An AI skill is a structured system prompt or set of custom instructions that gives AI assistants like ChatGPT, Claude, and Gemini specialized capabilities. It includes a clear role definition, specific knowledge areas, step-by-step processes, and quality criteria that help AI perform specific professional tasks.

How does Skill This work?

Simply describe your professional expertise in plain text. Our free AI skill generator analyzes your input and transforms it into a structured system prompt. The skill is then graded on clarity, specificity, and usefulness. You can share your skill via a unique URL or copy it to use as custom instructions with any AI assistant like ChatGPT, Claude, or Gemini.

Is Skill This free to use?

Yes, Skill This is completely free. No account or payment is required. You can create unlimited skills and share them with anyone.

How is my skill graded?

Skills are graded on a scale from A+ to F (0-100 points). The grade reflects how clear, specific, and actionable your skill is. Higher grades indicate skills that AI assistants can use more effectively.

How do I use my skill with an AI assistant?

Copy your generated skill and paste it into the system prompt or custom instructions of any AI assistant. For ChatGPT, use Personalization > Custom Instructions. For Claude, add it to a Project's instructions. For Cursor or other IDEs, save it as a .cursorrules or CLAUDE.md file. The AI will then adopt that skill and perform tasks according to your expertise.

AI Skill Report Card

Search Intent Ecosystem Modeling

B+75·Mar 1, 2026·Source: Web

Quick Start15 / 15

YAML
INPUT_TEMPLATE:
  business_context: "SaaS project management tool"
  primary_keywords: ["project management", "team collaboration"]
  target_languages: ["en", "es"]
  funnel_focus: "full" # or "TOFU", "MOFU", "BOFU"

OUTPUT:
  intent_ecosystem_map:
    - intent_cluster: "Planning & Setup"
      stage: "TOFU"
      ai_evolution: "How to choose project management methodology"
      content_gap: "Interactive methodology selector"
    - intent_cluster: "Tool Comparison"
      stage: "MOFU" 
      ai_evolution: "Best project management software for [specific use case]"
      content_gap: "Comparative feature matrix with ROI calculator"

Recommendation▾

Trim verbose sections like 'Best Practices' and 'Common Pitfalls' - Claude understands SEO fundamentals and doesn't need detailed explanations of basic concepts

Workflow15 / 15

Phase 0: Context Gathering

Business Intelligence Checklist:
- [ ] Industry vertical and business model
- [ ] Target audience personas (B2B/B2C/hybrid)
- [ ] Current content asset inventory
- [ ] Competitor analysis scope
- [ ] Geographic and language markets
- [ ] Sales funnel complexity level
- [ ] AI search behavior penetration in industry

Phase 1: Intent Discovery & Clustering

Extract seed keyword universe (minimum 100 terms)
Apply semantic clustering using search volume + SERP feature analysis
Map traditional intent types (informational/navigational/transactional/commercial)
Identify AI-rewritten query patterns from "People Also Ask" and autocomplete
Score intent clusters by search volume, competition, and conversion potential

Progress Tracking:

Seed keyword extraction (target: 100-500 terms)
Semantic clustering complete (target: 15-25 clusters)
Traditional intent mapping finished
AI evolution patterns identified
Intent scoring matrix populated

Phase 2: Funnel Stage Assignment

TOFU: Problem awareness, educational, "how to" patterns
MOFU: Solution research, comparison, "best for" patterns
BOFU: Purchase decision, vendor evaluation, "vs" and "pricing" patterns
Apply stage probability scoring (0-100%) for multi-stage intents

Phase 3: AI-Era Intent Evolution Analysis

Identify conversational query transformations
Map long-tail question patterns from AI interfaces
Analyze featured snippet and AI overview content gaps
Document voice search and mobile-first query variations

Phase 4: Content Strategy Matrix Generation

Match each intent cluster to optimal content format
Assign priority scores using ICE framework (Impact/Confidence/Ease)
Generate content gap analysis with competitive opportunities
Design conversion pathway mapping between intent stages

Recommendation▾

Consolidate the workflow phases - the current 4-phase breakdown with sub-checklists is overly complex for what is essentially: discover keywords → cluster by intent → map to funnel → identify AI patterns

Examples15 / 20

Example 1: B2B SaaS Input: "CRM software for small business" Output:

Intent Ecosystem Map:
├── TOFU (5 clusters)
│   ├── CRM basics education → Blog series + calculator
│   ├── Industry-specific needs → Vertical landing pages
│   └── Cost vs benefit analysis → ROI comparison tool
├── MOFU (7 clusters) 
│   ├── Feature comparisons → Interactive feature matrix
│   ├── Integration capabilities → Technical resource center
│   └── Implementation planning → Migration guide series
└── BOFU (4 clusters)
    ├── Pricing comparison → Transparent pricing page
    ├── Free trial decision → Risk-free trial landing page
    └── Vendor trust signals → Case studies + testimonials

Example 2: E-commerce Input: "Sustainable fashion brand" Output:

AI Evolution Patterns Identified:
- Traditional: "sustainable clothing brands"
- AI-era: "Is [brand] actually sustainable and ethical"
- Voice: "Show me eco-friendly alternatives to fast fashion"
- Visual: "Sustainable outfit ideas for work"

Content Strategy Matrix:
- Intent: Sustainability verification → Content: Third-party certification badges + transparency reports
- Intent: Style guidance → Content: AI-powered outfit generator + seasonal lookbooks
- Intent: Price justification → Content: Cost-per-wear calculator + material origin stories

Recommendation▾

Replace abstract framework names (ICE framework, TOFU/MOFU/BOFU) with concrete examples showing actual input queries and their intent classifications with confidence scores

Best Practices

Intent Classification Accuracy

Use minimum 3 classification methods: keyword analysis, SERP analysis, user journey mapping
Validate classifications with actual search console performance data
Account for seasonal and trending topic fluctuations

AI-Era Adaptations

Monitor "People Also Ask" expansion patterns monthly
Track featured snippet content performance and optimization opportunities
Analyze conversational query patterns from voice search data
Map AI overview content gaps where traditional results fall short

Multi-language Considerations

Adjust intent intensity by cultural context (direct vs indirect cultures)
Account for search behavior differences across markets
Validate intent evolution patterns per language/region
Consider local competitor landscape in intent prioritization

Common Pitfalls

Over-segmentation Risk

Avoid creating >30 intent clusters (diminishing returns on content ROI)
Don't split high-volume intents into micro-segments
Resist perfectionist tendency to map every possible variation

AI Pattern Misinterpretation

Don't assume all long-tail queries are AI-generated
Avoid over-optimizing for conversational queries at expense of traditional search
Don't ignore mobile vs desktop intent behavior differences

Funnel Stage Misalignment

Don't force single-stage classification on multi-stage intents
Avoid assuming linear progression through TOFU→MOFU→BOFU
Don't neglect re-engagement intents from existing customers

Validation Framework

Coverage Completeness Check:

TOFU coverage: Minimum 5 intent clusters with educational angle
MOFU coverage: Minimum 5 comparison/evaluation intent clusters
BOFU coverage: Minimum 3 decision/conversion intent clusters
AI evolution identification: 40%+ of clusters show conversational query variants

Strategic Alignment Validation:

Business goal mapping: Each cluster connects to specific KPI
Content resource feasibility: Production capacity vs intent priority
Competitive opportunity scoring: Blue ocean vs red ocean classification
ROI projection: Traffic potential × conversion rate × customer value estimation

Output Quality Gates:

Intent ecosystem map completeness (all funnel stages covered)
Content strategy matrix actionability (specific format + channel recommendations)
Priority execution roadmap with quarterly milestones
Measurement framework with leading/lagging indicator pairs