fire-your-seo-agency

🔥 fire-your-seo-agency

한국어 · English

Paying $400–$2,500/month for an SEO or "AI visibility" agency? Fire them. Your AI agent can do the work.

A wave of services now charges monthly retainers for "AI-era search optimization", "guaranteed ChatGPT citations", and "LLM visibility". Most of what they actually do is public standards and repeatable checklists. When a human does it, it's a retainer. When your agent does it, it's a skill.

This repository is a Claude Code skill. Install it and your agent audits your site, fixes what's broken, and measures the results.

Built on real numbers

This is not theory. The same playbook, applied to Chickenstock — a solo-built Korean stock research service:

• 1.54M search impressions in 30 days (+85,578% MoM), 7.4K clicks
• Pages cited paragraph-by-paragraph by Naver's AI Briefing (Korea's answer engine)
• Zero ad spend — all inbound from search and AI citations

The journey is documented publicly on Threads @kindainvestor.

The five lanes

"Search optimization" means different things to different engines. This skill treats them as five distinct lanes:

Lane	Target	The question it answers
SEO	Google & Bing crawlers	Can crawlers read and index my content at all?
AEO (Answer Engine)	Google AI Overviews, Bing Copilot	Does the AI answer box above the results cite me?
GEO (Generative Engine)	ChatGPT, Perplexity, Claude	When generative AI browses, am I the primary source?
LLMO (LLM Optimization)	The model's own knowledge	Does the model know my brand — and know it correctly?
NEO (Naver Engine)	Naver search & AI Briefing	Half of the Korean market — does Naver cite me?

NEO is what makes this skill different. Global AEO guides ignore Naver entirely, but if you serve the Korean market, half your traffic lives there.

Plus a content engine. Most of a retainer pays for "N posts a month". This skill replaces that with a pipeline: a question backlog pulled from your own search data, a frontmatter content model that generates the JSON-LD, a publish gate every post must pass, and a refresh/merge policy so posts don't rot. Output is measured as "posts that get seen and cited", not "posts published".

Install

As a plugin (recommended — one command, easy updates):

/plugin marketplace add leopard627/fire-your-seo-agency
/plugin install fire-your-seo-agency@fire-your-seo-agency

Or via git clone:

# As a project skill (this project only)
git clone https://github.com/leopard627/fire-your-seo-agency.git .claude/skills/fire-your-seo-agency

# Or as a personal skill (every project)
git clone https://github.com/leopard627/fire-your-seo-agency.git ~/.claude/skills/fire-your-seo-agency

Then in Claude Code:

/fire-your-seo-agency audit my site

Your agent starts with a crawler-eye audit and returns a scorecard like this before touching anything:

Lane	Status	Evidence
SEO	⚠️	Body is SSR'd, but 214 detail pages missing from sitemap
AEO	❌	No direct-answer first paragraphs; FAQ structured data: 0
GEO	❌	No llms.txt; GPTBot/PerplexityBot policy undecided in robots.txt
LLMO	⚠️	Brand name spelled 3 different ways across surfaces
NEO	❌	Not registered in Naver Search Advisor

…then proposes priorities, implements them, and schedules the re-measurement.

What it does

1. Audit — reads your site the way a crawler does (no JavaScript) and scores all five lanes
2. Technical base — fixes SSR exposure, sitemaps, meta, structured data
3. Intent landing pages — designs pages on the "one question = one page" principle
4. Machine readability — llms.txt, JSON-LD, citation-ready paragraph structure
5. Naver — from Search Advisor registration to AI Briefing citation requirements
6. Content engine — sub-blog setup, question backlog, publish gate, refresh & merge policy
7. Measurement loop — schedules a re-measurement and proves the change with numbers

What it refuses to do

• ❌ Buying backlinks, engagement pods, content spam — we don't fight the search engine
• ❌ "Guaranteed rankings" promises — no claims without measurement
• ❌ Keyword stuffing, hidden text, cloaking — nothing that gets your domain killed

The whole philosophy fits in one line: AI doesn't cite good writing. AI cites accurate data. Become the primary source for a number, and the citations follow.

Structure

SKILL.md              ← agent operating procedure (audit → implement → measure)
references/
  seo.md              ← technical SEO checklist + real-world traps
  aeo.md              ← answer engine optimization (Bing WMT · AI Overviews · Copilot · E-E-A-T)
  geo.md              ← generative engine optimization (AI crawler policy · llms.txt · primary source)
  llmo.md             ← model-knowledge optimization (brand entity)
  neo-naver.md        ← Naver (Search Advisor · AI Briefing · blog two-track)
  content.md          ← content ops (sub-blog · content model · backlog · publish gate · refresh)
  measure.md          ← the measurement loop (fixing it is not the finish line)
  en/                 ← English mirrors of all reference docs (for human readers)
.claude-plugin/       ← plugin & marketplace manifests (/plugin install support)
tools/                ← repository checks (validate.py) + lossless asset optimizer
tests/                ← unit tests for the checks
.github/workflows/    ← CI: checks, tests and the asset check on every push/PR

The Korean documents under references/ are canonical (the agent reads those); references/en/ mirrors them in English for human readers.

Repository checks

This repo checks itself — .github/workflows/validate.yml runs all of it on every push and pull request:

python3 tools/validate.py --strict          # 10 checks, standard library only
python3 -m unittest discover -s tests -t .  # unit tests for every check
python3 -m pip install pillow
python3 tools/optimize_assets.py --check    # flags recompressible assets

tools/validate.py enforces the things that rot silently in a docs-only repo: the SKILL.md frontmatter contract (kebab-case name, 64-character name limit, 1024-character single-line description), plugin.json ↔ marketplace.json version and name agreement, CHANGELOG.md matching the manifest version, relative links and anchors, Korean/English reference parity (heading counts, table rows, checkboxes, section numbering), the asset budget and preview dimensions, the MIT license metadata, and a secret scan over every text file.

Star History

License

MIT — use it freely, and keep the retainer money.