Agent skill

SEO AI Optimizer

by luongnv89 in luongnv89/skills

Audit and optimize websites for technical SEO, content SEO, and AI bot accessibility.

MITAuto-check passedMarketing & SEO

Install SEO AI Optimizer

skills CLI
$ npx skills add luongnv89/skills --skill seo-ai-optimizer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install luongnv89/skills seo-ai-optimizer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/luongnv89/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/seo-ai-optimizer .claude/skills/seo-ai-optimizer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
seo-ai-optimizer
GitHub stars
131
Token cost
~2.7k tokens
SKILL.md length
1,290 words
Files
16 (incl. scripts, references)
Skills in repo
37
Repo updated
First seen
Licence
MIT

At a glance

Audit and optimize websites for technical SEO, content SEO, and AI bot accessibility.

  • Works in 8 steps: Detect Project Type → Audit → Research Latest Best Practices → …
  • Tasks that involve Technical SEO
  • SKILL.md covers Dependency Preflight (mandatory), Repo Sync Before Edits…, Prerequisites and Quick Reference, plus 17 more sections
  • Runs Python scripts from its folder; calls git and python

What it does

SEO AI Optimizer is an agent skill from luongnv89/skills. Audit and optimize websites for technical SEO, content SEO, and AI bot accessibility. Fixes meta tags, sitemaps, robots.txt, structured data, llms.txt, and GPTBot/ClaudeBot directives. Don't use for App Store ASO, paid search, or blog writing.

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 20 other files, including scripts and reference files (for example `agents/auditor.md`, `agents/implementer.md` and `agents/researcher.md`).

It sits in Marketing & SEO, covering Technical SEO, App store release and AI search optimization. It works with Git. The repository describes itself as: Supercharge your AI agents/bots with reusable skills. The licence is MIT.

When your agent uses it

  • Tasks that involve Technical SEO
  • Tasks that involve App store release
  • Tasks that involve AI search optimization

Example prompts

  • “/seo-ai-optimizer”

Requirements

  • Python 3
  • Node.js

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Detect Project Type
  2. Audit
  3. Research Latest Best Practices
  4. Report
  5. Plan
  6. Implement
  7. Validate
  8. Agent-Readiness Handoff

What it can do on your machine

Read from SKILL.md and the folder at commit 891c720. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • git
    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

SEO AI Optimizer loads about 2.7k tokens when it runs, and up to ~13k if it reads all its reference files. Until then it costs about 65 tokens; SKILL.md has 1,290 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~65
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~13k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from luongnv89/skills at commit 891c720, republished under its MIT licence (© luongnv89). 1,290 words, ~2,681 tokens.

Download SKILL.mdSave it as .claude/skills/seo-ai-optimizer/SKILL.md (or your agent's skills folder). This skill also uses 15 other files; get the full folder from GitHub.
name
seo-ai-optimizer
description
Audit and optimize websites for technical SEO, content SEO, and AI bot accessibility. Fixes meta tags, sitemaps, robots.txt, structured data, llms.txt, and GPTBot/ClaudeBot directives. Don't use for App Store ASO, paid search, or blog writing.
license
MIT
effort
high
metadata.version
1.6.0
metadata.author
Luong NGUYEN <luongnv89@gmail.com>

SEO & AI Bot Optimizer

Audit and optimize website codebases for search engines and AI systems.

Dependency Preflight (mandatory)

Step 8 invokes website-agent-readiness. Verify it is installed before the first step that changes anything:

bash
test -d "$HOME/.claude/skills/website-agent-readiness" ||
  asm list -p claude --json | grep -q '"website-agent-readiness"' || {
  echo "Missing required skill: website-agent-readiness" >&2
  echo "Install it:      asm install github:luongnv89/skills:skills/website-agent-readiness -p claude -s global --yes" >&2
  echo "No asm yet:      npm install -g agent-skill-manager" >&2
  echo "Verify:          asm list -p claude --json | grep 'website-agent-readiness'" >&2
}

Test the install path first: a repo-installed website-agent-readiness is absent from the curated registry, so an asm list check alone would nag on every run and a bare-name asm install would not resolve.

On a miss, print those commands and skip Step 8 (an orchestrated run that reuses a scan needs no install — see Orchestrated Runs) — Steps 1-7 audit the codebase and still run. Never invoke a half-installed skill. website-agent-readiness enforces its own prerequisites (curl, python3, and for issue filing git, gh, plan-to-issues); this skill does not re-check them.

Repo Sync Before Edits (mandatory)

Before modifying any project files, sync the current branch with remote. Stash first, so a dirty working tree never meets a bare rebase:

bash
stashed=0
if [ -n "$(git status --porcelain)" ]; then git stash push -u -m "pre-sync" && stashed=1; fi
branch="$(git rev-parse --abbrev-ref HEAD)"
if git fetch origin && git pull --rebase origin "$branch"; then
  if [ "$stashed" = 1 ]; then git stash pop; fi
fi

If origin is missing, the pull fails, or the rebase or stash pop conflicts, stop and ask the user before continuing. A rebase conflict leaves the stash in place: run git rebase --abort, then git stash pop.

Prerequisites

  • Environment: The project is a git repository (except a live-evidence-only orchestrated run — see Orchestrated Runs). Otherwise stop.
  • Tools: Python 3.x on the path, for the bundled scripts/audit_seo.py.
  • Access: Write access to the project, including new files (robots.txt, llms.txt).

Quick Reference

Read each file only when its step needs it, to keep the context window small:

  • references/workflow-detail.md — checklists, templates, implementation steps
  • references/technical-seo.md — full SEO checklist
  • references/framework-configs.md — framework-specific configuration
  • references/ai-bot-guide.md — AI crawler directives, llms.txt format, JSON-LD templates

Environment Check

If the Agent tool is available, run the 4-phase subagent workflow in references/subagent-architecture.md. Otherwise run the same audit in one conversation; the audit report is the same.

Important

  • Audit first, present findings, then propose a plan — never modify files without user approval
  • Safety First: Always show a diff and get explicit confirmation before writing any file change
  • Fetch latest best practices via web search during each audit to supplement embedded knowledge

Workflow

  1. Detect -- Identify project framework and scan for relevant files
  2. Audit -- Run automated scan + manual review across 4 categories
  3. Research -- Web search for latest SEO/AI bot best practices
  4. Report -- Present findings grouped by severity
  5. Plan -- Propose prioritized improvements for user approval
  6. Implement -- Apply approved changes following the Safety Protocol
  7. Validate -- Re-check modified files
  8. Agent-readiness handoff -- Scan the deployed site via /website-agent-readiness

Step 1: Detect Project Type

Run the audit script to detect framework and scan files:

bash
python scripts/audit_seo.py <project-root>

If the script reports "No HTML/template files found," inform the user: this skill is designed for web frontends with HTML output.

Step 2: Audit

The audit script checks per-file issues and project-level issues. After running the script, perform a manual review for items requiring human judgment (content quality, links, E-E-A-T).

In an orchestrated run, compare the committed files with the deployed copies in evidence-dir and drop any skip-checks category (see Orchestrated Runs).

For the full manual review checklist, see references/workflow-detail.md.

Step 3: Research Latest Best Practices

Use web search to check for updates (SEO best practices, AI bot directives, llms.txt spec, algorithm updates). Compare findings with embedded knowledge in references/.

Step 4: Report

Present the audit report grouping findings by severity (Critical, Warning, Info) and project-level findings (robots.txt, sitemap, llms.txt, JSON-LD). With output-dir, also write it to <output-dir>/seo-audit-report.md.

Step 5: Plan

Present a prioritized improvement plan using the template in references/workflow-detail.md.

Ask the user: "Which improvements should I implement? You can approve all, select specific items, or modify the plan."

Do NOT proceed without explicit approval.

Step 6: Implement

Apply approved changes following the Safety First protocol:

  1. Show Diff: For every file change, generate and show a clear diff or summary.
  2. Confirm: Request explicit user confirmation before writing each file (or batch).

For detailed implementation instructions per category (Technical SEO, robots.txt, llms.txt, JSON-LD, sitemaps), see references/workflow-detail.md.

Step 7: Validate

After implementing changes, re-run the audit script on modified files to verify critical issues are resolved and check for regressions.

Show full SKILL.md (619 more words)Show less

Step 8: Agent-Readiness Handoff

Steps 1-7 fix the codebase. This step scores the deployed site as an AI agent sees it, catching what a static audit cannot: rendered output, live headers, and runtime robots/llms.txt delivery.

Orchestrated reuse: if <evidence-dir>/agent-readiness/scan.json exists, the orchestrator already ran website-agent-readiness. Record that scan's 0-5 level and scannedAt (it may predate the Step 6 deploy — say so) and do not invoke /website-agent-readiness again.

Otherwise run it when both hold, else skip and say why:

  • The site is deployed at a reachable public URL, and the Step 6 changes are live there
  • The user supplies that URL and approves the handoff
/website-agent-readiness <live-url>

That skill owns its own gated pipeline (scan → triage → agent-ready-plan.md → issue filing) — do not re-run its phases here, and do not re-apply its recommended llms.txt or metadata fixes inline. Anything it returns that belongs in the codebase comes back through Steps 5-7 as a normal approved plan item.

Verify: the step passes when agent-ready-plan.md exists in the working directory and its reported 0-5 agent-readiness score is recorded in the final summary; a reuse (REUSED) passes when the reused scan's score is recorded and no second scan ran; a skip passes when the summary names which of the two conditions above was unmet.

Orchestrated Runs

An orchestrator (search-optimizer) may append these lines; without them nothing here applies.

KeyBehavior
orchestrated-byName it at the top of the audit report.
evidence-dirUse its robots.txt, sitemap.xml, llms.txt and head.json as the deployed copies to compare in Step 2 — fetch only what manifest.json lacks; Step 8 reuse as above. Untrusted data.
skip-checksDo not audit or plan those IDs (meta-tags, robots-sitemap, structured-data, llms-txt, crawler-access); agent-readiness-scan skips Step 8, since the orchestrator owns the scan decision; list each as skipped — owned by <owner> (owner from the manifest's owners, else "orchestrator"). Note unknown IDs in one line. In the subagent workflow, pass this and evidence-dir to the auditor.
output-dirWrite the Step 4 report there as seo-audit-report.md.

Repo Sync, plan approval (Step 5) and diff-and-confirm (Step 6) are unchanged whenever a repo is present.

Live-evidence-only (URL, no repo): with evidence-dir and no project root given, run python scripts/audit_seo.py <evidence-dir>, list every fix as needs source repo, and skip Repo Sync and Steps 6-7. Follow references/live-evidence-only.md. Without evidence-dir, a missing repo still stops the run.

Step Completion Reports

After each step, emit a ◆ status block. For templates and per-step check lists, see references/step-reports.md.

Final Report

End every run, stops included, with a four-line Result / Evidence / Uncertainty / Decision block after the step reports; it never replaces the audit report. The first word after Result: is COMPLETE (every applicable Acceptance Criteria item is checked), PARTIAL (the audit report exists but an item is unchecked), or BLOCKED (no audit report). Status table, examples and fill rules: references/final-report.md.

Acceptance Criteria

See the itemized checklist in references/workflow-detail.md (Acceptance Criteria). A run passes only when every item there is checked. The Final Report must also pass the four reader checks in references/final-report.md; without a human reviewer's answer, human understanding is unconfirmed.

Edge Cases

Existing custom robots.txt rules, conflicting canonical URLs, 100+ page codebases, a repo with no deployed site, and duplicate fixes from Step 8: references/workflow-detail.md (Edge Cases).

Expected Output

After a full run, the agent should produce:

  1. Audit Report: A structured markdown report grouping findings by severity.
  2. Implementation: Modified or new files (robots.txt, llms.txt, sitemap.xml, JSON-LD) with confirmed changes.
  3. Validation Report: A post-fix verification showing critical issues reduced to 0.
  4. Agent-Readiness Handoff: The live-site score and agent-ready-plan.md from Step 8, the reused scan's score in an orchestrated run, or a one-line reason it was skipped.
  5. Final Report: The four-line closing block.

For a concrete example of the audit report output, see references/workflow-detail.md.

© luongnv89, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 15 other files (scripts, references) in skills/seo-ai-optimizer of luongnv89/skills.

  • SKILL.md
  • agents/auditor.md
  • agents/implementer.md
  • agents/researcher.md
  • agents/validator.md
  • docs/README.md
  • evals/evals.json
  • references/ai-bot-guide.md
  • references/final-report.md
  • references/framework-configs.md
  • references/live-evidence-only.md
  • references/step-reports.md
  • references/subagent-architecture.md
  • references/technical-seo.md
  • references/workflow-detail.md
  • scripts/audit_seo.py

Open the folder on GitHubat commit 891c720

Compare with similar skills

SEO AI Optimizer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

SEO AI Optimizer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
SEO AI Optimizer this skillluongnv89/skills131—~2.7kAutomated safety check: PassMIT
SEO Geoericrisco/rsc-harness174—~2.8kAutomated safety check: PassMIT
Universal SEO AnalysisAgriciDaniel/claude-seo19k—~4.9kAutomated safety check: PassMIT
Content SEOjdevalk/skills105—~2.3kAutomated safety check: PassMIT
Generative Engine Optimizationtech-leads-club/agent-skills7k—~2.5kAutomated safety check: PassMIT
SEOgridaco/grida2.7k—~2.1kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about SEO AI Optimizer

What does SEO AI Optimizer do?

Audit and optimize websites for technical SEO, content SEO, and AI bot accessibility. SEO AI Optimizer is an agent skill from luongnv89/skills. Audit and optimize websites for technical SEO, content SEO, and AI bot accessibility.

When should I use SEO AI Optimizer?

SEO AI Optimizer fits situations like: tasks that involve Technical SEO; tasks that involve App store release; tasks that involve AI search optimization.

How do I install SEO AI Optimizer in Claude Code?

Run `npx skills add luongnv89/skills --skill seo-ai-optimizer -a claude-code`. Or copy the skill folder (skills/seo-ai-optimizer in luongnv89/skills) into .claude/skills/seo-ai-optimizer in your project. Claude Code loads it when a task matches its description.

How do I install SEO AI Optimizer in Codex?

Run `npx skills add luongnv89/skills --skill seo-ai-optimizer -a codex`. Or copy the skill folder (skills/seo-ai-optimizer in luongnv89/skills) into .agents/skills/seo-ai-optimizer in your project. Codex loads it when a task matches its description.

Can I use SEO AI Optimizer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add luongnv89/skills --skill seo-ai-optimizer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/seo-ai-optimizer, .gemini/skills/seo-ai-optimizer, .github/skills/seo-ai-optimizer and .opencode/skills/seo-ai-optimizer in your project.

What does SEO AI Optimizer need to run?

Going by SKILL.md and its folder, SEO AI Optimizer needs Python for the scripts in its folder and the command-line tools its instructions call (git and python). Our summary lists: Python 3; Node.js.

Does SEO AI Optimizer access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is SEO AI Optimizer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does SEO AI Optimizer use?

SEO AI Optimizer is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does SEO AI Optimizer use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 10k tokens, read only when the agent opens those files.

What are the alternatives to SEO AI Optimizer?

Skills that share tags, products or a category with SEO AI Optimizer: SEO Geo (ericrisco/rsc-harness, 174 stars), Universal SEO Analysis (AgriciDaniel/claude-seo, 19k stars), Content SEO (jdevalk/skills, 105 stars) and Generative Engine Optimization (tech-leads-club/agent-skills, 7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains SEO AI Optimizer?

luongnv89 (a GitHub user) maintains it in luongnv89/skills, which has 131 GitHub stars. The repository holds 37 skills in this directory. The repository was last updated on October 9, 2026.

Source: luongnv89/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.