mkt-seo-ops

name	mkt-seo-ops
description	AI-powered SEO operations with keyword intelligence, competitor gap analysis, GSC optimization, and trend detection. Use for keyword research, content briefs, quick-win keyword identification, competitor gaps, trending topics, and decaying content analysis.

AI SEO Ops

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