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.

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

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

SEO/AEO/GEO Closed Loop

Use analytics readbacks before promoting any SEO, AEO, GEO, or content-refresh playbook change.

Inputs: - GSC clicks, impressions, CTR, average position, queries, pages - GA4 sessions, engaged sessions, conversions, assisted leads - Ahrefs rankings, backlinks, traffic estimates, keyword movement - ClickFlow opportunities where available - AI-search / answer-engine / GEO visibility where available - CMS/page change log

Judgment: - Compare baseline vs candidate windows. - Segment by page, query, topic, intent, and source. - Track confounders: seasonality, indexing lag, brand spikes, campaigns, tracking changes, and unrelated site edits.

Promotion rule: - Promote the playbook patch only if the candidate beats baseline or exposes a repeatable signal. - Otherwise mark it unproven, keep testing, or rollback.

Common readback windows: - Content refresh: 7, 14, 28, and 56 days - New content: 14, 28, 56, and 90 days - Technical SEO fix: daily for 7 days, then 28-day readback - AEO/GEO visibility: weekly, because answer engines are noisy gremlins with citations

Required readback fields: - change made - owner - page/query/topic affected - baseline window - candidate window - source systems pulled - primary and secondary metrics - caveats - decision: promote / keep testing / rollback / unproven - next playbook patch

Dependencies

pip install -r requirements.txt

SEO Ops