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Agent skill

Keyword Research

by aaron-he-zhu in aaron-he-zhu/aaron-marketing-skills

A skill your agent uses 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.

Apache-2.0Auto-check passedMarketing & SEO

Install Keyword Research

skills CLI
$ npx skills add aaron-he-zhu/aaron-marketing-skills --skill keyword-research -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aaron-he-zhu/aaron-marketing-skills keyword-research --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aaron-he-zhu/aaron-marketing-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/seo-geo/survey/keyword-research .claude/skills/keyword-research && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
keyword-research
GitHub stars
2.9k
Used in
1 other repo
Token cost
~2.1k tokens
SKILL.md length
824 words
Files
6 (incl. references)
Skills in repo
119
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses 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.

  • Works in 8 steps: Scope — clarify product, audience,… → Discover — seed from core, problem,… → Variations — expand with modifiers and… → …
  • The user asks to find keywords
  • SKILL.md covers Quick Start, Skill Contract, Data Sources and Instructions, plus 4 more sections
  • Calls python3

What it does

Keyword Research is an agent skill from aaron-he-zhu/aaron-marketing-skills. 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

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `references/example-report.md`, `references/instructions-detail.md` and `references/keyword-intent-taxonomy.md`). Compatibility notes: Claude Code and compatible agent-skill hosts

It sits in Marketing & SEO, covering Keyword research. The repository describes itself as: 120 marketing skills as an AI marketing staff — plugin, portable skills, or an 8-bot team across 7 disciplines (narrative, SEO/GEO, social, email, paid, influencer, launch) on… The licence is Apache-2.0.

When your agent uses it

  • The user asks to find keywords
  • Prioritizes search volume
  • Keyword difficulty
  • Topic clusters from provided

Example prompts

  • “find keywords”
  • “/keyword-research”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Claude Code and compatible agent-skill hosts

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  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.

What it can do on your machine

Read from SKILL.md and the folder at commit d5529cb. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Claude Code and compatible agent-skill hosts

    From compatibility in the SKILL.md frontmatter.

Context cost

Keyword Research loads about 2.1k tokens when it runs, and up to ~6.9k if it reads all its reference files. Until then it costs about 66 tokens; SKILL.md has 824 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~66
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from aaron-he-zhu/aaron-marketing-skills at commit d5529cb, republished under its Apache-2.0 licence (© aaron-he-zhu). 824 words, ~2,136 tokens.

Download SKILL.mdSave it as .claude/skills/keyword-research/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
keyword-research
description
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
compatibility
Claude Code and compatible agent-skill hosts
slug
keyword-research
displayName
Keyword Research · 关键词研究
summary
关键词研究/内容选题
version
20.1.0
license
Apache-2.0
homepage
https://github.com/aaron-he-zhu/aaron-marketing-skills
when_to_use
Use when starting keyword research for a new page, topic, or campaign. Also when the user asks about search volume, keyword difficulty, topic clusters…
argument-hint
<topic or seed keyword> [market/language]
metadata.author
aaron-he-zhu
metadata.version
20.1.0

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.

Show full SKILL.md (261 more words)Show less
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.

© aaron-he-zhu, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (references) in seo-geo/survey/keyword-research of aaron-he-zhu/aaron-marketing-skills.

  • SKILL.md
  • references/example-report.md
  • references/instructions-detail.md
  • references/keyword-intent-taxonomy.md
  • references/keyword-prioritization-framework.md
  • references/topic-cluster-templates.md

Open the folder on GitHubat commit d5529cb

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in aaron-he-zhu/aaron-marketing-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Keyword Research next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Keyword Research compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Keyword Research this skillaaron-he-zhu/aaron-marketing-skills2.9k1 repos~2.1kAutomated safety check: PassApache-2.0
SEO Keyword ClusteringAgriciDaniel/claude-seo19k2 repos~3.3kAutomated safety check: PassMIT
Evaluate Skillevery-app/open-seo23k—~1.8kAutomated safety check: NotesMIT
SEO Content Brief GeneratorAgriciDaniel/claude-seo19k2 repos~2.6kAutomated safety check: PassMIT
Blog GoogleAgriciDaniel/claude-blog2.3k1 repos~3.3kAutomated safety check: NotesMIT
FLOW SEO FrameworkAgriciDaniel/claude-seo19k2 repos~1.4kAutomated safety check: PassMIT

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Categories

Questions about Keyword Research

What does Keyword Research do?

A skill your agent uses 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. Keyword Research is an agent skill from aaron-he-zhu/aaron-marketing-skills. 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.

When should I use Keyword Research?

Keyword Research fits situations like: the user asks to find keywords; prioritizes search volume; keyword difficulty; topic clusters from provided.

How do I install Keyword Research in Claude Code?

Run `npx skills add aaron-he-zhu/aaron-marketing-skills --skill keyword-research -a claude-code`. Or copy the skill folder (seo-geo/survey/keyword-research in aaron-he-zhu/aaron-marketing-skills) into .claude/skills/keyword-research in your project. Claude Code loads it when a task matches its description.

How do I install Keyword Research in Codex?

Run `npx skills add aaron-he-zhu/aaron-marketing-skills --skill keyword-research -a codex`. Or copy the skill folder (seo-geo/survey/keyword-research in aaron-he-zhu/aaron-marketing-skills) into .agents/skills/keyword-research in your project. Codex loads it when a task matches its description.

Can I use Keyword Research in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aaron-he-zhu/aaron-marketing-skills --skill keyword-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/keyword-research, .gemini/skills/keyword-research, .github/skills/keyword-research and .opencode/skills/keyword-research in your project.

What does Keyword Research need to run?

Going by SKILL.md and its folder, Keyword Research needs the command-line tools its instructions call (python3). Our summary lists: Python 3. Compatibility (from SKILL.md): Claude Code and compatible agent-skill hosts.

Does Keyword Research access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Keyword Research safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Keyword Research use?

Keyword Research is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Keyword Research use?

About 2.1k tokens (SKILL.md is roughly 8.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.8k tokens, read only when the agent opens those files.

What are the alternatives to Keyword Research?

Skills that share tags, products or a category with Keyword Research: SEO Keyword Clustering (AgriciDaniel/claude-seo, 19k stars), Evaluate Skill (every-app/open-seo, 23k stars), SEO Content Brief Generator (AgriciDaniel/claude-seo, 19k stars) and Blog Google (AgriciDaniel/claude-blog, 2.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Keyword Research?

aaron-he-zhu (a GitHub user) maintains it in aaron-he-zhu/aaron-marketing-skills, which has 2,898 GitHub stars. The repository holds 119 skills in this directory. The repository was last updated on October 11, 2026.

Source: aaron-he-zhu/aaron-marketing-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.