Web Optimization

Web Optimization (SEO · GEO · AEO)

This skill treats SEO, GEO, and AEO as three lenses over one shared body of web-optimization practice. It supports two workflows: Audit an existing page, and Generate new optimized content.

The three lenses (what each optimizes for)

| Lens | Optimizes for | Load file | |------|---------------|-----------| | SEO | Google/Bing ranking (blue links) | references/seo.md | | GEO | LLM synthesis (ChatGPT, Perplexity, AI Overviews) | references/geo.md | | AEO | Answer engines, featured snippets, voice | references/aeo.md |

Shared support files:

• references/schema.md — JSON-LD patterns (load whenever structured data is involved).
• references/scoring.md — the rubric and priority weighting used by both workflows.

> Progressive disclosure: Do NOT read every reference up front. Read only the > lens file(s) relevant to the request, plus scoring.md for audits and > schema.md when structured data is in play.

Workflow A — Audit

Use when given a URL or a local content file and asked to evaluate/improve it.

1. Acquire the content.

• URL: prefer python scripts/audit.py for deterministic, objective

checks (returns JSON). Also fetch the rendered content to judge quality.

• Local file (.html/.md): read it directly; run audit.py --file .

1. Apply the three lenses. Read references/seo.md, geo.md, aeo.md and

evaluate the content against each. Fold in the JSON facts from audit.py.

1. Score & prioritize using references/scoring.md (P0 blocking → P3 nice-to-have).
2. Emit the report using assets/audit-report-template.md: every finding =

issue · lens · impact · concrete fix. Lead with the prioritized action list.

Workflow B — Generate

Use when given a topic/brief + target keyword or user intent and asked to produce new content.

1. Clarify intent — target query, audience, and primary lens emphasis if any

(default: optimize for all three).

1. Draft applying all three lenses: answer-first structure (AEO), keyword/

intent coverage and clean heading hierarchy (SEO), high information density with concrete measurable claims (GEO).

1. Attach structured data — pick the right JSON-LD from

assets/schema-templates/ per references/schema.md (Article + FAQPage are the common pair).

1. Produce an llms.txt entry from assets/llms-txt-template.md.
2. Output the content + JSON-LD + llms.txt entry + a short "why this is

optimized" rationale mapping choices back to the three lenses.

Workflow C — Fix (interactive step-by-step)

Use when the user says "fix [URL]", "fix the issues", or "apply the fixes" after an audit — or wants to be guided through improvements interactively.

1. Run or recall the audit. If an audit was just completed, use those findings.

Otherwise run Workflow A first silently, then begin fixing.

1. Load references/fix-playbook.md. It maps every finding type to the

minimum question(s) to ask and what to generate.

1. Work P0 → P1 → P2 → P3, one fix per turn:

• State: what you're fixing and why (one line).
• Ask: the minimum question(s) needed (often zero — derive from fetched content).
• Generate: the complete, ready-to-paste output (JSON-LD block, file content,

rewritten copy, HTML snippet, diff of tag changes).

• Confirm: "Fix N of M done — move to the next one? (or say skip / stop)"

1. Maintain a session checklist (✅ done / ⏭ skipped / ⬜ pending) and show

it at the top of each turn so the user always knows where they are.

1. On "stop" or "done": show the full checklist summary and remind the user

of any skipped items.

> Key principle: Never ask the user to write anything manually. Every output > should be copy-paste ready. If you need a number or URL you can't derive, > ask for that one thing — then generate everything else yourself.

Quick rules of thumb

• Information density beats word count. Replace hedges ("might be fast") with

measurable claims ("sub-50ms p95 latency"). LLMs cite specifics.

• Answer the question in the first sentence, then elaborate — this serves

snippets, voice, and LLM extraction simultaneously.

• Structure for two readers: a human operator and a natural-language parser.

Tables, lists, and JSON-LD help the parser without hurting the human.

• Authority is distributed. Off-domain mentions (GitHub, Reddit, citations)

matter for GEO trust, not just on-page factors.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: staksoft
• Source: staksoft/geo-seo-aeo-skill
• License: MIT
• Homepage: https://www.staksoft.com

Install and usage instructions live in the source repository linked above.