New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Geo Brand Mentions

by zubair-trabzada in zubair-trabzada/geo-seo-claude

Brand mention and authority scanner for AI visibility. An agent skill from zubair-trabzada/geo-seo-claude.

MITAuto-check: notesMarketing & SEO

Install Geo Brand Mentions

skills CLI
$ npx skills add zubair-trabzada/geo-seo-claude --skill geo-brand-mentions -a claude-code

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

GitHub CLI
$ gh skill install zubair-trabzada/geo-seo-claude geo-brand-mentions --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/zubair-trabzada/geo-seo-claude.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/geo-brand-mentions .claude/skills/geo-brand-mentions && 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
geo-brand-mentions
GitHub stars
11k
Token cost
~5.8k tokens
SKILL.md length
2,431 words
Files
1
Skills in repo
16
Repo updated
First seen
Licence
MIT

At a glance

Brand mention and authority scanner for AI visibility. An agent skill from zubair-trabzada/geo-seo-claude.

  • Works in 10 steps: YouTube Mentions -- Correlation ~0.737… → Reddit Mentions -- High Correlation → Wikipedia Presence -- High Correlation → …
  • Tasks that involve AI search optimization
  • SKILL.md covers Core Insight, Platform Importance Ranking…, Composite Brand Authority Score and Analysis Procedure, plus 2 more sections
  • Calls python3; reaches en.wikipedia.org and wikidata.org

What it does

Geo Brand Mentions is an agent skill from zubair-trabzada/geo-seo-claude. Brand mention and authority scanner for AI visibility. Analyzes brand presence across platforms that AI models rely on for entity recognition and citation decisions. Produces a Brand Authority Score (0-100) with platform-specific recommendations.

Its SKILL.md is about 5.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Marketing & SEO, covering AI search optimization. It works with YouTube and Reddit. The repository describes itself as: GEO-first SEO skill for Claude Code. Comprehensive AI search optimization for any website — citability scoring, AI crawler analysis, brand authority, schema markup…. The licence is MIT.

When your agent uses it

  • Tasks that involve AI search optimization

Example prompts

  • “/geo-brand-mentions”

Requirements

  • Pre-approved tools (allowed-tools): Read, Grep, Glob, Bash, WebFetch, Write

Workflow steps

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

  1. YouTube Mentions -- Correlation ~0.737 (STRONGEST)
  2. Reddit Mentions -- High Correlation
  3. Wikipedia Presence -- High Correlation
  4. LinkedIn Presence -- Moderate Correlation
  5. Other Platform Presence -- Supplementary
  6. Identify Brand Information
  7. Platform Scanning
  8. Sentiment Assessment
  9. Competitive Comparison (Optional)
  10. Score Calculation

What it can do on your machine

Read from SKILL.md and the folder at commit 989cae0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Grep
    • Glob
    • Bash
    • WebFetch
    • Write

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • en.wikipedia.org
    • wikidata.org

    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

Geo Brand Mentions loads about 5.8k tokens when it runs. Until then it costs about 66 tokens; SKILL.md has 2,431 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
~5.8k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Grep, Glob, Bash, WebFetch, Write

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 zubair-trabzada/geo-seo-claude at commit 989cae0, republished under its MIT licence (© zubair-trabzada). 2,431 words, ~5,774 tokens.

Download SKILL.mdSave it as .claude/skills/geo-brand-mentions/SKILL.md (or your agent's skills folder).
name
geo-brand-mentions
description
Brand mention and authority scanner for AI visibility. Analyzes brand presence across platforms that AI models rely on for entity recognition and citation decisions. Produces a Brand Authority Score (0-100) with platform-specific recommendations.
allowed-tools
Read, Grep, Glob, Bash, WebFetch, Write

Brand Mention Scanner Skill

Core Insight

Brand mentions correlate approximately 3x more strongly with AI visibility than traditional backlinks. An Ahrefs study published in December 2025, analyzing 75,000 brands across AI search platforms, found that unlinked brand mentions -- references to a brand name without a hyperlink -- are a stronger predictor of whether AI systems cite and recommend a brand than Domain Rating or backlink count.

The critical finding: the platform where the mention appears matters enormously. Not all mentions are equal. A mention on YouTube or Reddit carries far more weight for AI citation than a mention on a low-authority blog, because AI training data and retrieval systems disproportionately index high-engagement platforms.

This inverts a core assumption of traditional SEO. In traditional SEO, a backlink from a high-DR site is the gold standard. In GEO, an unlinked mention on Reddit or a YouTube video description may be more valuable than a dofollow backlink from a DR 70 blog.

Platform Importance Ranking for AI Citations

Based on the Ahrefs December 2025 study and corroborating research from Profound (2025) and Terakeet (2025):

1. YouTube Mentions -- Correlation ~0.737 (STRONGEST)

Why YouTube matters most:

  • YouTube is the second-largest search engine and the largest video platform globally (2.5B+ monthly users).
  • AI training datasets heavily incorporate YouTube transcripts, descriptions, and metadata.
  • Google's Gemini and AI Overviews directly reference YouTube content.
  • Perplexity and ChatGPT both index and cite YouTube video content.
  • YouTube transcripts are particularly valuable because they contain natural language mentions in conversational context, which aligns with how AI models process and generate text.

What to check:

  • Brand YouTube channel: Does the brand have an active YouTube channel? How many subscribers? Video count? Upload frequency?
  • Third-party video mentions: Are other YouTubers or channels mentioning the brand? In what context (reviews, tutorials, comparisons)?
  • Video descriptions: Does the brand name appear in video descriptions of industry-relevant content?
  • Video transcripts: Is the brand mentioned in spoken content of relevant videos? (AI models index transcripts)
  • YouTube search presence: When searching "[brand name]" on YouTube, do results appear? Are they positive?
  • Comment mentions: Is the brand mentioned in comments on relevant industry videos?

Scoring for YouTube (0-100):

ScoreCriteria
90-100Active channel with 10K+ subscribers, regular uploads, brand mentioned in 20+ third-party videos, appears in YouTube search results for industry terms
70-89Active channel with 1K+ subscribers, brand mentioned in 10-19 third-party videos, some YouTube search presence
50-69Channel exists with some content, brand mentioned in 5-9 third-party videos, limited YouTube search presence
30-49Channel exists but inactive, brand mentioned in 1-4 third-party videos
10-29No channel or empty channel, brand mentioned in 1-2 videos only
0-9No YouTube presence whatsoever
2. Reddit Mentions -- High Correlation

Why Reddit matters:

  • Reddit is one of the most heavily indexed platforms in AI training data (confirmed in Google's $60M/year Reddit licensing deal, 2024).
  • AI systems heavily weight Reddit for product recommendations, comparisons, and user sentiment.
  • "Reddit" is now appended to an estimated 10-15% of Google searches by users seeking authentic opinions.
  • Perplexity frequently cites Reddit threads as sources.
  • ChatGPT and Claude both reference Reddit discussions when answering product/service questions.

What to check:

  • Subreddit presence: Is the brand discussed in relevant subreddits? Which ones?
  • Mention volume: How many Reddit threads mention the brand? What is the trend (increasing/decreasing)?
  • Sentiment: Are mentions mostly positive, negative, or neutral? What are common praise points and complaints?
  • Official presence: Does the brand have an official Reddit account? Do they participate in discussions? Have they done AMAs?
  • Recommendation threads: Does the brand appear in "What do you recommend for X?" threads? Is it the top recommendation or an also-ran?
  • Subreddit community: Does the brand have its own subreddit? How active is it?

Scoring for Reddit (0-100):

ScoreCriteria
90-100Frequently recommended in relevant subreddits, predominantly positive sentiment, active official presence, own subreddit with 5K+ members, appears in top recommendations for industry queries
70-89Regularly mentioned in relevant subreddits, mostly positive sentiment, some official presence, appears in multiple recommendation threads
50-69Mentioned in several relevant threads, mixed sentiment, brand name is recognized by community members
30-49Occasional mentions, limited to 1-2 subreddits, no official presence
10-29Rare mentions, brand largely unknown on Reddit
0-9No Reddit presence
3. Wikipedia Presence -- High Correlation

Why Wikipedia matters:

  • Wikipedia is one of the highest-authority sources in AI training data. All major AI models have been trained on Wikipedia dumps.
  • AI systems use Wikipedia as a primary source for entity recognition -- determining whether a brand is a "real" entity worth knowing about.
  • Wikidata (Wikipedia's structured data sibling) provides machine-readable facts that AI models use for knowledge graph construction.
  • Having a Wikipedia page is a strong signal of notability, which correlates with AI systems treating the brand as an authoritative entity.

What to check:

  • Wikipedia page: Does the brand or company have its own Wikipedia article? Is it marked for deletion or quality issues?
  • Founder page: Does the founder/CEO have a Wikipedia page? (Strong authority signal)
  • Wikipedia citations: Is the brand's website cited as a reference in any Wikipedia articles?
  • Wikidata entry: Does the brand have a Wikidata item (Q-number)? How complete is it?
  • Wikipedia mentions: Is the brand mentioned in other Wikipedia articles (industry articles, competitor pages, category pages)?
  • Article quality: If a Wikipedia page exists, is it a stub, start-class, or higher quality?

Scoring for Wikipedia (0-100):

ScoreCriteria
90-100Detailed Wikipedia article (B-class or higher), Wikidata entry with complete properties, brand cited as reference in multiple articles, founder has Wikipedia page
70-89Wikipedia article exists (start-class or higher), Wikidata entry exists, brand mentioned in 2+ other Wikipedia articles
50-69Wikipedia article exists (stub or start), basic Wikidata entry, limited mentions in other articles
30-49No Wikipedia article but brand is mentioned in other articles or cited as reference; Wikidata entry may exist
10-29Brand mentioned in 1-2 Wikipedia articles as a passing reference only
0-9No Wikipedia or Wikidata presence of any kind
4. LinkedIn Presence -- Moderate Correlation

Why LinkedIn matters:

  • LinkedIn content is increasingly indexed by AI systems for professional and B2B context.
  • Company LinkedIn pages and employee thought leadership posts build brand entity signals.
  • AI models reference LinkedIn for company information, team credentials, and professional authority.
  • LinkedIn articles and posts are indexed by search engines and AI crawlers.

What to check:

  • Company page: Does the brand have a LinkedIn company page? Follower count? Post frequency?
  • Employee thought leadership: Are employees (especially leadership) posting thought leadership content that mentions the brand?
  • Company mentions: Is the brand mentioned in LinkedIn posts by non-employees? Industry analysts? Customers?
  • LinkedIn articles: Are there long-form LinkedIn articles about or mentioning the brand?
  • Employee profiles: Do employees list the company with detailed descriptions? Do they have strong professional profiles?
  • Engagement metrics: What is the typical engagement (likes, comments, shares) on company posts?

Scoring for LinkedIn (0-100):

ScoreCriteria
90-100Active company page with 10K+ followers, leadership regularly posts thought leadership, brand frequently mentioned by industry professionals, strong employee profiles
70-89Active company page with 5K+ followers, some employee thought leadership, occasional third-party mentions
50-69Company page exists with 1K+ followers, irregular posting, limited third-party mentions
30-49Company page exists but is sparse or inactive, few followers, no third-party mentions
10-29Basic company page with minimal information
0-9No LinkedIn company page
5. Other Platform Presence -- Supplementary

These platforms have lower but still meaningful correlation with AI visibility:

Quora
  • Relevance: Quora answers are frequently included in AI training data and cited by Perplexity.
  • What to check: Is the brand mentioned in Quora answers to industry-relevant questions? Does the brand have an official Quora presence?
  • Signal strength: Moderate for B2C, lower for B2B.
Stack Overflow / Stack Exchange
  • Relevance: Critical for developer-facing brands (SaaS, dev tools, APIs).
  • What to check: Is the brand's product discussed in Stack Overflow questions/answers? Does the brand have a tag? Do they have an official account answering questions?
  • Signal strength: High for technical products, irrelevant for most B2C.
GitHub
  • Relevance: Critical for open-source and developer-focused brands.
  • What to check: Does the brand have a GitHub organization? Stars on repositories? Mentions in other repos' documentation or discussions?
  • Signal strength: High for dev tools and open-source, low for non-technical brands.
Industry Forums and Communities
  • Relevance: Niche authority signals that AI models pick up from domain-specific training data.
  • What to check: Is the brand discussed in industry-specific forums (e.g., Hacker News for tech, ProductHunt for startups, industry-specific Slack communities)?
  • Signal strength: Moderate, but valuable for establishing niche authority.
News and Press
  • Relevance: News mentions build entity authority and recency signals.
  • What to check: Has the brand been covered by major news outlets or industry publications? How recently? What was the context?
  • Signal strength: Moderate. Recency matters -- a mention in the last 6 months is far more valuable than one from 3 years ago.
Show full SKILL.md (977 more words)Show less
Podcasts
  • Relevance: Growing AI training data source. Transcripts are increasingly indexed.
  • What to check: Has the brand or its leadership appeared on podcasts? Are podcast transcripts mentioning the brand indexed by search engines?
  • Signal strength: Moderate and growing.

Composite Brand Authority Score

Scoring Formula
PlatformWeightRationale
YouTube Presence25%Strongest correlation with AI citation (0.737)
Reddit Presence25%Second strongest correlation; critical for product recommendations
Wikipedia / Wikidata20%Entity recognition foundation; AI training data cornerstone
LinkedIn Authority15%Professional authority signals; B2B relevance
Other Platforms15%Supplementary signals from Quora, GitHub, news, forums, podcasts

Formula:

Brand_Authority_Score = (YouTube * 0.25) + (Reddit * 0.25) + (Wikipedia * 0.20) + (LinkedIn * 0.15) + (Other * 0.15)
Score Interpretation
Score RangeRatingInterpretation
85-100DominantBrand is a well-recognized entity across AI platforms. Highly likely to be cited and recommended by AI systems.
70-84StrongBrand has solid cross-platform presence. AI systems likely recognize and cite it for relevant queries.
50-69ModerateBrand has presence on some platforms but gaps exist. AI citation is inconsistent.
30-49WeakBrand has limited platform presence. AI systems may not recognize it as a distinct entity.
0-29MinimalBrand has negligible platform presence. AI systems are unlikely to cite or recommend it.

Analysis Procedure

Step 1: Identify Brand Information

Gather the following from the user or from the website:

  • Brand name (exact spelling, including any official variants)
  • Founder/CEO name(s)
  • Domain URL
  • Industry/category
  • Key products or services (top 3)
  • Key competitors (for comparison context)
Step 2: Platform Scanning

For each platform, use WebFetch to search and assess presence:

YouTube Check:

  1. Search: [brand name] site:youtube.com
  2. Check: youtube.com/@[brand-name] or youtube.com/c/[brand-name] for official channel
  3. Search: "[brand name]" site:youtube.com (exact match for mentions in descriptions)
  4. Note: Channel subscriber count, video count, latest upload date, third-party mention count

Reddit Check:

  1. Search: [brand name] site:reddit.com
  2. Search: "[brand name]" site:reddit.com (exact match)
  3. Check: reddit.com/r/[brand-name] for official subreddit
  4. Check: reddit.com/user/[brand-name] for official account
  5. Note: Thread count, dominant subreddits, sentiment (positive/negative/neutral), recommendation frequency

Wikipedia Check (IMPORTANT — use BOTH methods to avoid false negatives):

Method 1 — Python API check (MOST RELIABLE, do this FIRST):

bash
python3 -c "
import requests, json
from urllib.parse import quote_plus
brand = '[Brand_Name]'
# Check Wikipedia API directly
api_url = f'https://en.wikipedia.org/w/api.php?action=query&list=search&srsearch={quote_plus(brand)}&format=json'
r = requests.get(api_url, headers={'User-Agent': 'GEO-Audit/1.0'}, timeout=15)
data = r.json()
results = data.get('query', {}).get('search', [])
if results and brand.lower() in results[0].get('title', '').lower():
    print(f'WIKIPEDIA PAGE EXISTS: {results[0][\"title\"]}')
    print(f'URL: https://en.wikipedia.org/wiki/{results[0][\"title\"].replace(\" \", \"_\")}')
else:
    print('No direct Wikipedia page found')
# Check Wikidata
wd_url = f'https://www.wikidata.org/w/api.php?action=wbsearchentities&search={quote_plus(brand)}&language=en&format=json'
r2 = requests.get(wd_url, headers={'User-Agent': 'GEO-Audit/1.0'}, timeout=15)
wd = r2.json()
entities = wd.get('search', [])
if entities:
    print(f'WIKIDATA ENTRY: {entities[0].get(\"id\", \"\")} — {entities[0].get(\"description\", \"\")}')
"

Method 2 — Direct URL check (backup verification):

  1. WebFetch: https://en.wikipedia.org/wiki/[Brand_Name] — check if the page loads (not a redirect to search)
  2. WebFetch: https://en.wikipedia.org/wiki/[Founder_Name] for founder article

Method 3 — Search (least reliable, use only for supplemental info):

  1. Search: [brand name] site:wikipedia.org
  2. Search: [brand name] site:wikidata.org

CRITICAL: Web search alone is NOT reliable for determining Wikipedia presence. ALWAYS run the Python API check first. If the API says a page exists, it exists — do not override this with a search result that fails to find it.

  1. Note: Article existence, quality, edit history, Wikidata completeness

LinkedIn Check:

  1. Search: [brand name] site:linkedin.com
  2. Check: linkedin.com/company/[brand-name] for company page
  3. Note: Follower count, post frequency, employee count listed, engagement levels

Other Platforms:

  1. Search: [brand name] site:quora.com
  2. Search: [brand name] site:stackoverflow.com (if technical brand)
  3. Search: [brand name] site:github.com (if technical brand)
  4. Search: [brand name] site:news.ycombinator.com (Hacker News)
  5. Search: "[brand name]" broadly for news mentions (filter to last 6 months)
  6. Note: Presence/absence and quality of mentions on each platform
Step 3: Sentiment Assessment

For Reddit and other discussion platforms, assess sentiment by analyzing the most recent and most prominent mentions:

SentimentIndicators
PositiveRecommendations ("I love [brand]," "We switched to [brand] and...", "Highly recommend"), upvoted mentions, positive comparison against competitors
NeutralFactual mentions ("We use [brand] for...", "[Brand] offers..."), questions about the brand, balanced comparisons
NegativeComplaints ("Avoid [brand]", "[Brand] has terrible support"), downvoted recommendations, negative comparisons
MixedCombination of positive and negative. Note the ratio and primary themes.
Step 4: Competitive Comparison (Optional)

If competitors are identified, do a quick scan of their platform presence for context. This helps calibrate the score -- a brand with "moderate" Reddit presence in an industry where competitors have zero Reddit presence is relatively strong.

Step 5: Score Calculation
  1. Score each platform (0-100) using the rubrics above.
  2. Apply weights to calculate the composite Brand Authority Score.
  3. Identify the strongest and weakest platforms.
  4. Generate specific, actionable recommendations for the weakest platforms.

Output Format

Generate a file called GEO-BRAND-MENTIONS.md:

markdown
# Brand Authority Report: [Brand Name]

**Analysis Date:** [Date]
**Brand:** [Brand Name]
**Domain:** [URL]
**Industry:** [Industry]

---

## Brand Authority Score: [X]/100 ([Rating])

### Platform Breakdown

| Platform | Score | Weight | Weighted | Status |
|---|---|---|---|---|
| YouTube | [X]/100 | 25% | [X] | [Active Channel / Mentioned / Absent] |
| Reddit | [X]/100 | 25% | [X] | [Active / Discussed / Absent] |
| Wikipedia | [X]/100 | 20% | [X] | [Article / Mentioned / Absent] |
| LinkedIn | [X]/100 | 15% | [X] | [Active / Basic / Absent] |
| Other Platforms | [X]/100 | 15% | [X] | [Summary] |
| **Total** | | | **[X]/100** | |

---

## Platform Detail

### YouTube ([X]/100)

**Official Channel:** [Yes/No] | [URL if exists]
**Subscribers:** [Count or N/A]
**Videos:** [Count or N/A]
**Last Upload:** [Date or N/A]
**Third-Party Mentions:** [Estimated count]
**Key Findings:**
- [Finding 1]
- [Finding 2]

### Reddit ([X]/100)

**Official Account:** [Yes/No] | [URL if exists]
**Own Subreddit:** [Yes/No] | [URL and member count if exists]
**Mention Volume:** [Estimated thread count]
**Primary Subreddits:** [List of subreddits where brand is discussed]
**Sentiment:** [Positive/Negative/Neutral/Mixed]
**Key Findings:**
- [Finding 1]
- [Finding 2]

### Wikipedia ([X]/100)

**Company Article:** [Yes/No] | [URL if exists]
**Founder Article:** [Yes/No] | [URL if exists]
**Wikidata Entry:** [Yes/No] | [Q-number if exists]
**Cited in Other Articles:** [Yes/No] | [Which articles]
**Key Findings:**
- [Finding 1]
- [Finding 2]

### LinkedIn ([X]/100)

**Company Page:** [Yes/No] | [URL if exists]
**Followers:** [Count or N/A]
**Post Frequency:** [Weekly/Monthly/Rare/Never]
**Key Findings:**
- [Finding 1]
- [Finding 2]

### Other Platforms ([X]/100)

| Platform | Presence | Notes |
|---|---|---|
| Quora | [Yes/No] | [Brief note] |
| Stack Overflow | [Yes/No] | [Brief note] |
| GitHub | [Yes/No] | [Brief note] |
| Hacker News | [Yes/No] | [Brief note] |
| News/Press | [Yes/No] | [Brief note] |
| Podcasts | [Yes/No] | [Brief note] |

---

## Recommendations

### Immediate Actions (Week 1-2)

1. **[Platform]:** [Specific action to take with expected impact]
2. **[Platform]:** [Specific action]

### Short-Term Strategy (Month 1-3)

1. **[Platform]:** [Strategy with tactics]
2. **[Platform]:** [Strategy with tactics]

### Long-Term Authority Building (Month 3-12)

1. **[Platform]:** [Long-term strategy]
2. **[Platform]:** [Long-term strategy]

---

## Competitive Context

[If competitors were analyzed, show a brief comparison table]

| Brand | YouTube | Reddit | Wikipedia | LinkedIn | Other | Total |
|---|---|---|---|---|---|---|
| [Subject Brand] | [X] | [X] | [X] | [X] | [X] | **[X]** |
| [Competitor 1] | [X] | [X] | [X] | [X] | [X] | **[X]** |
| [Competitor 2] | [X] | [X] | [X] | [X] | [X] | **[X]** |

## Key Takeaway

[1-2 sentence summary of the brand's AI visibility standing and the single most impactful action to take]

Reference Data

Correlation Strengths (Ahrefs Dec 2025, 75K Brands)
SignalCorrelation with AI CitationTraditional SEO Value
YouTube mentions~0.737Low (not a ranking factor)
Reddit mentionsHigh (exact coefficient not published)Low
Wikipedia presenceHighModerate (trust signal)
LinkedIn presenceModerateLow
Domain Rating~0.266Very High
Backlink count~0.266Very High
Organic trafficModerateVery High

Key insight: The signals that matter most for AI visibility (YouTube, Reddit) are almost irrelevant in traditional SEO, and the signals that matter most for traditional SEO (backlinks, DR) are weak predictors of AI visibility. This requires a fundamentally different optimization strategy.

Platform-Specific Tips for Building Presence

YouTube Quick Wins:

  • Create a channel and upload 3-5 explainer videos about your core topics.
  • Ensure your brand name appears in video titles, descriptions, and spoken content.
  • Pursue guest appearances on relevant industry YouTube channels.
  • Create comparison or "alternatives" videos (these get cited by AI for comparison queries).

Reddit Quick Wins:

  • Identify 3-5 subreddits where your target audience is active.
  • Participate authentically (do not shill -- Reddit communities detect and punish this).
  • Do an AMA if appropriate for your brand.
  • Monitor and respond to mentions of your brand.
  • Create genuinely helpful posts that naturally mention your brand's expertise.

Wikipedia Strategy:

  • Hire a Wikipedia-knowledgeable consultant -- do NOT edit your own article (conflict of interest).
  • Build notability through press coverage, academic citations, and industry recognition first.
  • Ensure your Wikidata entry is complete even if you do not have a Wikipedia article.
  • Contribute to industry-relevant articles where your brand can be naturally cited as a source.

LinkedIn Quick Wins:

  • Optimize your company page with complete information and regular posting.
  • Encourage leadership to post thought leadership content weekly.
  • Publish LinkedIn articles on topics where your brand has unique expertise.
  • Engage with industry discussions to increase brand visibility in professional contexts.

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

Files

Just SKILL.md in skills/geo-brand-mentions of zubair-trabzada/geo-seo-claude.

Open the folder on GitHubat commit 989cae0

Compare with similar skills

Geo Brand Mentions 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.

Geo Brand Mentions compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Geo Brand Mentions this skillzubair-trabzada/geo-seo-claude11k—~5.8kAutomated safety check: NotesMIT
AI Search Optimizationsocial-media-skills/skills134—~2kAutomated safety check: PassMIT
Blog StrategyAgriciDaniel/claude-blog2.3k—~4.4kAutomated safety check: PassMIT
Blog StrategyInfrasity-Labs/dev-gtm-claude-skills136—~3.8kAutomated safety check: PassMIT
SEO DataforseoAgriciDaniel/codex-seo7992 repos~4.6kAutomated safety check: PassMIT
DataForSEO Live SEO DataAgriciDaniel/claude-seo19k—~4.4kAutomated safety check: PassMIT

Similar skills

  • AI Search Optimization

    social-media-skills/skills

    A skill your agent uses to get a brand and its content CITED and RECOMMENDED by AI answer engines — the GEO (Generative Engine Optimization) / AI-search-visibility skill.

    134 GitHub stars~2k tokensUpdated 9 days ago
    Marketing & SEOAuto-check passed

  • Blog Strategy

    AgriciDaniel/claude-blog

    Blog strategy development including topic cluster architecture with hub-and-spoke design, audience mapping, competitive landscape analysis, AI citation surface strategy across ChatGPT/Perplexity/AI…

    2.3k GitHub stars~4.4k tokensUpdated 2 days ago
    Marketing & SEOAuto-check passed

  • Blog Strategy

    Infrasity-Labs/dev-gtm-claude-skills

    Blog strategy development including topic cluster architecture with hub-and-spoke design, audience mapping, competitive landscape analysis, AI citation surface strategy across ChatGPT/Perplexity/AI…

    136 GitHub stars~3.8k tokensUpdated 3 mo ago
    Marketing & SEOAuto-check passed

  • SEO Dataforseo

    AgriciDaniel/codex-seo

    Live SEO data via DataForSEO MCP server. An agent skill from AgriciDaniel/codex-seo.

    799 GitHub starsUsed in 2 repos~4.6k tokens
    Marketing & SEOAuto-check passed

  • DataForSEO Live SEO Data

    AgriciDaniel/claude-seo

    Pulls live SERP results, keyword metrics, backlink profiles and AI visibility data through the DataForSEO MCP server, with a cost check before each call.

    19k GitHub stars~4.4k tokensUpdated 6 days ago
    Marketing & SEOAuto-check passed

  • Reddit Opportunity Research

    onvoyage-ai/gtm-engineer-skills

    Researches Reddit using a brand's Brand DNA to find promotable pain-point discussions, target subreddits, and real user search language.

    1.3k GitHub stars~2.1k tokensUpdated 4 mo ago
    Marketing & SEOAuto-check passed

More from zubair-trabzada/geo-seo-claude

All 16 skills in this repo

  • GEO-First SEO Audit Tool

    zubair-trabzada/geo-seo-claude

    Audits a website for AI search visibility across ChatGPT, Claude, Perplexity and Google AI Overviews while checking traditional SEO, schema and E-E-A-T content quality.

    11k GitHub stars~2.8k tokensUpdated yesterday
    Auto-check: notes

  • GEO Monthly Delta Report

    zubair-trabzada/geo-seo-claude

    Compares a baseline and a current GEO audit for a client, calculates score changes and action item progress, and writes a monthly progress report.

    11k GitHub stars~2.4k tokensUpdated yesterday
    Auto-check: notes

  • AI Citability Scorer

    zubair-trabzada/geo-seo-claude

    Scores how likely AI assistants are to quote passages from a web page and suggests rewrites that make those passages easier to extract.

    11k GitHub starsUsed in 2 repos~3.7k tokens
    Auto-check: notes

  • GEO Service Proposal Generator

    zubair-trabzada/geo-seo-claude

    Builds a client-ready AI-search-optimization proposal from an existing GEO audit, with pricing tiers, an ROI estimate and a markdown document ready to send.

    11k GitHub stars~3k tokensUpdated yesterday
    Auto-check: notes

  • GEO Content E-E-A-T Scorer

    zubair-trabzada/geo-seo-claude

    Scores a page's content against Google's E-E-A-T framework and AI-citability structure, then writes a scored gap-analysis report.

    11k GitHub starsUsed in 2 repos~4k tokens
    Auto-check: notes

  • GEO Prospect Tracker

    zubair-trabzada/geo-seo-claude

    Tracks GEO agency leads and clients through a sales pipeline in a local JSON file, with notes, audit scores, deal values and a pipeline summary.

    11k GitHub stars~1.7k tokensUpdated yesterday
    Auto-check: notes

Works with

Categories

Questions about Geo Brand Mentions

What does Geo Brand Mentions do?

Brand mention and authority scanner for AI visibility. An agent skill from zubair-trabzada/geo-seo-claude. Geo Brand Mentions is an agent skill from zubair-trabzada/geo-seo-claude. Brand mention and authority scanner for AI visibility.

When should I use Geo Brand Mentions?

Geo Brand Mentions fits situations like: tasks that involve AI search optimization.

How do I install Geo Brand Mentions in Claude Code?

Run `npx skills add zubair-trabzada/geo-seo-claude --skill geo-brand-mentions -a claude-code`. Or copy the skill folder (skills/geo-brand-mentions in zubair-trabzada/geo-seo-claude) into .claude/skills/geo-brand-mentions in your project. Claude Code loads it when a task matches its description.

How do I install Geo Brand Mentions in Codex?

Run `npx skills add zubair-trabzada/geo-seo-claude --skill geo-brand-mentions -a codex`. Or copy the skill folder (skills/geo-brand-mentions in zubair-trabzada/geo-seo-claude) into .agents/skills/geo-brand-mentions in your project. Codex loads it when a task matches its description.

Can I use Geo Brand Mentions 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 zubair-trabzada/geo-seo-claude --skill geo-brand-mentions -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/geo-brand-mentions, .gemini/skills/geo-brand-mentions, .github/skills/geo-brand-mentions and .opencode/skills/geo-brand-mentions in your project.

What does Geo Brand Mentions need to run?

Going by SKILL.md and its folder, Geo Brand Mentions needs the command-line tools its instructions call (python3). Its frontmatter pre-approves these tools: Read, Grep, Glob, Bash, WebFetch, Write.

Does Geo Brand Mentions access the network?

SKILL.md names 2 domains. In commands or code: en.wikipedia.org and wikidata.org; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Geo Brand Mentions safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Geo Brand Mentions use?

Geo Brand Mentions is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Geo Brand Mentions use?

About 5.8k tokens (SKILL.md is roughly 23k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Geo Brand Mentions?

Skills that share tags, products or a category with Geo Brand Mentions: AI Search Optimization (social-media-skills/skills, 134 stars), Blog Strategy (AgriciDaniel/claude-blog, 2.3k stars), Blog Strategy (Infrasity-Labs/dev-gtm-claude-skills, 136 stars) and SEO Dataforseo (AgriciDaniel/codex-seo, 799 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Geo Brand Mentions?

zubair-trabzada (a GitHub user) maintains it in zubair-trabzada/geo-seo-claude, which has 10,982 GitHub stars. The repository holds 16 skills in this directory. The repository was last updated on October 10, 2026.

Source: zubair-trabzada/geo-seo-claude on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.