SEO Skill (Agentic / Claude / Codex)

LLM-first SEO analysis skill with 16 specialized sub-skills, 10 specialist agents, and 89 scripts for website, blog, and GitHub repository optimization.

Deterministic Trigger Mapping

For prompt reliability in Codex/agent IDEs, map common user wording to a fixed workflow:

• If user says perform seo analysis on <url> (or similar generic SEO request with a URL), treat it as a single-URL full audit.
• If no explicit sub-skill is specified, run the full/page audit path with LLM-first reasoning and script-backed evidence.
• For full/page audits, always produce:
  • FULL-AUDIT-REPORT.md (detailed findings)
  • ACTION-PLAN.md (prioritized fixes)
• If generate_report.py is run, also return the saved HTML path (for example SEO-REPORT.html).

Available Commands

Orchestration Logic

When the user requests SEO analysis, follow this routing:

Step 1 — Identify the Task

Parse the user's request to determine which sub-skill(s) to activate:

• Full audit: Read resources/skills/seo-audit.md — crawl multiple pages, delegate to agents, score and report
• Single page: Read resources/skills/seo-page.md — deep dive on one URL
• Specific area: Read the matching resources/skills/seo-*.md file
• Strategic plan: Read resources/skills/seo-plan.md and the matching resources/templates/*.md for the detected industry
• GitHub repository SEO: Read resources/skills/seo-github.md and use GitHub scripts with --provider auto for API/gh fallback.
• Generic perform seo analysis on <url> request: treat as single-page full audit, read resources/skills/seo-page.md, and generate FULL-AUDIT-REPORT.md + ACTION-PLAN.md.

Step 2 — Collect Evidence

Primary method (LLM-first) — use the built-in read_url_content tool first:

read_url_content(url)  →  returns parsed HTML content directly

Use this as the baseline evidence for reasoning.

Deterministic verification (recommended when script execution is available):

# Fetch/parse raw HTML for structured checks
python3 <SKILL_DIR>/scripts/fetch_page.py <url> --output /tmp/page.html
python3 <SKILL_DIR>/scripts/parse_html.py /tmp/page.html --url <url> --json

# Optional: generate shareable HTML dashboard artifact
python3 <SKILL_DIR>/scripts/generate_report.py <url> --output SEO-REPORT.html

Do not use third-party mirrors (e.g., r.jina.ai) as primary evidence when direct site fetch or bundled scripts are available. <SKILL_DIR> = absolute path to this skill directory (the folder containing this SKILL.md).

Step 3 — Perform LLM-First Analysis

Use the LLM as the primary SEO analyst:

1. Synthesize evidence from page content, metadata, and optional script outputs.
2. Produce findings with explicit proof:
   • Finding
   • Evidence (specific element, metric, or snippet)
   • Impact (why it matters for ranking/indexing/UX)
   • Fix (clear implementation step)
3. Prioritize by impact and implementation effort.
4. Separate confirmed issues, likely issues, and unknowns (missing data).

Always read and apply resources/references/llm-audit-rubric.md to keep scoring, severity, confidence, and output structure consistent across audit types.

Step 4 — Run Baseline Verification Scripts (When execution is available)

For full/page audits, run baseline checks to avoid hypothesis-only reporting. Do not replace LLM reasoning with script-only scoring.

# Check robots.txt and AI crawler management
python3 <SKILL_DIR>/scripts/robots_checker.py <url>

# Check llms.txt for AI search readiness
python3 <SKILL_DIR>/scripts/llms_txt_checker.py <url>

# Get Core Web Vitals from PageSpeed Insights (free API, no key needed)
python3 <SKILL_DIR>/scripts/pagespeed.py <url> --strategy mobile

# Check security headers (HSTS, CSP, X-Frame-Options, etc.)
python3 <SKILL_DIR>/scripts/security_headers.py <url>

# Detect broken links on a page (404s, timeouts, connection errors)
python3 <SKILL_DIR>/scripts/broken_links.py <url> --workers 5

# Trace redirect chains, detect loops and mixed HTTP/HTTPS
python3 <SKILL_DIR>/scripts/redirect_checker.py <url>

# Analyze readability from fetched HTML (Flesch-Kincaid, grade level, sentence stats)
python3 <SKILL_DIR>/scripts/readability.py /tmp/page.html --json

# Validate Open Graph and Twitter Card meta tags
python3 <SKILL_DIR>/scripts/social_meta.py <url>

# Analyze internal link structure, find orphan pages
python3 <SKILL_DIR>/scripts/internal_links.py <url> --depth 1 --max-pages 20

# Extract article content and perform keyword research for LLM-driven optimization
python3 <SKILL_DIR>/scripts/article_seo.py <url> --keyword "<optional_target_keyword>" --json

# Credentials for paid/auth APIs (PageSpeed, GitHub, GSC, Knowledge Graph)
# are loaded from CLI flags, then env vars, then a `.env` file in the repo
# root / cwd / `~/.agentic-seo/.env`. Copy `.env.example` to `.env` and fill
# in only the keys you have. Never paste secrets in prompts.

# GitHub repository SEO (provider fallback: auto|api|gh)
# Auth setup (choose one):
# export GITHUB_TOKEN="ghp_xxx"   # or export GH_TOKEN="ghp_xxx"
# gh auth login -h github.com && gh auth status -h github.com
python3 <SKILL_DIR>/scripts/github_repo_audit.py --repo <owner/repo> --provider auto --json
python3 <SKILL_DIR>/scripts/github_readme_lint.py README.md --json
python3 <SKILL_DIR>/scripts/github_community_health.py --repo <owner/repo> --provider auto --json
# Benchmark/competitor inputs should be provided by LLM/web-search discovery when possible.
# If omitted, github_seo_report.py auto-derives repo-specific benchmark queries.
python3 <SKILL_DIR>/scripts/github_search_benchmark.py --repo <owner/repo> --query "<llm_or_web_query>" --provider auto --json
python3 <SKILL_DIR>/scripts/github_competitor_research.py --repo <owner/repo> --query "<llm_or_web_query>" --provider auto --top-n 6 --json
python3 <SKILL_DIR>/scripts/github_competitor_research.py --repo <owner/repo> --competitor <owner/repo> --competitor <owner/repo> --provider auto --json
python3 <SKILL_DIR>/scripts/github_traffic_archiver.py --repo <