External Link Audit

External Link Audit

Audit the external (outbound) links of a website and produce an actionable SEO report.

When to use this skill

Use this skill when the user asks to:

• Audit or analyze external/outbound links on a website
• Find broken external links (link rot) or redirected destinations
• Check affiliate, sponsored, or paid links for correct rel attribution
• Find insecure http:// outbound links or unsafe target="_blank" usage
• Review which external domains a site links to and how often

Inputs to collect

Before starting, confirm with the user:

1. Site URL or local files — a live site root URL (e.g., https://example.com), a sitemap URL, or a local folder of HTML files.
2. Scope — full site, a specific section (e.g., /blog/), or a list of URLs.
3. Crawl limits — max pages (default 500) and max depth (default 5) for live crawls.
4. Known paid/affiliate domains — domains that must carry rel="sponsored" (e.g., amzn.to, affiliate networks), if any.

Workflow

Step 1: Gather the page set and extract external links

• If a sitemap is available (/sitemap.xml), fetch it to get the canonical page list.
• Otherwise, crawl from the homepage following only same-host links.
• For local HTML folders, enumerate all .html files.

Use scripts/extract_external_links.py to crawl the site and collect every external link into a JSON inventory:

python3 scripts/extract_external_links.py https://example.com --max-pages 500 --max-depth 5 --output external_links.json
# already crawled once (e.g. in a full SEO audit)? skip the crawl and reuse the shared cache:
# python3 scripts/fetch_pages.py https://example.com --output page_cache.json
# python3 scripts/extract_external_links.py --from-cache page_cache.json --output external_links.json

For every external link, the inventory records: source page, target URL, target domain, anchor text, rel attributes, target attribute, and whether the link is in <nav>/<footer> or in main content.

Step 2: Check link targets and run the audit

Use scripts/check_external_links.py to resolve the HTTP status of each external target (deduplicated per URL) and run all audit checks:

python3 scripts/check_external_links.py external_links.json --output audit_report.json

Add --sponsored-domains amzn.to,partner.example to flag affiliate domains missing rel="sponsored". Add --skip-status-checks to run only the static checks without network requests.

Step 3: Evaluate the audit checks

Evaluate each check defined in references/audit-checks.md. The core checks are:

Step 4: Produce the report

Write a report following the structure in references/report-template.md:

1. Summary — pages crawled, external links found, unique domains, issue counts by severity
2. Domain overview — most-linked external domains with link counts and follow/nofollow split
3. Issues — one section per failing check, listing source page, target URL, anchor text, and a specific fix
4. Prioritized action list — ordered by severity and estimated impact

Step 5: Recommend fixes

For each issue, give a concrete, copy-pasteable recommendation, e.g.:

• Remove or replace the dead link https://gone.example/page on /blog/post-1; link to an archived copy or an equivalent live resource
• Update http://partner.example on /resources to https://partner.example
• Add rel="sponsored" to the affiliate link https://amzn.to/xyz on /reviews/widget
• Add rel="noopener" to the target="_blank" link on /about

Optional: export the report

As a Word document (.docx)

If the user wants the report as a .docx (for example, to share with stakeholders or attach to a ticket), save the Markdown report to a file and convert it:

python3 scripts/md_to_docx.py report.md --output report.docx

scripts/md_to_docx.py uses only the Python standard library (no pip install) and renders headings, tables, lists, links, bold/italic, and code blocks. Offer this whenever a user asks for a Word doc, a .docx, or a shareable/downloadable report.

As a CSV of findings (.csv)

If the user wants the raw findings as a spreadsheet (for filtering, sorting, or triage in Sheets or Excel), convert the audit's audit_report.json directly:

python3 scripts/findings_to_csv.py audit_report.json --output findings.csv

scripts/findings_to_csv.py is also standard-library only. It writes one row per finding, with check and severity columns prepended and list fields (e.g. the pages sharing a duplicate value) joined with ; . Unlike the .docx, which reformats the written report, the CSV is a direct dump of the structured findings — offer it whenever a user wants the data itself, a spreadsheet, or to slice findings by check or severity.

Resources

• scripts/md_to_docx.py — convert the Markdown report into a Word (.docx) document (standard library only)
• scripts/findings_to_csv.py — flatten the audit findings JSON into a CSV, one row per finding (standard library only)
• references/audit-checks.md — full definitions, thresholds, and rationale for every audit check
• references/report-template.md — report output structure
• scripts/extract_external_links.py — crawl a site and build the external link inventory
• scripts/check_external_links.py — resolve target statuses and run audit checks