keyword-research

kostja94/marketing-skills · updated May 27, 2026

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$npx skills add https://github.com/kostja94/marketing-skills --skill keyword-research
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summary

Guides keyword research for SEO: finding target keywords, assessing difficulty, understanding search intent, and building topical maps. ~95% of keywords get fewer than 10 searches/month; low-volume, high-intent terms often yield faster rankings and conversion.

skill.md

SEO Content: Keyword Research

Guides keyword research for SEO: finding target keywords, assessing difficulty, understanding search intent, and building topical maps. ~95% of keywords get fewer than 10 searches/month; low-volume, high-intent terms often yield faster rankings and conversion.

When invoking: On first use, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.

Initial Assessment

Check for project context first: If .claude/project-context.md or .cursor/project-context.md exists, read it for product, audience, and positioning.

Identify:

  1. Product/service: What you offer
  2. Audience: Who searches for it
  3. Goals: Traffic, conversions, brand
  4. Tool access: Google Keyword Planner, Google Trends, or SEO tools

Discovery Methods

Base Discovery

Method Purpose
User perspective What pain points? What would they search? Customer language from product context
Tool expansion Related keywords, questions, suggestions; Google autocomplete, PAA, Related Searches
Competitor reverse Analyze competitor titles, H1, URL; identify topics they rank for; find gaps (#4–10 = opportunity) — see competitor-research
Google PAA People Also Ask and Related Searches; high-value signals from real user behavior
Extract from article When auditing existing content: extract seed keywords from title, H1, H2s, meta keywords, first 100 words; then search "[primary keyword]" or "[primary keyword] related keywords" for opportunities; use "[primary keyword]" site:competitor.com if competitors known

Google Autocomplete (Long-Tail Discovery)

Google autocomplete reflects real user searches; suggestions only appear if queries have actual traffic. Free; often uncovers low-volume long-tail that keyword tools miss. ~70% of search traffic is long-tail; lower competition, higher conversion.

Alphabet method (seed + space + letter):

  • Type seed keyword + space + each letter: keyword a, keyword b, ... keyword z
  • Record relevant suggestions; repeat with numbers 0-9
  • Example: SEO a -> "SEO audit," "SEO agency"; SEO b -> "SEO basics," "SEO best practices"

Position variants (seed in different positions):

  • Prefix: a keyword, b keyword (discover what users add before)
  • Suffix: keyword a, keyword b (most common; alphabet method)
  • Middle: how to keyword a, best keyword for (question + modifier combos)

Question modifiers:

  • how to keyword, what is keyword, why keyword, when to keyword, keyword vs
  • keyword for beginners, keyword for small business, keyword without

Why it works: Keyword tools filter low-volume terms; autocomplete only shows queries with real traffic. Use with PAA and Related Searches for full coverage. Categorize results by intent (informational, commercial, transactional).

Incremental Discovery

  • User feedback: Support, community, reviews, NPS—high-frequency questions = unmet search demand
  • Multi-platform search: Reddit, Quora, X (Twitter), Hacker News—real questions and discussions

Search Intent

Intent Content type Example
Informational Blog, guide, FAQ "how to optimize sitemap"
Navigational Brand page "alignify login"
Commercial Comparison, review "SEO tools comparison"
Transactional Product, pricing "best SEO tool pricing"

Intent Identification

Modifier words (often signal intent):

Intent Modifiers
Informational "how," "what," "why," "guide," "tutorial"
Commercial "best," "compare," "vs," "review," "top"
Transactional "buy," "price," "cheap," "coupon," "free shipping"
Local Location names

SERP check: Search the term—knowledge cards/Wiki → informational; product lists/reviews → commercial; brand sites → navigational. Broader terms often show mixed SERP. See serp-features for feature types.

Long-Tail Expansion

  • Google Autocomplete: Alphabet method, position variants, question modifiers; see above. Primary source for long-tail.
  • Intent modifiers: Core + "how," "best," "vs," "compare," "price"
  • Question words: "how to," "what is," "why," "when"
  • Functional modifiers: Core + "-er/-or" (e.g., "image optimizer" for tool-type queries); often higher conversion
  • Clustering: Group by SERP overlap (same top pages), semantic similarity, or intent.

Keyword Clustering & Topical Map

Method Use
SERP overlap Keywords with overlapping top-ranking pages → same cluster
Semantic Group by meaning, LSI, related concepts
Intent-based Group by intent; separate pages if intent differs within cluster

Pillar–cluster (map keywords to structure):

  • Pillar (Hub): Broad topic page; links to clusters
  • Cluster (Spoke): Focused subtopic; links back to pillar
  • Target long-tail first; then pillar. Interlink clusters within topic.
  • See content-strategy for full pillar-cluster planning and implementation.

Evaluate & Screen

Factor Consider
Search volume Monthly searches; ~100+/month typical floor; niche can relax
Keyword difficulty (KD) New sites target lower KD
CPC Higher CPC often = stronger commercial intent
SERP features Featured Snippet, PAA, zero-click; SERP features can satisfy intent without click—affects real traffic; see serp-features (Zero-Click section), featured-snippet
Screening order 1) Remove irrelevant 2) Filter very low volume 3) Assess achievability 4) Prioritize commercial/transactional

Product Positioning Test (SEO Fit)

Test if positioning is clear enough for search:

  • XXX + Function words: Generator, Creator, Maker, Builder, Changer, Shortener, Scraper, Converter, Downloader, Translator, Extender, Summarizer, Resizer, Remover, Extractor, Recorder, Rewriter, Solver, Calculator; or Platform, Tool, Software, App, Provider, Assistant, Copilot
  • Input + to + Output: e.g., "image to video," "text to speech"—clear input/output signals intent

Agent/Copilot products: Pure native Agent hard to grow via SEO; users rarely search "agent." Release related features first (e.g., CRM, sales bot for sales agent) to build traffic, then funnel to Agent product.

Principles

  • Core rule: Someone must search it—validate with tools; avoid inventing terms
  • Functional keywords: Tool-type (-er/-or) often convert better; users are closer to action
  • Multi-language: Re-research in target language; don't translate existing lists. See translation for translation workflow.

SEO–PPC Keyword Synergy

Keyword research serves both SEO and Google Ads. Align both channels to avoid duplication, cannibalization, and wasted spend.

Data flow Use
keyword-research → google-ads Keyword list, clusters, intent; support terms (login, forum, pricing) → negative keywords for PPC
google-ads → keyword-research PPC conversion rate, Search Terms report → SEO priority; high-converting PPC terms = worth ranking organically
keyword-research → landing-page Clusters → dedicated LP per intent; PAA questions → FAQ sections
GSC organic rank 4+ If you rank well organically, consider reducing/pausing PPC on those terms to avoid cannibalization

PPC data for SEO priority: SEO ROI ≈ (Organic clicks × PPC conversion rate × Customer value) − SEO cost. Use PPC conversion data to validate which keywords to pursue in organic.

Reference: Backlinko – SEO and PPC: 8 Smart Ways to Align

Data Sources

Source Use
Ahrefs Keywords Explorer, Site Explorer
SEMrush Keyword Overview, Organic Research
GSC Search queries, impressions, clicks
GA Traffic by landing page
PostHog Feature/search usage

Report Workflow

  1. Parse — Read Excel/CSV, infer keyword, volume, KD, intent, etc. from headers
  2. Enrich — Web search, visit competitor/product pages; read project-context.md if present
  3. Build — Structure data for report
  4. Generate — Output report in chosen format

Output Format

  • Keyword list with volume, KD, intent
  • Keyword mapping to pages/content
  • Content gaps (competitors rank, you don't)
  • Priority ranking for implementation
  • Topical map (cluster → pillar → page mapping)

Report Structure Reference

Section Content
Executive Summary Priorities (top 3)
Keyword Overview Total keywords, primary intent, avg KD, content gaps count
Keyword List Keyword, volume, KD, intent, priority, target page
Keyword Mapping Page/URL, target keywords, status
Content Gaps Keywords competitors rank for that you don't
Action Plan Priority, action, impact, effort
Appendix Search intent reference (Informational, Commercial, Transactional, Navigational)

Related Skills

  • seo-strategy: SEO workflow, Product-Led SEO, audit approach; keyword research is Content phase
  • google-ads: Keywords inform Search targeting; PPC data feeds back into SEO priority
  • paid-ads-strategy: When to use paid vs organic; channel selection
  • content-strategy: Keywords inform content plan; topic clusters
  • content-optimization: Keyword placement, density vs stuffing, H2 keywords
  • title-tag, meta-description: Keywords in title, description
  • heading-structure: Keywords in H1, H2
  • link-building: Keywords inform link targets
  • serp-features: SERP features in keyword screening; PAA, Featured Snippet
  • featured-snippet: Snippet-worthy query targeting
  • competitor-research: Competitor keyword/topic analysis; reverse engineering
  • faq-page-generator: PAA questions to FAQ sections; question-based keyword to FAQ content
how to use keyword-research

How to use keyword-research on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add keyword-research
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/kostja94/marketing-skills --skill keyword-research

The skills CLI fetches keyword-research from GitHub repository kostja94/marketing-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/keyword-research

Reload or restart Cursor to activate keyword-research. Access the skill through slash commands (e.g., /keyword-research) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.650 reviews
  • Shikha Mishra· Dec 28, 2024

    Registry listing for keyword-research matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Noah White· Dec 24, 2024

    I recommend keyword-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Evelyn Patel· Dec 20, 2024

    keyword-research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Meera Dixit· Dec 8, 2024

    keyword-research reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Yash Thakker· Nov 19, 2024

    Solid pick for teams standardizing on skills: keyword-research is focused, and the summary matches what you get after install.

  • Benjamin Rahman· Nov 19, 2024

    keyword-research reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Benjamin Ramirez· Nov 15, 2024

    Useful defaults in keyword-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Dhruvi Jain· Oct 10, 2024

    I recommend keyword-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Evelyn Rao· Oct 10, 2024

    keyword-research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Dev Nasser· Oct 6, 2024

    Registry listing for keyword-research matched our evaluation — installs cleanly and behaves as described in the markdown.

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