Andrew Yim
6ac547e78f
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Adopt: directory keeps its NN- ordering prefix; skill `name:` is the clean form without it (dir 16-seo-schema-validator → name: seo-schema-validator). Nicer to invoke, matches the original desktop/SKILL.md names, still globally unique. - 71 root SKILL.md: name: NN-foo → name: foo (flat skills + reference-curator suite). Plugins (mac-optimizer/multi-agent-guide/dintel-bootstrap) already clean; 95 already clean. - scripts/migrate_skill_root.py: derive name = dirname minus NN- prefix (skill_name()). - CLAUDE.md + SKILL-MIGRATION-GUIDE.md: document the dir-prefix / clean-name convention. verify_skills.py: 0 name collisions across all renamed skills. (The ~/.claude/skills symlinks were re-pointed to the clean names separately — filesystem only.) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
149 lines
6.6 KiB
Markdown
149 lines
6.6 KiB
Markdown
---
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name: seo-keyword-strategy
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description: |
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Keyword strategy and research for SEO campaigns.
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Triggers: keyword research, keyword analysis, keyword gap, search volume,
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keyword clustering, intent classification, 키워드 전략, 키워드 분석,
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키워드 리서치, 검색량 분석, 키워드 클러스터링.
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---
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# SEO Keyword Strategy & Research
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## Purpose
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Expand seed keywords, classify search intent, cluster topics, and identify competitor keyword gaps for comprehensive keyword strategy development.
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## Core Capabilities
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1. **Keyword Expansion** - Matching terms, related terms, search suggestions
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2. **Korean Market** - Suffix expansion, Naver autocomplete, Korean intent patterns
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3. **Intent Classification** - Informational, navigational, commercial, transactional
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4. **Topic Clustering** - Group keywords into semantic clusters
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5. **Gap Analysis** - Find competitor keywords missing from target site
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## Data Source Selection
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This skill can pull keyword data from multiple backends. **Pick one per task** — don't fan out to every backend by default (cost + rate limits).
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| Backend | Best for | Notes |
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|---|---|---|
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| **Semrush MCP** (`mcp__semrush__*`) | Default for keyword volume, related/matching terms, organic competitor pulls | Call pattern: `keyword_research` → `get_report_schema` → `execute_report`. `database="us"` default; `"kr"` for Korean market. |
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| **Ahrefs MCP** (`mcp__ahrefs__*`) | Ahrefs DR/UR weighting; first-party `gsc-keywords` (only Ahrefs integrates GSC inside its MCP) | `keywords-explorer-overview`, `-matching-terms`, `-related-terms`, `-search-suggestions`, `-volume-by-country`, `gsc-keywords`. |
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| **OurSEO Agent CLI** (`our keywords *`) | DataForSEO under the hood — cheapest per call, batch-friendly, Korean-aware via `--location 2410` | Claude Code only (needs Bash). Wrap calls: `our keywords volume`, `ideas`, `for-site`, `intent`, `difficulty`. |
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| **OurSEO Agent MCP** (`mcp__ourseo__*`) | Claude Desktop equivalent for crawl-derived keywords + Knowledge Graph entity expansion | `search_knowledge_graph` for entity seeding; `crawl_website` to extract on-page keyword inventory from the target site itself. |
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| **DataForSEO MCP** (`mcp__dfs-mcp__*`) | Direct fallback when `our` CLI isn't available | Same data as `our keywords *`. |
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| **GSC** (via `our research search-console` or Ahrefs `gsc-*`) | First-party queries the site actually ranks for — ground truth, not estimates | Use to validate/prune Semrush or Ahrefs lists with real impressions/CTR. |
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### How to pick
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Apply these in order; stop at the first match:
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1. **User named a backend explicitly** in the prompt → use it.
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2. **User preference memory** — read `feedback_seo_tool_preferences.md`; honor the task-type default there.
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3. **Task needs a capability only one backend has** (e.g., `gsc-keywords` first-party data, or `mcp__ourseo__search_knowledge_graph` entity expansion) → use that backend.
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4. **Default by market**:
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- English-market or unspecified → **Semrush MCP** with `database="us"`.
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- Korean market → **OurSEO CLI** `our keywords <subcmd> --location 2410 --language ko` (Claude Code), or **Semrush MCP** with `database="kr"` (Claude Desktop).
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5. **Still ambiguous on a non-trivial task** → ask once via `AskUserQuestion` listing the top 2–3 candidates.
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### Backend call patterns
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**Semrush MCP (default):**
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```
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mcp__semrush__keyword_research(query="<seed>", database="us")
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mcp__semrush__get_report_schema(report_id="...")
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mcp__semrush__execute_report(report_id="...", params={...})
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```
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**OurSEO CLI (Korean default, Claude Code):**
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```bash
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our keywords volume "<keyword>" --location 2410 --language ko
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our keywords ideas "<keyword>" --location 2410 --limit 50
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our keywords for-site <competitor.com> --location 2410 --limit 100
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our keywords intent "<kw1>" "<kw2>" "<kw3>"
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our keywords difficulty "<kw1>" "<kw2>"
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```
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**Ahrefs MCP (when user requests, or for GSC first-party):**
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```
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mcp__ahrefs__keywords-explorer-overview(keyword="<seed>", country="us")
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mcp__ahrefs__keywords-explorer-matching-terms(keyword="<seed>", country="us")
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mcp__ahrefs__keywords-explorer-volume-by-country(keyword="<seed>")
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mcp__ahrefs__gsc-keywords(...)
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```
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**OurSEO Agent MCP (Claude Desktop, KG/entity expansion):**
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```
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mcp__ourseo__search_knowledge_graph(query="<brand or entity>")
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mcp__ourseo__crawl_website(url="<target>", max_pages=50)
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```
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### Common parameters across backends
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| Concept | Semrush | Ahrefs | DataForSEO / `our` CLI |
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|---|---|---|---|
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| Korean market | `database="kr"` | `country="kr"` | `--location 2410` |
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| US market | `database="us"` | `country="us"` | `--location 2840` |
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| Japan | `database="jp"` | `country="jp"` | `--location 2392` |
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| Language | (database-bound) | (country-bound) | `--language ko`/`en`/`ja` |
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## Workflow
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### 1. Seed Keyword Expansion
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1. Determine backend via **Data Source Selection** above.
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2. Fetch search volume for the seed.
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3. Expand via the chosen backend's "related" / "ideas" / "matching-terms" endpoint.
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4. Apply Korean suffix expansion if Korean market (regardless of backend).
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5. Deduplicate and merge.
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### 2. Intent Classification & Clustering
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1. Classify each keyword by search intent (informational / navigational / commercial / transactional).
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2. Group keywords into topic clusters.
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3. Identify pillar topics and supporting terms.
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4. Calculate cluster-level metrics (total volume, avg KD).
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### 3. Gap Analysis
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1. Pull organic keywords for target via chosen backend.
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2. Pull organic keywords for competitors (parallel).
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3. Identify keywords present in competitors but missing from target.
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4. Score opportunities by volume/difficulty ratio.
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5. Prioritize by intent alignment with business goals.
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## Output Format
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```markdown
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## Keyword Strategy Report: [seed keyword]
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### Overview
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- Data source: [Semrush | Ahrefs | OurSEO CLI | OurSEO MCP | GSC]
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- Market: [database/location code]
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- Total keywords discovered: [count]
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- Topic clusters: [count]
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- Total search volume: [sum]
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### Top Clusters
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| Cluster | Keywords | Total Volume | Avg KD |
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|---|---|---|---|
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| ... | ... | ... | ... |
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### Top Opportunities
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| Keyword | Volume | KD | Intent | Cluster |
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|---|---|---|---|---|
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| ... | ... | ... | ... | ... |
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### Keyword Gaps (vs competitors)
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| Keyword | Volume | Competitor Position | Opportunity Score |
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|---|---|---|---|
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| ... | ... | ... | ... |
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```
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Always record the chosen data source in the **Overview** so future audits can compare apples to apples.
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## Notion Output (Required)
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All audit reports MUST be saved to OurDigital SEO Audit Log:
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- **Database ID**: `2c8581e5-8a1e-8035-880b-e38cefc2f3ef`
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- **Properties**: Issue (title), Site (url), Category, Priority, Found Date, Audit ID
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- **Language**: Korean with English technical terms
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- **Audit ID Format**: KW-YYYYMMDD-NNN
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