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. Not for competitor-relative coverage gaps — use content-gap-analysis. 关键词研究/内容选题
How do I install this agent skill?
npx skills add https://github.com/aaron-he-zhu/aaron-marketing-skills --skill keyword-researchIs this agent skill safe to install?
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This keyword research skill is a comprehensive SEO tool that automates keyword discovery, scoring, and clustering. It utilizes local Python scripts as 'connectors' to fetch data from Google Autocomplete, Wikipedia, and Firecrawl. The skill follows a strict 8-phase methodology and records all research in organized local directories. All identified behaviors align with its stated purpose, and no malicious patterns or security risks were detected.
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Risk: LOW · No issues
What does this agent skill do?
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, andmemory/research/. - Done when: every shortlisted keyword carries volume + difficulty + intent (or a labeled 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]:
- Scope — clarify product, audience, business goal, DR, geography, and language.
- Discover — seed from core, problem, solution, audience, and industry terms.
- Variations — expand with modifiers and long-tail patterns.
- Classify — tag by intent (informational, navigational, commercial, transactional).
- Score — assign difficulty (1-100) and compute
Opportunity = (Volume × Intent Value) / Difficulty, with Intent Value1 / 1 / 2 / 3. - GEO-Check — flag AI-answer-friendly queries such as questions, definitions, comparisons, lists, and how-tos.
- Cluster — group keywords into pillar + cluster topic hubs.
- 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, or Estimated (model inference); never present an estimate as measured; if a required metric is unavailable, mark it N/A — do not invent it.
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
- Instructions Detail — Workflow, scoring, cluster template, advanced usage
- Keyword Intent Taxonomy — Intent signals and content mapping
- Topic Cluster Templates — Pillar and cluster patterns
- Keyword Prioritization Framework — Scoring and prioritization rules
- Example Report — Worked sample
Next Best Skill
Primary: competitor-analysis. Also: content-gap-analysis and serp-analysis.
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/aaron-he-zhu/aaron-marketing-skills/keyword-research">View keyword-research on skillZs</a>