each gap, classify its importance:
- Core gap — all 3 competitors cover this, you don't. Critical to add.
- Differentiator gap — 1-2 competitors cover this and it's working for them. Worth adding.
- Commodity gap — everyone covers this superficially. Add briefly or skip.
- Opportunity gap — competitors skip this, you could own it. Your angle.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago Changed f082f7760e27
- 11d ago First seen · 117 lines · 54 tokens per session scan A fda93240026c
semantic-gap-analysis is a skill published in the GitHub repository inhouseseo/superseo-skills (317 stars, last pushed 7d ago), licensed Apache-2.0. It adds 54 tokens to every session and 1,233 once invoked, about $0.0003 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 2026-08-30.
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