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Agent skill

Semantic Gap Analysis

by inhouseseo in inhouseseo/superseo-skills

A skill your agent uses when a page ranks for a keyword but isn't in the top 3 and you want to know exactly what's missing.

Apache-2.0Auto-check passedMarketing & SEO

Install Semantic Gap Analysis

skills CLI
$ npx skills add inhouseseo/superseo-skills --skill semantic-gap-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install inhouseseo/superseo-skills semantic-gap-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/inhouseseo/superseo-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/semantic-gap-analysis .claude/skills/semantic-gap-analysis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
semantic-gap-analysis
GitHub stars
352
Token cost
~1.4k tokens
SKILL.md length
754 words
Files
5 (incl. references)
Skills in repo
11
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when a page ranks for a keyword but isn't in the top 3 and you want to know exactly what's missing.

  • Works in 6 steps: Read Your Page → Read the Top 3 Competitors → Build the Semantic Inventory → …
  • A page ranks for a keyword but isnt in the top 3 and you want to know exactly whats missing
  • SKILL.md covers Input, Role, Step 1: Read Your Page and Step 2: Read the Top 3…, plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Semantic Gap Analysis is an agent skill from inhouseseo/superseo-skills. Use when a page ranks for a keyword but isn't in the top 3 and you want to know exactly what's missing. The agent compares the page to the top-ranking competitors and produces a specific list of entities, subtopics, and relationships to add.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `references/eav-triple-worked-examples.md`, `references/gap-classification-rubric.md` and `references/predicate-verb-fields.md`).

It sits in Marketing & SEO. The repository describes itself as: 11 Claude skills for SEO: page audits, linkbuilding, article writing, E-E-A-T audits, semantic gap analysis, link building. Methodology from Koray Tuğberk, Kyle Roof, and Lily…. The licence is Apache-2.0.

When your agent uses it

  • A page ranks for a keyword but isnt in the top 3 and you want to know exactly whats missing

Example prompts

  • “t in the top 3 and you want to know exactly what”
  • “/semantic-gap-analysis”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Read Your Page
  2. Read the Top 3 Competitors
  3. Build the Semantic Inventory
  4. Classify the Gaps
  5. Map Entity Relationships
  6. Output

What it can do on your machine

Read from SKILL.md and the folder at commit 9cf22cc. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Semantic Gap Analysis loads about 1.4k tokens when it runs, and up to ~9k if it reads all its reference files. Until then it costs about 66 tokens; SKILL.md has 754 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~66
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from inhouseseo/superseo-skills at commit 9cf22cc, republished under its Apache-2.0 licence (© inhouseseo). 754 words, ~1,392 tokens.

Download SKILL.mdSave it as .claude/skills/semantic-gap-analysis/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
semantic-gap-analysis
description
Use when a page ranks for a keyword but isn't in the top 3 and you want to know exactly what's missing. The agent compares the page to the top-ranking competitors and produces a specific list of entities, subtopics, and relationships to add.

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 coversCompetitors cover but you don'tUnique 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.

Step 5: Map Entity Relationships

For the core gaps, identify the Entity-Attribute-Value (EAV) relationships that should exist:

  • Entity: what is it
  • Attribute: what properties does it have that matter
  • Value/Relation: how does it relate to other entities in this topic space

Example for "espresso machine reviews":

  • Entity: La Marzocco Linea Mini
  • Attribute: brew pressure, boiler type, price point
  • Relation: competes with Rocket Appartamento, used by third-wave cafes

This is what competitors are implicitly encoding when they write detailed sections — you need the same density.

Step 6: Output

Semantic Fingerprint

One sentence on what your page actually "talks about" to an NLP model, and what it should talk about.

Your Page

List of entities and predicates currently present.

Show full SKILL.md (299 more words)Show less
Competitor Coverage

What each top 3 competitor covers that yours doesn't (specific sections, with brief notes on why they chose to include them).

Gap List
GapImportanceAdd to sectionDepth required

Importance: Core / Differentiator / Commodity / Opportunity. Section: where in your H2/H3 structure this belongs. Depth: Paragraph / subsection / full section.

Entity Relationships to Encode

The EAV triples that should appear in your page, even if just in passing. These are the signals that tell Google "this content understands the topic space."

Unique Angle to Preserve

What your page does well that competitors don't. Don't lose this when adding depth.

Content Addition Plan

Specific sections to add or expand, in order of priority. Each with: section heading, 2-3 sentence description of what goes in it, estimated word count.

What to Ignore

  • Keyword density for related terms — don't force. Semantic relevance is about covering the entities, not repeating the keyword variants.
  • Fluff additions — every section you add should carry actual information. Padding defeats the purpose.
  • Wikipedia-style completeness — you don't need to cover everything. You need to cover enough that an NLP model recognizes the topic depth.

Next Step

Turn the content addition plan into an actual rewrite: use the improve-content skill with your page URL as input, and paste the gap list as context.

Bundled references

Load from references/ only when the step calls for them.

  • eav-triple-worked-examples.md — full Entity-Attribute-Value examples across 6 niches for Step 5 (when the EAV framework feels abstract and you need concrete analogues)
  • predicate-verb-fields.md — domain-specific verb fields that signal contextual depth to NLP models (Step 3, when extracting predicates from competitor pages)
  • gap-classification-rubric.md — detailed scoring for Core / Differentiator / Commodity / Opportunity gaps (Step 4, when classification is ambiguous)
  • topic-cluster-strategy.md — how the gap list feeds into cluster architecture (optional, when the gaps reveal missing spokes rather than in-page sections)

© inhouseseo, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (references) in skills/semantic-gap-analysis of inhouseseo/superseo-skills.

  • SKILL.md
  • references/eav-triple-worked-examples.md
  • references/gap-classification-rubric.md
  • references/predicate-verb-fields.md
  • references/topic-cluster-strategy.md

Open the folder on GitHubat commit 9cf22cc

Compare with similar skills

Semantic Gap Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Semantic Gap Analysis compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Semantic Gap Analysis this skillinhouseseo/superseo-skills352—~1.4kAutomated safety check: PassApache-2.0
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Hreflang and International SEOAgriciDaniel/claude-seo19k5 repos~3.4kAutomated safety check: PassMIT
Referralscoreyhaines31/marketingskills54k2 repos~2.6kAutomated safety check: PassMIT
SEO GeoReScienceLab/opc-skills1.8k4 repos~2.1kAutomated safety check: PassApache-2.0

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Categories

Questions about Semantic Gap Analysis

What does Semantic Gap Analysis do?

A skill your agent uses when a page ranks for a keyword but isn't in the top 3 and you want to know exactly what's missing. Semantic Gap Analysis is an agent skill from inhouseseo/superseo-skills. Use when a page ranks for a keyword but isn't in the top 3 and you want to know exactly what's missing.

When should I use Semantic Gap Analysis?

Semantic Gap Analysis fits situations like: A page ranks for a keyword but isnt in the top 3 and you want to know exactly whats missing.

How do I install Semantic Gap Analysis in Claude Code?

Run `npx skills add inhouseseo/superseo-skills --skill semantic-gap-analysis -a claude-code`. Or copy the skill folder (skills/semantic-gap-analysis in inhouseseo/superseo-skills) into .claude/skills/semantic-gap-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Semantic Gap Analysis in Codex?

Run `npx skills add inhouseseo/superseo-skills --skill semantic-gap-analysis -a codex`. Or copy the skill folder (skills/semantic-gap-analysis in inhouseseo/superseo-skills) into .agents/skills/semantic-gap-analysis in your project. Codex loads it when a task matches its description.

Can I use Semantic Gap Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add inhouseseo/superseo-skills --skill semantic-gap-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/semantic-gap-analysis, .gemini/skills/semantic-gap-analysis, .github/skills/semantic-gap-analysis and .opencode/skills/semantic-gap-analysis in your project.

What does Semantic Gap Analysis need to run?

SKILL.md names no scripts, command-line tools or credentials: Semantic Gap Analysis is instructions for the agent only.

Does Semantic Gap Analysis access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Semantic Gap Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Semantic Gap Analysis use?

Semantic Gap Analysis is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Semantic Gap Analysis use?

About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.6k tokens, read only when the agent opens those files.

What are the alternatives to Semantic Gap Analysis?

Skills that share tags, products or a category with Semantic Gap Analysis: Geo Fundamentals (wasp-lang/wasp, 19k stars), Ab Testing (coreyhaines31/marketingskills, 54k stars), Hreflang and International SEO (AgriciDaniel/claude-seo, 19k stars) and Referrals (coreyhaines31/marketingskills, 54k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Semantic Gap Analysis?

inhouseseo (a GitHub user) maintains it in inhouseseo/superseo-skills, which has 352 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on September 3, 2026.

Source: inhouseseo/superseo-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.