Seo priority score
Rank SEO tasks by expected impact, confidence, effort, authority feasibility, and revenue proximity. Use when Codex needs to decide which of many identified issues to work on first.From its SKILL.md
npx -y skills add marketingskills/seo --skill seo-priority-scoreAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
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SEO Priority Score
Overview
Given a list of potential SEO tasks (from any Diagnose skill), score each one on a standardized framework and produce a ranked priority list.
Inputs
- List of SEO tasks or opportunities with supporting data
- Optional: business context (revenue per visitor, conversion rates, margins)
Workflow
- For each task, collect or estimate:
- Impact: traffic, conversion, or revenue potential (scale 1-10)
- Confidence: how reliable the signal is (scale 1-5)
- Effort: hours or cost to implement (scale 1-5, inverted)
- Authority feasibility: can the site realistically compete (scale 1-5)
- Revenue proximity: how directly this affects pipeline (scale 1-5)
- Calculate a composite priority score:
(Impact × Confidence) / (Effort × Authority gap)with revenue proximity as a multiplier. - Group tasks by effort band: quick wins (< 4 hours), medium projects (4-20 hours), strategic initiatives (20+ hours).
- Output a sorted priority list with the score breakdown.
Output
Ranked task list with priority score, breakdown, and effort band.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 0 of the 12 instructions most marketing audience skills give in 260 tokens
Counted across 690 of the 894 authors here whose files we hold, read 2026-08-07
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Said here and by no other author read
- Collect or estimate impact for each SEO task
- Collect or estimate confidence for each SEO task
- Collect or estimate authority feasibility for each SEO task
- Collect or estimate revenue proximity for each SEO task
- Calculate a composite priority score
- Group tasks by effort band
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.