Skill: adamkristopher/seo-dataforseo (version 1.0.0)

--- name: seo-dataforseo description: "SEO keyword research using the DataForSEO API. Perform keyword analysis, YouTube keyword research, competitor analysis, SERP analysis, and trend tracking. Use when the user asks to: research keywords, analyze search volume/CPC/competition, find keyword suggestions, check keyword difficulty, analyze competitors, get trending topics, do YouTube SEO research, or optimize landing page keywords. Requires a DataForSEO API account and credentials in .env file." --- # SEO Keyword Research (DataForSEO) ## Setup Install dependencies: ```bash pip install -r scripts/requirements.txt ``` Configure credentials by creating a `.env` file in the project root: ``` DATAFORSEO_LOGIN=[REDACTED_SECRET] DATAFORSEO_PASSWORD=your_api_password ``` Get credentials from: https://app.dataforseo.com/api-access ## Quick Start | User says | Function to call | |-----------|-----------------| | "Research keywords for [topic]" | `keyword_research("topic")` | | "YouTube keyword data for [idea]" | `youtube_keyword_research("idea")` | | "Analyze competitor [domain.com]" | `competitor_analysis("domain.com")` | | "What's trending?" | `trending_topics()` | | "Keyword analysis for [list]" | `full_keyword_analysis(["kw1", "kw2"])` | | "Landing page keywords for [topic]" | `landing_page_keyword_research(["kw1"], "competitor.com")` | Execute functions by importing from `scripts/main.py`: ```python import sys from pathlib import Path sys.path.insert(0, str(Path("scripts"))) from main import * result = keyword_research("AI website builders") ``` ## Workflow Pattern Every research task follows three phases: ### 1. Research Run API functions. Each function call hits the DataForSEO API and returns structured data. ### 2. Auto-Save All results automatically save as timestamped JSON files to `results/{category}/`. File naming pattern: `YYYYMMDD_HHMMSS__operation__keyword__extra_info.json` ### 3. Summarize After research, read the saved JSON files and create a markdown summary in `results/summary/` with data tables, ranked opportunities, and strategic recommendations. ## High-Level Functions These are the primary functions in `scripts/main.py`. Each orchestrates multiple API calls for a complete research workflow. | Function | Purpose | What it gathers | |----------|---------|----------------| | `keyword_research(keyword)` | Single keyword deep-dive | Overview, suggestions, related keywords, difficulty | | `youtube_keyword_research(keyword)` | YouTube content research | Overview, suggestions, YouTube SERP rankings, YouTube trends | | `landing_page_keyword_research(keywords, competitor_domain)` | Landing page SEO | Overview, intent, difficulty, SERP analysis, competitor keywords | | `full_keyword_analysis(keywords)` | Strategic content planning | Overview, difficulty, intent, keyword ideas, historical volume, Google Trends | | `competitor_analysis(domain, keywords)` | Competitor intelligence | Domain keywords, Google Ads keywords, competitor domains | | `trending_topics(location_name)` | Current trends | Currently trending searches | ### Parameters All functions accept an optional `location_name` parameter (default: "United States"). Most functions also have boolean flags to skip specific sub-analyses (e.g., `include_suggestions=False`). ### Individual API Functions For granular control, import specific functions from the API modules. See [references/api-reference.md](references/api-reference.md) for the complete list of 25 API functions with parameters, limits, and examples. ## Results Storage Results auto-save to `results/` with this structure: ``` results/ ├── keywords_data/ # Search volume, CPC, competition ├── labs/ # Suggestions, difficulty, intent ├── serp/ # Google/YouTube rankings ├── trends/ # Google Trends data └── summary/ # Human-readable markdown summaries ``` ### Managing Results ```python from core.storage import list_results, load_result, get_latest_result # List recent results files = list_results(category="labs", limit=10) # Load a specific result data = load_result(files[0]) # Get most recent result for an operation latest = get_latest_result(category="labs", operation="keyword_suggestions") ``` ### Utility Functions ```python from main import get_recent_results, load_latest # List recent files across all categories files = get_recent_results(limit=10) # Load latest result for a category data = load_latest("labs", "keyword_suggestions") ``` ## Creating Summaries After running research, create a markdown summary document in `results/summary/`. Include: - **Data tables** with volumes, CPC, competition, difficulty - **Ranked lists** of opportunities (sorted by volume or opportunity score) - **SERP analysis** showing what currently ranks - **Recommendations** for content strategy, titles, tags Name the summary file descriptively (e.g., `results/summary/ai-tools-keyword-research.md`). ## Tips 1. **Be specific** — "Get keyword suggestions for 'AI website builders'" works better than "research AI stuff" 2. **Request summaries** — Always create a summary document after research, named specifically 3. **Batch related keywords** — Pass multiple related keywords at once for comparison 4. **Specify the goal** — "for a YouTube video" vs "for a landing page" changes which data matters most 5. **Ask for competition analysis** — "Show me what videos are ranking" helps identify content gaps ## Defaults - **Location**: United States (code 2840) - **Language**: English - **API Limits**: 700 keywords for volume/overview, 1000 for difficulty/intent, 5 for trends, 200 for keyword ideas


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