seo-ops

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name: seo-ops description: "SEO operations automation: generate content attack briefs from competitor gaps, optimize GSC data for quick wins, and scout emerging search trends. Use when asked to "SEO audit", "content attack brief", "GSC optimization", "find keyword gaps", "trending topics for SEO", or "search console analysis"." description_zh: "SEO 运营自动化，内容攻击简报、GSC 优化与趋势侦察" description_en: "SEO operations: content attack briefs, GSC optimization, and trend scouting" version: 1.0.0 homepage: https://github.com/ericosiu/ai-marketing-skills

AI SEO Ops

Preamble (runs on skill start)

# Version check (silent if up to date)
python3 telemetry/version_check.py 2>/dev/null || true

# Telemetry opt-in (first run only, then remembers your choice)
python3 telemetry/telemetry_init.py 2>/dev/null || true

Privacy: This skill logs usage locally to ~/.ai-marketing-skills/analytics/. Remote telemetry is opt-in only. No code, file paths, or repo content is ever collected. See telemetry/README.md.

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AI-powered SEO operations: keyword intelligence, competitor gap analysis, GSC optimization, and trend detection.

When to Use

• User asks for keyword research, content brief, or SEO analysis
• User wants to find quick-win keywords from Google Search Console
• User needs a competitor gap analysis
• User wants to identify trending topics for content creation
• User asks about decaying content or traffic drops
• User wants a prioritized list of keywords to target

Tools

Content Attack Brief (content_attack_brief.py)

Full keyword intelligence pipeline. Requires AHREFS_TOKEN and GSC auth.

# Run the full brief
python content_attack_brief.py

What it produces:

• Topic fingerprint from your content library
• BOFU money keywords ranked by Impact × Confidence
• Trending keywords with sparkline visualizations
• Competitor gap analysis (keywords they rank for, you don't)
• Decaying page alerts (traffic drops >30%)
• Execution pipeline (auto-create → semi-auto → team)

Output: Prints formatted report to stdout + saves JSON to OUTPUT_DIR/content-attack-brief-latest.json

GSC Client (gsc_client.py)

Google Search Console API client. Works as CLI or importable library.

# CLI usage
python gsc_client.py --queries 50 --days 28
python gsc_client.py --striking                    # Striking distance keywords (pos 4-20)
python gsc_client.py --pages 100 --days 7
python gsc_client.py --trend                       # Daily click/impression trend
python gsc_client.py --devices                     # Mobile vs desktop split
python gsc_client.py --sites                       # List verified properties
python gsc_client.py --json --queries 25           # JSON output

# Library usage
from gsc_client import GSCClient

gsc = GSCClient()
rows = gsc.striking_distance(days=28, min_position=4, max_position=20)
for row in rows:
    print(f"{row['keys'][0]}: pos {row['position']:.1f}, {row['impressions']} impressions")

GSC Auth (gsc_auth.py)

One-time OAuth setup for Google Search Console access.

python gsc_auth.py
# Opens browser → Google Sign-In → saves token locally

Trend Scout (trend_scout.py)

Multi-source trend detection. No API keys required for basic functionality.

python trend_scout.py

Sources: Google Trends RSS, Hacker News, Reddit, X/Twitter (needs BRAVE_API_KEY), YouTube outlier detection

Output: Prints summary + saves JSON to OUTPUT_DIR/flash-trends-latest.json and markdown report.

Configuration

All scripts read from environment variables. Copy .env.example to .env and fill in your values.

Required:

• GSC_SITE_URL — your Google Search Console property URL
• GOOGLE_CLIENT_ID / GOOGLE_CLIENT_SECRET — for GSC OAuth
• YOUR_DOMAIN — your root domain

Optional:

• AHREFS_TOKEN — enables Ahrefs keyword data and competitor analysis
• COMPETITORS — comma-separated competitor domains
• BRAVE_API_KEY — enables X/Twitter trend scanning
• CONTENT_VERTICALS — comma-separated topics for trend relevance scoring
• TREND_SUBREDDITS — comma-separated subreddits to monitor

Scoring Model

Keywords are scored on two axes:

Impact (0-10): Volume + CPC + Funnel Stage + Trend direction Confidence (0-10): Keyword Difficulty + Current ranking position + Topic authority

Priority = Impact × Confidence (max 100)

Funnel Classification

• BOFU: Commercial/transactional intent, or keywords containing "agency", "services", "pricing", "best", "vs", "hire"
• MOFU: Informational with buying signals — "how to", "guide", "roi", "case study"
• TOFU: Pure informational

Recommended Workflow

1. Weekly: Run content_attack_brief.py for the full intelligence report
2. Daily: Run gsc_client.py --striking to monitor striking distance keywords
3. 2x/week: Run trend_scout.py to catch trending topics early
4. Monthly: Review competitor gaps and adjust COMPETITORS list

Dependencies

pip install -r requirements.txt