跳到正文
cccskills
免费在 GitHub 公开

keyword-research

Find and organize search keywords for a product, service, or topic. Use available search volume, ranking difficulty, and search intent data to prioritize opportunities, group related topics, and build a content calendar with clearly labeled evidence gaps.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

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. Not for competitor-relative coverage gaps — use content-gap-analysis. 关键词研究/内容选题

集成工具Search Console

实测评分 未实测

查看安装方法

适用场景

  • Choosing keywords for a new product page
  • Turning a broad topic into article ideas
  • Prioritizing queries already near the first search results page
  • Organizing related keywords into a content calendar

包含的文件(6)

  • SKILL.md8.5 KB
  • references/example-report.md3.6 KB
  • references/instructions-detail.md4.5 KB
  • references/keyword-intent-taxonomy.md4.5 KB
  • references/keyword-prioritization-framework.md1.9 KB
  • references/topic-cluster-templates.md4.2 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Audit paid advertising accounts before increasing spend by comparing campaign exports with your own sales or lead records. Review 20 criteria covering measurement, spending, and safety, then receive a decision and prioritized fixes.

中文简介正在准备中。当前显示英语简介。

  • Review an ad account before increasing its budget
  • Check reported ad returns against sales and lead records
  • Identify wasted spend and prioritize measurement fixes
aaron-he-zhu/aaron-marketing-skills30132026年10月23日 更新

Create paid-ad headlines, descriptions, and opening hooks that match the destination page. Build a matrix of messaging angles and variants, check format limits, and flag unsupported claims or policy risks before handing the set off for review.

中文简介正在准备中。当前显示英语简介。

  • Drafting responsive search ad headlines and descriptions
  • Comparing ad angles and opening hooks
  • Replacing weak headlines while keeping successful ones
aaron-he-zhu/aaron-marketing-skills30132026年10月23日 更新

Plan advertising and landing page experiments with a clear hypothesis, controlled variants, sample sizes, and duration. Interpret exported results by separating statistical evidence from meaningful business impact, applying an action rule only when its owner approved it in advance.

中文简介正在准备中。当前显示英语简介。

  • Plan an A/B test for landing page headlines
  • Estimate sample size and duration for an ad test
  • Assess effect size and uncertainty in exported test results
aaron-he-zhu/aaron-marketing-skills30132026年10月23日 更新

Design employee and founder sharing programs with voluntary participation, personalized share kits, disclosure lines, and staggered posting windows. Get roster and Slack or Teams distribution specifications, plus safeguards against identical coordinated posts and organized engagement exchanges.

中文简介正在准备中。当前显示英语简介。

  • Designing a voluntary employee sharing program
  • Preparing personalized launch share kits
  • Planning founder posts and disclosure lines
aaron-he-zhu/aaron-marketing-skills30132026年10月23日 更新

Reconcile paid advertising conversions with GA4 or ecommerce order records. Identify duplicate and unmatched claims, align attribution windows and currencies, compare how models assign credit, and assess additional orders when test and control data are available.

中文简介正在准备中。当前显示英语简介。

  • Find sales claimed by both Meta and Google
  • Build a monthly conversion reconciliation workbook
  • Compare results across attribution models
aaron-he-zhu/aaron-marketing-skills30132026年10月23日 更新

Turn customer interviews and won or lost deal notes into a map of buyer beliefs, recurring objections, and reasons to switch or stay. Each finding includes its evidence, with suggested ways to address objections and clear labels for assumptions.

中文简介正在准备中。当前显示英语简介。

  • Map buyer beliefs from customer interviews
  • Organize recurring sales objections and response angles
  • Identify reasons buyers switch or stay
aaron-he-zhu/aaron-marketing-skills30132026年10月23日 更新

查看 aaron-he-zhu 的全部技能

报告此技能的问题