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日本語の概要は準備中です。原文の説明を表示しています。
Strategic keyword research powered by web search and brand context. Use this skill whenever the user mentions keywords, keyword research, SEO topics, content topics, 'what should I write about', content strategy, blog ideas, search traffic, or content planning. Also trigger when the user wants to plan what content to create, when existing content isn't attracting search traffic, when any skill needs keyword-plan.md but it doesn't exist, or when the user asks about SEO in the context of content creation. Even casual mentions like 'I need blog post ideas' or 'what topics should I cover' warrant this skill. 8-phase process from seed to content brief.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
brand/ directory exists in the project root.voice-profile.md, positioning.md, audience.md, creative-kit.md, stack.md, learnings.md.brand/ does not exist, proceed without it — this skill works standalone.Note: Examples below use fictional brands (Acme, Lumi, Helm). Replace with your own brand context.
Prefer measured KD/volume/CPC/intent/SERP/GSC via openseo-keyword-research when OpenSEO is configured; otherwise Exa-stack validation with metrics marked unknown. Full contract: skills/openseo/references/backend-contract.md.
Most keyword research is backwards. People start with tools, get overwhelmed by data, and end up with a spreadsheet they never use.
This skill starts with strategy. What does your business need? Who are you trying to reach? What would make them find you? Then it validates with live search data and builds a content plan that actually makes sense.
No expensive tools required. Systematic thinking plus web search.
Reads: positioning.md, audience.md, competitors.md, learnings.md
Writes: brand/keyword-plan.md, campaigns/content-plan/*.md
Before starting, check whether ./brand/keyword-plan.md already exists.
Do not start from scratch. Instead:
Read the existing plan.
Present a summary of the current keyword strategy:
EXISTING KEYWORD PLAN (last updated {date})
Pillars: {Pillar 1} {N} clusters {priority} · {Pillar 2} {N} {priority} · {Pillar 3} {N} {priority}
Top keywords: {kw1} {priority} · {kw2} {priority} · {kw3} {priority}
Content briefs: {N} created, {N} published
What would you like to do?
① Refresh with new SERP data ② Add a new topic area ③ Re-prioritize clusters
④ Full rebuild from scratch ⑤ Generate briefs for top keywords
Process the user's choice:
Before overwriting, show a compact diff of what changed (new clusters, priority changes, removals) and ask: "Save these changes? (y/n)"
Only overwrite after explicit confirmation.
Proceed to the full process below.
Transform a business context into a prioritized content plan with:
Output format: Clustered keywords mapped to content pieces, prioritized by business value, competitive opportunity, and search demand. Saved to disk as a keyword plan and individual content briefs.
SEED --> EXPAND --> SEARCH --> CLUSTER --> VALIDATE --> PRIORITIZE --> MAP --> BRIEF
Get these inputs before generating anything. If brand memory files exist, pre-fill what you can and confirm with the user.
If brand memory supplies 3+ of these, present what you found and ask for confirmation rather than re-asking:
From your brand profile:
├── Offer "{from positioning.md}"
├── Audience "{from audience.md}"
├── Competitors {list from competitors.md}
└── Positioning "{angle from positioning.md}"
Does this still look right? And two more
questions:
1. What is the goal -- traffic, leads, sales,
or authority?
2. Timeline -- quick wins or long-term plays?
From the business context (and brand memory if loaded), generate 20-30 seed keywords covering:
Direct terms -- What you actually sell
"AI marketing automation", "fractional CMO", "marketing workflows"
Problem terms -- What pain you solve
"can't keep up with content", "marketing team too small", "don't understand AI"
Outcome terms -- What results you deliver
"faster campaign execution", "10x content production", "marketing ROI"
Category terms -- Broader industry terms
"marketing automation", "AI marketing", "growth marketing"
Brand-aligned terms -- From positioning if loaded
If positioning is "The Anti-Agency" → seed "agency alternatives", "in-house marketing", "DIY marketing strategy" If positioning is "AI-First Marketing" → seed "AI marketing tools", "automated campaigns", "machine learning marketing"
See references/keyword-examples.md for detailed expansion techniques.
Data-backed research layer for each pillar keyword + top 30-50 expansions. Canonical stack (mktg-native, no Ahrefs): Exa MCP (web_search_advanced_exa, deep_search_exa, company_research_exa) + Firecrawl (autocomplete + SERP scrape) + /last30days (Reddit/X/HN aggregation) + gh CLI (OSS GitHub-stars). For Ahrefs precision see appendix in seo-machine/references/exa-recipes.md.
Proceed with Phase 1 (seed generation) and Phase 2 (6 Circles expansion) using brand context only. Skip Phase 3 entirely but note the limitation to the user: 'Keyword clusters are based on strategic assessment, not live SERP data. Validate against actual search results before committing to content production.' Cluster and prioritize based on brand alignment and audience pain points rather than search volume.
For each seed and pillar keyword, search for autocomplete suggestions:
Search: "[keyword] a", "[keyword] b", ... "[keyword] z"
Search: "how to [keyword]"
Search: "best [keyword]"
Search: "why [keyword]"
Search: "[keyword] vs"
Search: "[keyword] for"
Capture every unique suggestion. These are real queries people type.
What to look for:
For each pillar keyword, search Google and capture the People Also Ask boxes:
Search: "[pillar keyword]"
→ Capture PAA questions
→ Click/expand each PAA to get follow-up PAAs
→ Capture the second-level questions too
What PAA data reveals:
How to use PAA data:
For each priority keyword, examine the top search results:
Search: "[keyword]"
→ Analyze the top 5-10 results
→ Note: content type, word count, freshness, domain authority
Capture for each result:
SERP signals: Old results (2+ years) = freshness opportunity. Thin results (<1000 words) = depth opportunity. All big brands (DR 80+) = hard to win, try long-tail. Mixed big + small sites = winnable. Forums/Reddit in top 5 = huge content gap. Featured snippet = optimize for snippet format.
If ./brand/competitors.md is loaded (or competitors were provided), search for what they rank for:
Search: "site:{competitor-domain.com} [topic]"
Search: "{competitor name} [pillar keyword]"
Search: "{competitor name} blog"
Build a competitor content map for each competitor: topics covered, content types, keywords targeted, gaps, and quality. Identify three priority gap types: (1) catch-up — topics all competitors cover but you don't, (2) blue ocean — topics nobody covers well, (3) improvement — topics where competitors are weak/outdated.
After web search, present a summary before clustering: total terms found, top 3 discoveries (unexpected keywords, competitor gaps, PAA insights), and new keywords added from search. Show counts for autocomplete suggestions, PAA questions, SERPs analyzed, and competitor pages reviewed.
Group expanded keywords (including web search discoveries) into content pillars using the hub-and-spoke model:
[PILLAR]
Main Topic Area
|
+-------------+-------------+
| | |
[CLUSTER 1] [CLUSTER 2] [CLUSTER 3]
Subtopic Subtopic Subtopic
| | |
Keywords Keywords Keywords
A pillar is a major topic area that could support:
Ask: "Could this be a complete guide that thoroughly covers the topic?"
See references/keyword-examples.md for example cluster with search data.
See references/keyword-examples.md for validation criteria.
Validate: search volume, competition, content-market fit, business alignment.
Not all keywords are equal. Score each cluster using both strategic assessment AND live search evidence from Phase 3.
Score each cluster on three dimensions:
Priority: High Value + High Opportunity + Fast = DO FIRST. High + High + Medium = DO SECOND. Medium + High + Fast = QUICK WIN. High + Low = LONG PLAY. Low value = BACKLOG.
Apply DR-cap (KD > DR + buffer → out_of_reach → BACKLOG) and tag every keyword with a 3-tier confidence label (high/medium/estimated). Table + output schema: references/dr-cap-and-confidence.md.
For each priority cluster, assign a content type (Pillar Guide, How-To, Comparison, Listicle, Use Case, or Definition), match to search intent (Informational, Commercial, Transactional), and place in the content calendar (Tier 1-4 over 12 weeks).
Use PAA questions to build article outlines — each PAA question becomes an H2, aligning content structure with what Google knows people are asking.
See references/keyword-examples.md for content type tables, intent matching rules, calendar tiers, and PAA-driven outline examples.
See references/keyword-examples.md for brief template and keyword plan format.
Generate briefs for top-priority keywords: target keyword, search intent, content type, SERP snapshot, PAA questions, outline, angle.
See references/keyword-examples.md for the full terminal output template and keyword plan file format. Use the premium formatting system for terminal output (box-drawing characters, section headers) and standard markdown for files saved to disk.
After presenting the keyword plan, actively offer to chain into content creation for the top-priority keyword:
──────────────────────────────────────────────
READY TO WRITE?
Your top-priority keyword is "{keyword}" with
a content brief ready at
./campaigns/content-plan/{slug}.md
I can write this article now using /seo-content.
It will use your brand voice, the content brief,
and the SERP data I just gathered.
→ "Write it" to start /seo-content now
→ "Not yet" to save the plan and stop here
──────────────────────────────────────────────
If the user says "write it" or similar, hand off to /seo-content with:
See references/keyword-examples.md for a complete worked example.
Cluster: 'verified meal delivery'
| Keyword | Volume | Difficulty | Intent | Priority |
|---|---|---|---|---|
| verified meal delivery near me | 12,100 | 45 | Transactional | P1 |
| verified-source restaurants that deliver | 3,600 | 38 | Transactional | P1 |
| is doordash organic | 2,900 | 22 | Informational | P2 |
| verified meal prep delivery | 1,300 | 31 | Transactional | P2 |
| verifiedeats delivery | 880 | 15 | Navigational | P3 |
Content brief: Target 'verified meal delivery near me' with a city-specific landing page template (programmatic SEO). Target 'is doordash organic' with a comparison article linking to Acme.
This skill provides strategic direction backed by search data, not:
The output is a validated, prioritized plan with content briefs. Execution is handled by /seo-content and other downstream skills.
See references/keyword-examples.md for free tools.
keyword-research creates the content strategy. Then: /seo-content writes articles from briefs, /positioning-angles finds the angle for each piece, /direct-response-copy handles commercial-intent landing pages, /content-atomizer repurposes pillar content, and /lead-magnet creates assets for top-of-funnel keywords.
A good keyword research output is data-backed (SERP evidence, not intuition), actionable (clear "start here" with a brief ready), prioritized (ranked by opportunity), realistic (acknowledges competition), strategic (connects to business goals), specific (content types and outlines, not just keywords), and executable (briefs ready for /seo-content). If the output is "here's 500 keywords, good luck" — it failed.
| Level | Context Available | Output Quality |
|---|---|---|
| L0 | Product name only, no web search | Basic seed keywords, strategic clusters, estimated priorities |
| L1 | + brand files (positioning, audience) | Brand-aligned keywords, audience-informed clusters |
| L2 | + competitors.md | Competitor gap analysis, differentiated content strategy |
| L3 | + web search available | Live SERP data, validated priorities, PAA-driven outlines |
| L4 | + existing keyword-plan.md (refresh) | Evolved plan with trend tracking, updated priorities |
After delivering the keyword plan, append learnings to brand/learnings.md when the user provides feedback on what worked or what they changed.
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概要と使いどころ
Add a new expert to the Remotion experts page
日本語の概要は準備中です。原文の説明を表示しています。
Add a new sound effect to @remotion/sfx
日本語の概要は準備中です。原文の説明を表示しています。
Use when setting up a secure email inbox for any AI agent — configuring inbound email via Resend, webhooks, tunneling for local development, and implementing security measures to prevent prompt injection attacks. Also use when someone mentions 'agent email', 'bot inbox', 'receive emails for agent', 'agent webhook', 'email security for AI', 'prompt injection via email', 'inbound email for bot', or wants their AI agent to receive and respond to emails securely.
日本語の概要は準備中です。原文の説明を表示しています。
Builds, measures, and improves an Answer Engine Optimization program across ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews, and Copilot. Use for AEO, GEO, LLMO, AI search visibility, AI citations, brand mentions, answer-engine monitoring, llms.txt, AI crawler audits, or questions like 'what does AI say about us?' and 'why are competitors cited instead of us?'. Produces a durable evidence report and prioritized gameplan, not a one-shot content checklist.
日本語の概要は準備中です。原文の説明を表示しています。
Generate Apple App Store screenshot pages as a Next.js app with html-to-image export at required resolutions. Screenshots are advertisements, not documentation — every screenshot sells one idea. Use when building App Store screenshots, generating exportable marketing screenshots for iOS apps, or creating programmatic screenshot generators. Triggers on 'app store screenshots', 'App Store', 'screenshot generator', 'iOS screenshots', 'marketing screenshots', 'phone mockup', 'ASO screenshots', 'app store assets', 'app listing', 'app store page', or 'app preview images'. Also use when someone is about to submit an app and needs store assets, or when they say 'make my app store page look good'. Includes iPhone and iPad mockup components with pre-measured dimensions.
日本語の概要は準備中です。原文の説明を表示しています。
Build detailed buyer personas and audience profiles from research. Use this skill whenever the user mentions audience, buyer persona, ideal customer, target market, ICP, watering holes, 'who am I selling to', customer research, or audience profile. Also use when content feels unfocused or generic (that's an audience problem), when conversion is low because messaging doesn't resonate, when starting any new project (audience should be first), or when any downstream skill needs audience.md but it doesn't exist yet. Even if the user doesn't explicitly ask for audience research, trigger this if they're writing copy or building a landing page without a clear audience defined. Three approaches: Quick Profile, Persona Build, Community Mining.
日本語の概要は準備中です。原文の説明を表示しています。