aaron-he-zhu/aaron-marketing-skills

keyword-research

Use when the user asks to "find keywords", "挖词", or "搜什么词"; prioritizes search volume, keyword difficulty, intent, and topic clusters from provided or connected data.

소스 보기
원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

Keyword Research

Discovers, scores, and clusters keywords for SEO and GEO planning.

Quick Start

Research keywords for [topic/product/service]
What keywords is [competitor URL] ranking for that I should target?

Skill Contract

Expected output: a prioritized keyword brief plus the standard handoff summary for memory/research/.

  • Reads: topic or seed keyword, target market/language, business goal, site DR, and any user-provided or tool metrics.
  • Writes: a user-facing research deliverable and reusable summary.
  • Promotes: durable keyword priorities, competitor facts, and pending strategy decisions to memory/hot-cache.md, memory/open-loops.md, and memory/research/.
  • Done when: every shortlisted keyword carries volume + difficulty + intent, each with source ref, observation time/window, market/language, and evidence label; an applicable missing value is Unknown with its gap reason, never N/A; keywords are grouped into pillar + cluster hubs; and the deliverable names at least 3 prioritized Quick Win / Growth / GEO opportunities.
  • Primary next skill: competitor-analysis when the keyword set is ready for market comparison.

Handoff Summary

Emit the standard shape from skill-contract.md §Handoff Summary Format.

Data Sources

Optional integrations: ~~SEO tool, ~~search console. Without tools, ask for seed keywords, audience, goals, and any known metrics. See CONNECTORS.md.

Zero-dependency local helper (no tool needed): python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/suggest.py" "<seed>" --expand harvests free keyword ideas from Google Autocomplete (⚠️ unofficial endpoint). Search volume / difficulty still needs ~~SEO tool or own Search Console data. See scripts/connectors/README.md.

Keyless live-SERP sampling: python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/firecrawl.py" search "<candidate keyword>" --limit 10 (Firecrawl keyless free tier, ~1,000 credits/mo, no key needed) shows who actually ranks for a candidate — feed the top-10 domains and formats into the intent check and the difficulty read as Measured evidence instead of guessing. Volume still needs ~~SEO tool or GSC.

Keyless topic-demand proxy: python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/pageviews.py" "<Topic_Article>" --months 12 returns a topic's real Wikipedia-attention series — Measured direction and seasonality evidence when no volume tool is connected. It is attention, not search volume: use it to rank topics against each other and time them, never to quote a volume number.

Striking-distance shortcut (when ~~search console is connected): before broad discovery, mine your own GSC query data for terms already ranking in positions ~5–20 — page-one tail and page two. These are proven demand a small push can convert, so they are the fastest opportunity set. The Search Analytics API sorts by clicks and has no position filter, so request a high rowLimit and filter the 5–20 window client-side, then attach volume / difficulty / intent to that shortlist. Work this set first; treat its metrics as Measured.

Instructions

When a user requests keyword research, run eight phases and announce each as [Phase X/8: Name]:

  1. Scope — clarify product, audience, business goal, DR, geography, and language.
  2. Discover — seed from core, problem, solution, audience, and industry terms.
  3. Variations — expand with modifiers and long-tail patterns.
  4. Classify — tag by intent (informational, navigational, commercial, transactional).
  5. Score — assign difficulty (1-100) and compute Opportunity = (Volume × Intent Value) / Difficulty, with Intent Value 1 / 1 / 2 / 3.
  6. GEO-Check — flag AI-answer-friendly queries such as questions, definitions, comparisons, lists, and how-tos.
  7. Cluster — group keywords into pillar + cluster topic hubs.
  8. Deliver — output an Executive Summary, Quick Wins / Growth / GEO opportunities, Topic Clusters, Content Calendar, and Next Steps.

Label every metric Measured (tool/export), User-provided, Calculated, Estimated, Proxy, or Unknown; retain query, locale, language, source ref, observation time, and window per field. Preserve conflicting sources. If an applicable metric is unavailable, mark it Unknown with a missing reason — N/A is only for a genuinely non-applicable field. An attention proxy never becomes search volume, and an Unknown decision-critical input makes the opportunity score NOT_SCORED.

Impact × Confidence lens (optional, layers onto Phase 5)

When you have richer signals than volume/difficulty alone, add a second pass on top of the Opportunity score:

  • Impact = volume + CPC + funnel stage + trend direction (how much winning the term is worth).
  • Confidence = difficulty + current ranking position + topic authority (how likely you are to win it).
  • Priority = Impact × Confidence — surfaces terms that are both valuable and winnable, not just high-volume.

Tag each keyword by funnel stage from its pattern:

  • BOFU — commercial/transactional, or contains "pricing", "best", "vs", "services", "agency", "hire", "buy".
  • MOFU — informational with buying signals: "how to", "guide", "roi", "case study", "review".
  • TOFU — pure informational (definitions, broad questions).

Work BOFU first when revenue is the goal; use TOFU/MOFU for reach and GEO answer coverage. (Impact×Confidence + funnel-stage scoring adapted from an external SEO-ops competitive analysis.)

Quality bar: every recommendation includes at least one specific number. Rewrite generic advice into a concrete keyword + volume + difficulty + reason.

Reference: See references/instructions-detail.md for the full 8-phase templates, expansion patterns, intent table, difficulty tiers, opportunity matrix, GEO indicators, cluster template, actionable-vs-generic examples, and advanced usage.

Example

See references/example-report.md for a full worked sample.

Save Results

Write path: memory/research/keyword-research/YYYY-MM-DD-<topic>.md; promote durable keyword priorities to memory/hot-cache.md. See Skill Contract §Save Results Template.

Reference Materials

Next Best Skill

Primary: competitor-analysis. Also: content-gap-analysis and serp-analysis.

같은 저장소의 Skills

더 많은 Skills

모든 Skills
aaron-he-zhu
커뮤니티

on-page-seo-checker

Use when the user asks to "audit on-page SEO" or "diagnose why a single page dropped"; scores titles, meta, header structure, keyword placement, links, and images with prioritized fixes. Not for E-E-A-T / publish-readiness scoring — use content-quality-auditor; not for crawl / CWV / indexing — use technical-seo-checker. 页面SEO审计/排名诊断

설치 수
2
GitHub Stars
2.8천
업데이트
9월 12일
aaron-he-zhu
커뮤니티

technical-seo-checker

Use when the user asks to "check technical SEO"; audits crawlability, indexing, Core Web Vitals, robots.txt, sitemaps, canonicals, redirects, and migrations. Not for on-page tags or content — use on-page-seo-checker. 技术SEO/网站速度

설치 수
2
GitHub Stars
2.8천
업데이트
9월 12일
aaron-he-zhu
커뮤니티

ad-test-designer

Use when the user asks to "design an A/B test", "set up a creative/landing test", "run an incrementality test", or "is this result statistically and practically material?"; produces a hypothesis, variant matrix, sample-size/duration/power plan, and a documented effect/uncertainty read from own exported results. It applies only a precommitted owner-approved action rule; the statistical helper never chooses a business action. Not for producing variants — use ad-creative-builder; not for reading back one shipped change — use paid-measurement-loop. 广告AB测试设计/实验设计/显著性判定/增效测试

설치 수
1
GitHub Stars
2.7천
업데이트
9월 3일
aaron-he-zhu
커뮤니티

attribution-reconciler

Use when platform-reported conversions disagree with GA4/ecommerce, when you suspect Meta and Google are double-counting the same sales, or for a standing (monthly) reconciliation workbook that de-dups stacked credit against an order-ID truth set, normalizes attribution windows and currency, compares attribution models, and reads incrementality from a geo/holdout test. Not for the point-in-time R2 veto or RQS gate — use ad-account-auditor; not for the ROI/ROAS ratio math itself — use roi-calculator; not for organic dark-social share attribution or GA4 direct-traffic decomposition — use dark-social-attributor. 付费广告归因对账/去重/增量

설치 수
1
GitHub Stars
2.7천
업데이트
9월 3일