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seo-keyword-research

SEO keyword research workflow for blog generation using Google Trends data. Use when writing blog posts, planning content calendars, or optimizing articles for search engines. Finds breakout keywords, builds content structure, and generates SEO-optimized blog outlines targeting t

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概要

SEO keyword research workflow for blog generation using Google Trends data. Use when writing blog posts, planning content calendars, or optimizing articles for search engines. Finds breakout keywords, builds content structure, and generates SEO-optimized blog outlines targeting tech and developer audiences.

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SEO Keyword Research Skill

Find trending, high-opportunity keywords BEFORE writing blog content. This skill turns generic blog topics into SEO-optimized content that ranks.

When to Use

  • User asks to write a blog post or article
  • User wants keyword research for a topic
  • User needs a content calendar or content plan
  • User wants to optimize existing content for SEO
  • Any blog generation task for a tech/developer-focused audience

Core Principle

Always research keywords BEFORE generating blog content.

BAD:  Write blog -> Hope it ranks -> Usually doesn't
GOOD: Research keywords -> Find breakout opportunity -> Write optimized blog -> Ranks well

Keyword Priority System

When analyzing Google Trends RELATED_QUERIES results, prioritize keywords in this order:

1. Breakout Keywords — HIGHEST priority
  • formatted_value: "Breakout" = 5000%+ growth
  • Very low competition (trend is new)
  • Use as PRIMARY blog keyword
  • Create content IMMEDIATELY (first-mover advantage)
2. High-Growth Keywords — VERY HIGH priority
  • formatted_value: "+100%" or higher
  • Low to moderate competition
  • Use as primary or strong secondary keyword
  • Create content within 2-4 weeks
3. Moderate-Growth Keywords — HIGH priority
  • formatted_value: "+50%" to "+99%"
  • Moderate competition
  • Use as secondary keywords in body content
4. Long-Tail Keywords — STRATEGIC priority
  • Question-based queries (how, what, why, when, where)
  • Low competition, high conversion
  • Use as H3 headings — target featured snippets and voice search
5. Established Keywords — MODERATE priority
  • Top queries with stable interest, high competition
  • Use in body content, not as primary target

SEO Research Workflow

Step 1: Keyword Discovery (1 API call — REQUIRED)

Query RELATED_QUERIES with the user's blog topic:

curl -s "https://serpapi.com/search?engine=google_trends&q=USER_TOPIC&data_type=RELATED_QUERIES&date=today+3-m&api_key=$SERPAPI_KEY"

From the response, extract:

  • Breakout keywords → candidate primary keywords
  • High-growth keywords (+100%) → secondary candidates
  • Question-based queries → H3 headings and featured snippet targets

Select the primary keyword:

  1. First breakout keyword (if any)
  2. Else first high-growth keyword
  3. Else top query by score
Step 2: Content Structure (1 API call — REQUIRED)

Query RELATED_TOPICS with the same topic:

curl -s "https://serpapi.com/search?engine=google_trends&q=USER_TOPIC&data_type=RELATED_TOPICS&date=today+3-m&api_key=$SERPAPI_KEY"

Extract topic titles from rising + top results. These become your H2 section headings (pick 3-5).

Step 3: Trend Validation (1 API call — OPTIONAL)

Only if choosing between multiple candidate keywords or validating viability:

curl -s "https://serpapi.com/search?engine=google_trends&q=KEYWORD&data_type=TIMESERIES&date=today+12-m&api_key=$SERPAPI_KEY"

Compare recent 2-month average vs. earlier 2-month average. If recent > earlier, trend is rising — proceed. If declining, consider a different keyword.

Step 4: Generate Blog Outline

Build the outline using this structure:

Title: [Primary Keyword] — [Benefit/Number] [Year]
  (max 60 characters, must include primary keyword)

Meta Description: (150-160 chars, primary + 1-2 secondary keywords)

# [H1 — same as or variation of title]

## Introduction (150 words)
  - Primary keyword in first 100 words
  - Hook with a problem or question
  - Preview what they'll learn

## [H2: Related Topic 1 from Step 2]
### [H3: Long-tail question from Step 1]
  Content answering the question (150-200 words)
### [H3: Another long-tail question]
  Content (150-200 words)

## [H2: Related Topic 2]
### [H3: Long-tail question]
### [H3: Long-tail question]

## [H2: Related Topic 3]
### [H3: Long-tail question]
### [H3: Long-tail question]

## Conclusion (100 words)
  - Summarize key points
  - Primary keyword mentioned once
  - Call-to-action

Target: 1500-2500 words total

Keyword Placement Rules

LocationRule
TitleInclude primary keyword, max 60 chars
H1Same as title or slight variation
H2 headings (3-5)Use related topics, natural language
H3 headings (8-12)Use long-tail keywords, question format
First paragraphPrimary keyword in first 100 words
Body contentPrimary keyword 1-2% density, secondary 0.5-1%
ConclusionPrimary keyword once
Meta descriptionPrimary + 1-2 secondary, 150-160 chars

Never keyword-stuff. Content must read naturally. Google penalizes unnatural repetition.

Quality Checklist

Before generating the blog, verify:

  • Found at least 1 breakout or +100% keyword (or justified using established keyword)
  • Have 3-5 H2 topics from RELATED_TOPICS
  • Have long-tail keywords for H3 headings
  • Primary keyword is specific enough to rank for
  • Blog structure follows the outline template above
  • Meta description is written (150-160 chars)
  • Target length is 1500-2500 words

If no breakout or high-growth keywords exist for the topic, inform the user that SEO opportunity is limited and suggest alternative angles or related topics that do have growth.

Budget Awareness

Free tier: 250 searches/month

StrategyCalls/BlogMonthly Capacity
Minimal (recommended)2125 blogs
Standard383 blogs
Complete462 blogs

Default to 2 calls (RELATED_QUERIES + RELATED_TOPICS). Only add TIMESERIES or GEO_MAP when specifically needed.

Common Mistakes to Avoid

  1. Skipping research — writing without checking trends misses breakout opportunities
  2. Ignoring breakout keywords — using a generic term when a breakout variant exists
  3. Keyword stuffing — repeating keywords unnaturally; keep density at 1-2%
  4. No long-tail keywords — missing featured snippet and voice search opportunities
  5. Generic H2 headings — always use RELATED_TOPICS data for section structure

Example Script

For a complete working example, run:

python scripts/blog_seo_research.py "your blog topic"

See scripts/blog_seo_research.py for the implementation.

References

ソースを見る ↗
ファイルのメタデータ
name: seo-keyword-research
description: SEO keyword research workflow for blog generation using Google Trends data. Use when writing blog posts, planning content calendars, or optimizing articles for search engines. Finds breakout keywords, builds content structure, and generates SEO-optimized blog outlines targeting tech and developer audiences.
license: MIT
compatibility: Requires the google-trends-api skill (or direct SerpApi access with SERPAPI_KEY). Designed for Claude Code and similar AI coding agents.
metadata:
  author: farizanjum
  version: "2.0"
  domain: tech-developer-blogs
allowed-tools: Bash(python:*) Bash(curl:*) Read
元のテキストを表示
---
name: seo-keyword-research
description: SEO keyword research workflow for blog generation using Google Trends data. Use when writing blog posts, planning content calendars, or optimizing articles for search engines. Finds breakout keywords, builds content structure, and generates SEO-optimized blog outlines targeting tech and developer audiences.
license: MIT
compatibility: Requires the google-trends-api skill (or direct SerpApi access with SERPAPI_KEY). Designed for Claude Code and similar AI coding agents.
metadata:
  author: farizanjum
  version: "2.0"
  domain: tech-developer-blogs
allowed-tools: Bash(python:*) Bash(curl:*) Read
---

# SEO Keyword Research Skill

Find trending, high-opportunity keywords BEFORE writing blog content. This skill turns generic blog topics into SEO-optimized content that ranks.

## When to Use

- User asks to write a blog post or article
- User wants keyword research for a topic
- User needs a content calendar or content plan
- User wants to optimize existing content for SEO
- Any blog generation task for a tech/developer-focused audience

## Core Principle

**Always research keywords BEFORE generating blog content.**

```
BAD:  Write blog -> Hope it ranks -> Usually doesn't
GOOD: Research keywords -> Find breakout opportunity -> Write optimized blog -> Ranks well
```

## Keyword Priority System

When analyzing Google Trends RELATED_QUERIES results, prioritize keywords in this order:

### 1. Breakout Keywords — HIGHEST priority
- `formatted_value: "Breakout"` = 5000%+ growth
- Very low competition (trend is new)
- Use as PRIMARY blog keyword
- Create content IMMEDIATELY (first-mover advantage)

### 2. High-Growth Keywords — VERY HIGH priority
- `formatted_value: "+100%"` or higher
- Low to moderate competition
- Use as primary or strong secondary keyword
- Create content within 2-4 weeks

### 3. Moderate-Growth Keywords — HIGH priority
- `formatted_value: "+50%"` to `"+99%"`
- Moderate competition
- Use as secondary keywords in body content

### 4. Long-Tail Keywords — STRATEGIC priority
- Question-based queries (how, what, why, when, where)
- Low competition, high conversion
- Use as H3 headings — target featured snippets and voice search

### 5. Established Keywords — MODERATE priority
- Top queries with stable interest, high competition
- Use in body content, not as primary target

## SEO Research Workflow

### Step 1: Keyword Discovery (1 API call — REQUIRED)

Query `RELATED_QUERIES` with the user's blog topic:

```bash
curl -s "https://serpapi.com/search?engine=google_trends&q=USER_TOPIC&data_type=RELATED_QUERIES&date=today+3-m&api_key=$SERPAPI_KEY"
```

From the response, extract:
- **Breakout keywords** → candidate primary keywords
- **High-growth keywords** (+100%) → secondary candidates
- **Question-based queries** → H3 headings and featured snippet targets

Select the primary keyword:
1. First breakout keyword (if any)
2. Else first high-growth keyword
3. Else top query by score

### Step 2: Content Structure (1 API call — REQUIRED)

Query `RELATED_TOPICS` with the same topic:

```bash
curl -s "https://serpapi.com/search?engine=google_trends&q=USER_TOPIC&data_type=RELATED_TOPICS&date=today+3-m&api_key=$SERPAPI_KEY"
```

Extract topic titles from rising + top results. These become your H2 section headings (pick 3-5).

### Step 3: Trend Validation (1 API call — OPTIONAL)

Only if choosing between multiple candidate keywords or validating viability:

```bash
curl -s "https://serpapi.com/search?engine=google_trends&q=KEYWORD&data_type=TIMESERIES&date=today+12-m&api_key=$SERPAPI_KEY"
```

Compare recent 2-month average vs. earlier 2-month average. If recent > earlier, trend is rising — proceed. If declining, consider a different keyword.

### Step 4: Generate Blog Outline

Build the outline using this structure:

```
Title: [Primary Keyword] — [Benefit/Number] [Year]
  (max 60 characters, must include primary keyword)

Meta Description: (150-160 chars, primary + 1-2 secondary keywords)

# [H1 — same as or variation of title]

## Introduction (150 words)
  - Primary keyword in first 100 words
  - Hook with a problem or question
  - Preview what they'll learn

## [H2: Related Topic 1 from Step 2]
### [H3: Long-tail question from Step 1]
  Content answering the question (150-200 words)
### [H3: Another long-tail question]
  Content (150-200 words)

## [H2: Related Topic 2]
### [H3: Long-tail question]
### [H3: Long-tail question]

## [H2: Related Topic 3]
### [H3: Long-tail question]
### [H3: Long-tail question]

## Conclusion (100 words)
  - Summarize key points
  - Primary keyword mentioned once
  - Call-to-action

Target: 1500-2500 words total
```

## Keyword Placement Rules

| Location | Rule |
|----------|------|
| Title | Include primary keyword, max 60 chars |
| H1 | Same as title or slight variation |
| H2 headings (3-5) | Use related topics, natural language |
| H3 headings (8-12) | Use long-tail keywords, question format |
| First paragraph | Primary keyword in first 100 words |
| Body content | Primary keyword 1-2% density, secondary 0.5-1% |
| Conclusion | Primary keyword once |
| Meta description | Primary + 1-2 secondary, 150-160 chars |

**Never keyword-stuff.** Content must read naturally. Google penalizes unnatural repetition.

## Quality Checklist

Before generating the blog, verify:

- [ ] Found at least 1 breakout or +100% keyword (or justified using established keyword)
- [ ] Have 3-5 H2 topics from RELATED_TOPICS
- [ ] Have long-tail keywords for H3 headings
- [ ] Primary keyword is specific enough to rank for
- [ ] Blog structure follows the outline template above
- [ ] Meta description is written (150-160 chars)
- [ ] Target length is 1500-2500 words

If no breakout or high-growth keywords exist for the topic, inform the user that SEO opportunity is limited and suggest alternative angles or related topics that do have growth.

## Budget Awareness

**Free tier: 250 searches/month**

| Strategy | Calls/Blog | Monthly Capacity |
|----------|-----------|-----------------|
| Minimal (recommended) | 2 | 125 blogs |
| Standard | 3 | 83 blogs |
| Complete | 4 | 62 blogs |

Default to 2 calls (RELATED_QUERIES + RELATED_TOPICS). Only add TIMESERIES or GEO_MAP when specifically needed.

## Common Mistakes to Avoid

1. **Skipping research** — writing without checking trends misses breakout opportunities
2. **Ignoring breakout keywords** — using a generic term when a breakout variant exists
3. **Keyword stuffing** — repeating keywords unnaturally; keep density at 1-2%
4. **No long-tail keywords** — missing featured snippet and voice search opportunities
5. **Generic H2 headings** — always use RELATED_TOPICS data for section structure

## Example Script

For a complete working example, run:

```bash
python scripts/blog_seo_research.py "your blog topic"
```

See [scripts/blog_seo_research.py](scripts/blog_seo_research.py) for the implementation.

## References

- [references/keyword-placement-guide.md](references/keyword-placement-guide.md) — Detailed placement rules and examples
- [references/tech-blog-examples.md](references/tech-blog-examples.md) — Real-world examples for tech/developer blogs

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ライセンス: MIT

  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • No explicit error handling for API failures or rate limits in the SKILL.md workflow.
  • The allowed-tools list is broad (Bash(python:*) and Bash(curl:*)) which could be tightened to specific commands, though no misuse is evident.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
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ソースリポジトリ
Varnan-Tech/opendirectory
ライセンス
MIT
バージョン
1.0.0
最終 GitHub プッシュ
2026年8月16日
登録情報の更新日
2026年9月5日
手順のパス
skills/google-trends-api-skills/seo-keyword-research/SKILL.md @ 62e437ab1340

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品質

72/100

強い

信頼

60/100

サンドボックス限定

監査

75/100

要レビュー

完全な監査を開く評価レポートを見る
  • Dependency or permission surface needs review
  • Permission surface may require sandboxing
  • No explicit error handling for API failures or rate limits in the SKILL.md workflow.
  • The allowed-tools list is broad (Bash(python:*) and Bash(curl:*)) which could be tightened to specific commands, though no misuse is evident.
  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
  • Dependency/runtime risk: command execution surface, credential or environment access
  • Permission surface: secrets or environment access, shell or command execution
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詳細情報
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      "license": "MIT",
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      "install": "npx skills add Varnan-Tech/opendirectory --skill seo-keyword-research",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "secrets or environment access, shell or command execution",
      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "research",
      "agent-skill"
    ],
    "known_risks": [
      "No explicit error handling for API failures or rate limits in the SKILL.md workflow.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 75,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "No explicit error handling for API failures or rate limits in the SKILL.md workflow.",
      "The allowed-tools list is broad (Bash(python:*) and Bash(curl:*)) which could be tightened to specific commands, though no misuse is evident.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 72,
    "label": "Strong"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "2mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "mvanhorn-last30days-skill",
      "name": "Last30days Skill",
      "url": "https://www.openagentskill.com/skills/mvanhorn-last30days-skill",
      "stars": 63666,
      "install_command": "",
      "trust_score": 94,
      "audit_score": 95
    },
    {
      "slug": "yanliudesign-mono-color-skill",
      "name": "mono-color",
      "url": "https://www.openagentskill.com/skills/yanliudesign-mono-color-skill",
      "stars": 1919,
      "install_command": "npx skills add yanliudesign/mono-color-skill --skill mono-color",
      "trust_score": 83,
      "audit_score": 90
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "No explicit error handling for API failures or rate limits in the SKILL.md workflow.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "The allowed-tools list is broad (Bash(python:*) and Bash(curl:*)) which could be tightened to specific commands, though no misuse is evident.",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use seo-keyword-research in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 68/100 Manual review",
      "Audit: 75/100 Needs review",
      "Safety: 35/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "varnan-tech-seo-keyword-research (seo-keyword-research)",
      "install_command": "npx skills add Varnan-Tech/opendirectory --skill seo-keyword-research",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "varnan-tech-seo-keyword-research",
      "task": "Use seo-keyword-research in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/varnan-tech-seo-keyword-research",
    "api": "https://www.openagentskill.com/api/agent/skills/varnan-tech-seo-keyword-research",
    "audit": "https://www.openagentskill.com/skills/varnan-tech-seo-keyword-research/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=varnan-tech-seo-keyword-research&task=Use%20seo-keyword-research%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20seo-keyword-research%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20seo-keyword-research%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/varnan-tech-seo-keyword-research/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/varnan-tech-seo-keyword-research"
  }
}

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作成者
Varnan-Tech
インデックス作成者
OpenAgentSkill コミュニティインデックス

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/varnan-tech-seo-keyword-research?metric=listed&label=Listed)](https://www.openagentskill.com/skills/varnan-tech-seo-keyword-research?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
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