When planning a content sprint, run keyword discovery, classify intent, cluster topics, and score opportunities to produce. — Claude Skill
Find the queries worth ranking for and rank them by priority.
- Discovery pulls 200 to 500 candidates from seeds, competitors, Search Console, SERPs, and customer language
- Intent classification across informational, navigational, commercial, and transactional before any prioritization
- Clustering by SERP overlap plus topical relevance so one page can target 10 to 30 related keywords
- Opportunity, difficulty, and strategic-fit scoring on a 1 to 5 scale (priority = opportunity + fit minus difficulty)
- CSV-ready sheet plus markdown summary with the top 10 to 20 clusters detailed for the production team
対象ユーザー
SEO specialists move from a flat keyword list to a scored cluster sheet with intent tagged and priority ranked; the next 10 briefs come straight from the top of the list.
この役職のスキルを見るGrowth marketers get intent and commercial-fit scoring per cluster so the content calendar attracts buyers, not just browsers.
この役職のスキルを見る機能
SEO specialist runs discovery on 5 competitors plus 12 seeds; produces 28 clusters scored on priority; the top 20 fill the next 5 months of the calendar.
Pulls 600 candidates including Search Console page-2 queries; finds 14 quick-win clusters already ranking on page 2 that need on-page work, not new content.
Identifies 47 clusters where all 3 competitors rank but the site does not; ranks by strategic fit; commits 15 to production this quarter.
Tags every existing piece by query intent; flags 22 pieces where intent and content type do not match (informational article targeting a commercial query).
仕組み
Run discovery across seeds, competitor exports, Search Console queries, SERP-related searches, and customer language to gather 200 to 500 candidates
Deduplicate and clean the candidate list, then classify each keyword by informational, navigational, commercial, or transactional intent
Cluster keywords into 20 to 50 topical groups by SERP overlap plus topical relevance (one cluster equals one target page)
Score each cluster on opportunity, difficulty, and strategic fit (1 to 5 each); compute priority and rank
Hand over a CSV per keyword plus a markdown summary with the top 10 to 20 clusters detailed for the production team
例
Site: rampstack.co blog. Audience: engineering managers at 50 to 500-person companies. Topic area: async engineering practices. Competitors: 3 named blogs. Tool: Ahrefs. Existing site: 40 articles, Search Console connected.
Primary: async standup format (1,400/mo, commercial). 12 secondary keywords. Opportunity 5, Difficulty 3, Fit 5. Priority 7.
Primary: engineering decision log (820/mo, informational). 8 secondary keywords. Opportunity 4, Difficulty 2, Fit 5. Priority 7.
9 queries ranking position 11 to 20 where existing pages need on-page work, not new content. Estimated 4,200 extra clicks per month.
Informational 62%, commercial 24%, navigational 9%, transactional 5%. Content mix should mirror this.
12 candidates dropped: featured snippet plus AI overview saturates the SERP, organic CTR below 8%.
改善される指標
対応ツール
Surface existing page-1 and page-2 queries for quick-win identification and intent calibration.
Host the keyword sheet with one row per keyword and one row per cluster for collaborative scoring.
Pull volume, difficulty, and competitor keyword exports for the discovery and difficulty-scoring steps.
Document the markdown research summary with top clusters and hand it to the content team.
Cross-check Ahrefs data and pull additional competitor keywords for the discovery step.
SEO Keywordを使ってみますか？
始め方を選択してください。
このスキルをコンピュータにローカルでインストールして実行します。
コンピュータでターミナルを開き、このコマンドを貼り付けます：
このコマンドでスキルとすべてのファイルをコンピュータにダウンロードします：
末尾に-gを追加すると、すべてのプロジェクトで利用可能になります。
Claude Codeを起動し、コマンドを入力します：
Keyword Research
Find the queries worth ranking for, classify them by intent, cluster them into topics, and prioritize what to produce. Stack-agnostic. Tool-agnostic (works with any keyword tool).
When to use
- Starting a new site or content section
- Planning a content calendar
- Looking for ranking opportunities on an existing site
- Understanding search intent before writing
- Building topic clusters for internal linking
- Identifying content gaps vs competitors
When NOT to use
- Optimizing a single page where the target query is already known (use
seo-onpage) - Comparing your site to a competitor across many dimensions (use
seo-competitor) - Auditing existing content for performance (use
seo-content-audit)
Required inputs
- The site or topic area
- The target audience and what they need
- A keyword tool (Ahrefs, Semrush, Moz, Google Keyword Planner, or similar) OR access to search console for an existing site
- Optional: 3 to 5 known competitors to seed the research
If no tool is available, the skill still works using SERP inspection and search console data alone, but the volume estimates will be rough.
The framework: 4 stages
Stage 1: Discover
Cast a wide net. Sources:
- Seed terms from the brief or the user's vocabulary
- Competitor keywords (any keyword tool will export these)
- Search console queries for an existing site (find the page-1 and page-2 queries)
- Related searches and "People also ask" in actual SERPs
- Customer language (support tickets, sales calls, reviews)
- Forum and community language (Reddit, niche forums, Stack Overflow)
Goal: 200 to 500 candidate keywords for a typical content sprint. More if planning a year of content.
Stage 2: Classify by intent
Every keyword maps to one of four intents. Get this right or the rest is noise.
| Intent | Signal | Page type that wins |
|---|---|---|
| Informational | "how to," "what is," "why," "best way to" | Article, guide, tutorial |
| Navigational | brand or product name + modifier | Brand homepage, product page |
| Commercial | "best," "review," "vs," "comparison," "alternatives" | Listicle, comparison, review |
| Transactional | "buy," "price," "deal," "near me," "for sale" | Product page, category page |
A keyword tool's volume tells you the demand. The SERP tells you the intent. When in doubt, look at what's actually ranking. If page 1 is articles, the query is informational. If page 1 is product pages, it's transactional.
Hybrid intents exist. "Best running shoes" is commercial-investigational. "Best running shoes under $100" is the same intent narrowed by a budget filter. Treat hybrids as their dominant intent and note the modifier.
Stage 3: Cluster
Group keywords that should target the same page (or topic cluster).
Two clustering approaches:
Approach A: SERP overlap. If two keywords share at least 3 of the top 10 results, they target the same page. This is mechanical and reliable.
Approach B: Topical relevance. Group keywords by the underlying topic, not just word overlap. "How to start a podcast" and "podcast equipment for beginners" are the same topic, different facets.
Use both. A typical cluster has:
- 1 primary keyword (highest volume, broadest intent)
- 5 to 15 secondary keywords (variations and long-tails)
- 1 page that targets them all
Stage 4: Prioritize
For each cluster, score on three dimensions:
Opportunity (1 to 5):
- Volume (raw search demand)
- Click potential (some queries answer themselves in the SERP, lowering CTR)
- Conversion potential (does this query attract buyers or browsers?)
Difficulty (1 to 5):
- Domain authority of top results
- Backlink count of top results
- Content depth and freshness of top results
- Whether the SERP has features (featured snippets, AI overview, video carousel) that compete with organic
Strategic fit (1 to 5):
- Does it serve our audience?
- Does it support our positioning?
- Does it link to commercial pages naturally?
Priority score = Opportunity + Strategic fit - Difficulty.
Rank the clusters. Top 20 percent get produced first.
Workflow
- Define the scope. What site, what topic area, what audience.
- Run discovery. Pull seeds, competitor exports, search console data, SERP inspections. Aim for 200 to 500 candidates.
- Deduplicate and clean. Remove obvious junk, brand misspellings, irrelevant terms.
- Classify by intent. Mark each keyword.
- Cluster. Group into topical clusters. Aim for 20 to 50 clusters.
- Score each cluster on opportunity, difficulty, and strategic fit.
- Prioritize. Rank by composite score. Identify the top 10 to 20 clusters to produce first.
- Output. Use the template in
references/keyword-research-template.md.
Failure patterns
- Chasing volume without intent. A 10,000-volume informational keyword does not drive purchases. Match query to commercial outcome.
- Targeting impossibly competitive keywords. New sites cannot rank for "credit cards." Find the underserved long-tail variant.
- Ignoring search console. Existing sites already rank for queries they did not target. These are the easiest wins.
- Treating clusters as one-keyword-per-page. A page can target 10 to 30 related keywords. One-keyword-per-page leads to thin, cannibalized content.
- Ignoring SERP features. A query with a featured snippet, AI overview, and a video carousel above the organic results may not be worth pursuing.
- Static keyword research. Search demand shifts. Refresh the research at least annually for evergreen sites, quarterly for fast-moving topics.
Output format
Default output: a spreadsheet (CSV or sheet) with one row per keyword and one row per cluster, plus a markdown summary with the top 10 to 20 clusters detailed.
Recommended columns for the keyword sheet:
| Column | Source |
|---|---|
| Keyword | Discovery |
| Volume | Tool |
| Difficulty | Tool |
| Intent | Manual classification |
| SERP features | Manual or tool |
| Cluster | Stage 3 |
| Cluster role (primary/secondary) | Stage 3 |
| Opportunity score | Stage 4 |
| Strategic fit | Stage 4 |
| Priority | Composite |
| Notes | Free text |
Reference files
references/keyword-research-template.md- Spreadsheet column definitions and a markdown summary template.references/intent-classification-guide.md- Detailed examples of each of the four intent categories.