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keyword-research

作者 Kostja Zhang · GitHub ↗ · v1.3.1 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install keyword-research-seo
功能描述
When the user wants to research keywords, find target keywords, or analyze search intent. Also use when the user mentions "keyword research," "keyword tool,"...
使用说明 (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
安全使用建议
This skill is coherent and low-risk: it only contains runtime instructions for SEO keyword research and asks for no installs or credentials. Two practical notes before installing: (1) it will look for and read project context files (.claude/project-context.md or .cursor/project-context.md) if present in the agent workspace — remove or redact any sensitive info from those files if you don't want them used; (2) the skill may recommend using external tools (Google Keyword Planner, Trends, SEO tools). If you later provide API keys or connect those tools, review permissions and trust the endpoint. Otherwise, it's safe to enable from a scope/privilege perspective.
能力评估
Purpose & Capability
The name/description (keyword research, search intent, clustering) match the SKILL.md steps (autocomplete, alphabet method, SERP checks, clustering). The skill does not request unrelated binaries, credentials, or config paths.
Instruction Scope
The runtime instructions are detailed but stay within keyword-research tasks (discovery methods, intent, clustering, SERP checks). The skill asks the agent to read project context files (.claude/project-context.md or .cursor/project-context.md) to learn product/audience—this is relevant to the task but is an unstated file-read requirement (no config path declared). There are no instructions to exfiltrate secrets or to post data to unknown endpoints.
Install Mechanism
No install spec and no code files — instruction-only skills have the lowest install risk and nothing will be written to disk by an installer.
Credentials
The skill requires no environment variables or credentials. It mentions using external SEO tools (Google Keyword Planner, Trends, etc.) but does not demand API keys or credentials in its manifest.
Persistence & Privilege
always:false (default) and model invocation is allowed (normal). The skill does not request permanent/privileged presence or attempt to modify other skills or system-wide settings.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install keyword-research-seo
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /keyword-research-seo 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.3.1
Automated batch sync
v1.3.0
Automated batch sync
元数据
Slug keyword-research-seo
版本 1.3.1
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 2
常见问题

keyword-research 是什么?

When the user wants to research keywords, find target keywords, or analyze search intent. Also use when the user mentions "keyword research," "keyword tool,"... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 184 次。

如何安装 keyword-research?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install keyword-research-seo」即可一键安装,无需额外配置。

keyword-research 是免费的吗?

是的,keyword-research 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

keyword-research 支持哪些平台?

keyword-research 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 keyword-research?

由 Kostja Zhang(@kostja94)开发并维护,当前版本 v1.3.1。

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