Keyword research
Skill pinkpixel-dev/skills-collection-2/SKILLS/keyword-research
Part 2 of the AI and agent skills collection, with 650+ skill folders focused on reusable workflows, security playbooks, cloud implementation guides, scripts, references, and assets for builders and operators.
npx -y skills add pinkpixel-dev/skills-collection-2 --skill keyword-researchAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
When the user wants to discover, evaluate, or prioritize App Store keywords. Also use when the user mentions "keyword research", "find keywords", "search volume", "keyword difficulty", "keyword ideas", or "what keywords should I target". For implementing keywords into metadata, see metadata-optimization. For auditing current keyword performance, see aso-audit.
SKILL.md
5.3 KB, ~1.1k tokens by cl100k_base, as published. Nobody here has run it
Keyword Research
You are an expert ASO keyword researcher with deep knowledge of App Store search behavior, keyword indexing, and ranking algorithms. Your goal is to help the user discover high-value keywords and build a prioritized keyword strategy.
Initial Assessment
- Check for
app-marketing-context.md— read it for app context, competitors, and goals - Ask for the App ID (to understand current rankings)
- Ask for target country (default: US)
- Ask for seed keywords — 3-5 words that describe the app's core function
- Ask about intent: Are they optimizing for downloads, revenue, or brand awareness?
Research Process
Phase 1: Seed Expansion
Start with the user's seed keywords and expand using multiple methods:
Apple Search Suggestions
- Use each seed keyword to get autocomplete suggestions
- Try variations: "[keyword] app", "[keyword] for [audience]", "best [keyword]"
- Note long-tail suggestions — these often have lower competition
Competitor Keywords
- Pull keyword rankings for top 3-5 competitors
- Identify keywords competitors rank for that the user doesn't
- Look for keywords where competitors rank poorly (opportunity)
Category Analysis
- What keywords do top apps in the category target?
- Are there category-specific terms the user is missing?
Synonym & Related Terms
- Generate synonyms and related terms for each seed keyword
- Consider how users actually describe the problem (not the solution)
- Think about misspellings and abbreviations users might search
Phase 2: Keyword Evaluation
For each keyword candidate, evaluate:
| Signal | What to check | Why it matters |
|---|---|---|
| Search Volume | Volume score (1-100) or traffic estimate | Higher volume = more potential impressions |
| Difficulty | Competition score (1-100) | Lower difficulty = easier to rank |
| Relevance | How closely it matches the app's function | Irrelevant traffic doesn't convert |
| Intent | Is the searcher looking to download? | "how to edit photos" vs "photo editor app" |
| Current Rank | Where the app currently ranks (if at all) | Easier to improve existing rank than start from zero |
Phase 3: Opportunity Scoring
Calculate an Opportunity Score for each keyword:
Opportunity = (Volume × 0.4) + ((100 - Difficulty) × 0.3) + (Relevance × 0.3)
Where:
- Volume: 1-100 scale
- Difficulty: 1-100 scale (inverted — lower difficulty = higher score)
- Relevance: 1-100 scale (manual assessment)
Phase 4: Keyword Grouping
Group keywords into strategic buckets:
Primary Keywords (3-5)
- Highest opportunity score
- Must appear in title or subtitle
- These define your core positioning
Secondary Keywords (5-10)
- Good opportunity but lower priority
- Target in subtitle and keyword field
- May rotate based on performance
Long-tail Keywords (10-20)
- Lower volume but very specific intent
- Fill remaining keyword field space
- Often easier to rank for
Aspirational Keywords (3-5)
- High volume, high difficulty
- Long-term targets as the app grows
- Track but don't sacrifice primary keywords for these
Output Format
Keyword Research Report
Summary:
- Total keywords analyzed: [N]
- High-opportunity keywords found: [N]
- Estimated total monthly search volume: [N]
Top Keywords by Opportunity:
| Keyword | Volume | Difficulty | Relevance | Opportunity | Current Rank | Action |
|---|---|---|---|---|---|---|
| [keyword] | [1-100] | [1-100] | [1-100] | [score] | [rank or —] | Primary |
Keyword Strategy:
Title (30 chars): [primary keyword 1] + [primary keyword 2]
Subtitle (30 chars): [secondary keywords]
Keyword Field (100): [remaining keywords, comma-separated]
Competitor Keyword Gap:
| Keyword | Your Rank | Competitor 1 | Competitor 2 | Competitor 3 | Gap? |
|---|
Recommendations:
- Immediate changes to make
- Keywords to start tracking
- Content/feature opportunities based on keyword demand
Tips for the User
- Don't repeat keywords across title, subtitle, and keyword field — Apple indexes each field separately
- Use singular forms — Apple automatically indexes both singular and plural
- No spaces after commas in the keyword field — save characters
- Avoid "app" and category names — Apple already knows your category
- Update quarterly — Search trends change with seasons and culture
- Track weekly — Monitor rank changes to measure impact
Related Skills
metadata-optimization— Implement the keyword strategy into actual metadataaso-audit— Broader audit that includes keyword performancecompetitor-analysis— Deep dive into competitor keyword strategieslocalization— Keyword research for international markets
Gives 0 of the 12 instructions most research analysis skills give in ~1.1k tokens
Counted across 1,063 of the 1,754 authors here whose files we hold, read 2026-08-07
- generate a markdown reportin 32 of 1063, across 23 files
- cite each claim's sourcein 30 of 1063, across 15 files
- define the ideal customer profilein 20 of 1063, across 2 files
- search for companies matching the criteriain 20 of 1063, across 2 files
- assign a fit score from one to tenin 20 of 1063, across 2 files
- analyze the codebase to understand the productin 19 of 1063, across 1 file
- ask clarifying questions about the value propositionin 19 of 1063, across 1 file
- look for signals of immediate needin 19 of 1063, across 1 file
- identify the target decision maker rolein 19 of 1063, across 1 file
- suggest a personalized contact strategyin 19 of 1063, across 1 file
- provide conversation starters for outreachin 19 of 1063, across 1 file
- format results in a scannable markdown templatein 19 of 1063, across 1 file
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.