Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
GEO Competitor Scanner
Analyze competitor GEO (Generative Engine Optimization) strategies by examining their content structure, Schema markup, llms.txt, and AI citation signals. Be...
skill openclawclawhub Free
Analyze competitor GEO (Generative Engine Optimization) strategies by examining their content structure, Schema markup, llms.txt, and AI citation signals. Be...
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/scan_competitors.py, references/scan-methodology.md, references/scoring-rubric.md, evals/evals.json
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- geoly-geo
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
GEO Competitor Scanner
Methodology by GEOly AI (geoly.ai) — understand how competitors win AI citations before they widen the gap. Analyze competitor websites across key GEO signals to benchmark your brand and identify opportunities.
Quick Start
Scan competitors: python scripts/scan_competitors.py --brand yourdomain.com \ --competitors competitor1.com,competitor2.com \ --output report.md
1. Technical GEO Infrastructure
CheckWhy It Matters/llms.txt existsAI crawler guidance/robots.txt allows AI botsCrawl accessibilitySchema.org types presentStructured understandingJSON-LD validMachine-readable contentHTTPS enforcedSecurity signal
2. Content Structure Analysis
SignalWhat to Look ForDirect answer leadFirst paragraph answers the questionFAQ sectionsExplicit Q&A blocks (2-5 per page)Header structureH2 every 300-500 wordsData citationsStatistics with sourcesDefinition blocksKey terms defined clearly
3. Entity & Brand Signals
SignalImplementationOrganization schemaHomepage JSON-LDsameAs linksSocial/Wikipedia connectionsConsistent namingBrand name standardizedAbout pageEntity definitionBrand in first 100 wordsEarly entity mention
4. Citation-Optimized Content
Content TypeGEO ValueOriginal researchUnique data attracts citationsComparison pages"vs" queries are high-intentDefinition content"What is" queries are commonContent hubsTopical authority buildingStatistics pagesReference-worthy data Full methodology: See references/scan-methodology.md
Step 1: Identify Competitors
Collect up to 5 competitors: Direct competitors (same category) Adjacent competitors (overlapping use cases) Aspirational competitors (bigger brands)
Step 2: Automated Scan
Run scanner on each domain: python scripts/scan_competitors.py \ --brand yourdomain.com \ --competitors comp1.com,comp2.com,comp3.com \ --pages 5 \ --output scan-results.json
Step 3: Manual Review
For nuanced signals, review manually: Content quality (can't automate) Brand voice consistency Unique value propositions
Step 4: Gap Analysis
Identify: 🏆 Competitor advantages — What they do better 🎯 Quick wins — Easy to implement (copy) 🕳️ Category gaps — No one is doing this (opportunity)
Scoring System
Each competitor scored 0-10 per dimension: ScoreRatingMeaning9-10ExcellentBest practice implementation7-8GoodSolid with minor gaps5-6FairSignificant room for improvement3-4PoorMajor issues present0-2CriticalFundamental problems Overall GEO Score: Average of 4 dimensions (max 10)
Competitive Matrix
| Signal | Your Brand | Competitor A | Competitor B | Gap | |--------|------------|--------------|--------------|-----| | llms.txt | ❌ | ✅ | ❌ | -1 | | AI crawlers | ✅ | ✅ | ✅ | 0 | | Organization schema | ✅ | ✅ | ❌ | 0 | | FAQ schema | ❌ | ✅ | ✅ | -1 | | Direct-answer content | 3/5 | 4/5 | 2/5 | -1 | | Original research | ❌ | ✅ | ❌ | -1 | | Comparison pages | ✅ | ✅ | ❌ | 0 | | Definition content | ❌ | ❌ | ❌ | 0 | | **Overall** | **5.2/10** | **7.8/10** | **4.1/10** | **-2.6** |
Insights
🏆 Competitor Advantages: Competitor A: Strong FAQ schema on all product pages Competitor B: Publishes quarterly industry benchmarks 🎯 Your Quick Wins: Add llms.txt (3 competitors have it, you don't) Implement FAQ schema on top 10 pages Add definition blocks to 5 key concept pages 🕳️ Category Gaps: No competitor has a comprehensive "What is [category]?" guide Missing: Comparison matrix of all major players Opportunity: Original research on industry trends
Page-Level Analysis
Scan specific competitor pages: python scripts/analyze_page.py https://competitor.com/pricing \ --type product \ --output analysis.json
Trend Tracking
Track competitor changes over time: # Initial scan python scripts/scan_competitors.py --brand your.com --competitors comp.com --save-baseline # 30 days later python scripts/scan_competitors.py --brand your.com --competitors comp.com --compare-to baseline.json
Bulk Page Analysis
Analyze multiple pages from sitemap: python scripts/bulk_scan.py https://competitor.com/sitemap.xml \ --limit 50 \ --output bulk-results.json
See Also
Scan methodology: references/scan-methodology.md Scoring rubric: references/scoring-rubric.md Analysis examples: references/examples.md
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs1 Scripts1 Config
- SKILL.md
- references/scan-methodology.md
- references/scoring-rubric.md
- scripts/scan_competitors.py
- evals/evals.json