/seo-backlinks

name: seo-backlinks
description: Backlink profile audit via the DataForSEO Backlinks API. Returns total backlinks, referring domains, dofollow ratio, top anchors with over-optimization flags, top referring domains by authority, and a toxicity assessment with an Authority Score (0-100).
allowed-tools:
  - Bash
  - Write

Phase 0: Credential Preflight (REQUIRED — run BEFORE anything else)

Before running any of the steps below, **always** invoke the shared preflight check:

~/.claude/skills/seo/scripts/preflight.sh

**If exit code is 0:** credentials are configured — proceed with the rest of this skill silently.

**If exit code is 2:** the script prints the DataForSEO setup wizard to stdout. STOP, display that wizard to the user verbatim, and **wait for them to paste credentials** in this format:

login: their_email@example.com
password: their_api_password_here

When they reply:

1. Parse `login:` and `password:` from their message. 2. Write them to `~/.claude/skills/seo/.env`:

   DATAFORSEO_LOGIN=<login>
   DATAFORSEO_PASSWORD=<password>

3. `chmod 600 ~/.claude/skills/seo/.env` 4. Run a verification call: `~/.claude/skills/seo/scripts/keyword_research.py volume "test"` 5. If verification succeeds (real JSON returned): tell the user "✅ Credentials verified. Running your command now..." and proceed with the original request. 6. If status `40104 — Please verify your account`: tell the user to verify their account at https://app.dataforseo.com/, then say "continue" to retry. 7. If any other auth error: ask them to double-check the API password (the long alphanumeric string from https://app.dataforseo.com/api-access — not their account login password).

**Never** echo credentials back to the user, never include them in tool output, and never commit them.

---

Backlink Audit Skill

> **Powered by:** [DataForSEO API](https://dataforseo.com) — Backlinks `summary` + `referring_domains` + `anchors` + `domain_intersection`. > **Cost:** ~$0.03-0.05 per run.

Run

Three calls, parallel:

~/.claude/skills/seo/scripts/backlinks.py summary --target <domain>
~/.claude/skills/seo/scripts/backlinks.py refdomains --target <domain> --limit 100
~/.claude/skills/seo/scripts/backlinks.py anchors --target <domain> --limit 50

What to evaluate

Volume & freshness

• Total backlinks, total referring domains
• New backlinks in last 30/90 days
• Lost backlinks in last 30/90 days
• Trend: growing, stable, declining

Quality

• Dofollow ratio (50-80% is healthy; >95% looks artificial)
• Domain rank distribution of referring sites

(mostly low-rank domains = weak profile)

• TLD distribution (.edu / .gov / .org weight more)
• IP / subnet diversity (PBN red flag if all same C-class)

Anchor text

• Top 10 anchors by frequency
• Branded anchor share — should be 40-60% for a healthy profile
• <30% looks unnatural
• >80% means few topical links
• Exact-match anchor share — should be < 10%
• >20% = over-optimization, manual penalty risk

Toxicity flags

• High share of links from low-rank domains (rank < 50)
• Sudden spike in backlinks (possible negative SEO)
• Anchor text patterns suggesting paid links
• Adult / gambling / pharma TLDs in referring domains

Authority Score (0-100)

| Signal | Weight | |--------|--------| | log10(referring_domains) scaled to 0-30 | 30% | | Domain rank percentile | 25% | | Anchor naturalness (penalty for >20% exact-match) | 15% | | Dofollow ratio in healthy band (50-80%) | 10% | | TLD/IP diversity | 10% | | Toxicity penalty (negative) | up to -10% |

Return JSON shape

{
  "authority_score": 68,
  "backlink_summary": {
    "backlinks": 12345, "referring_domains": 432,
    "dofollow_pct": 67, "broken_backlinks": 12,
    "new_30d": 18, "lost_30d": 5
  },
  "top_referrers": [{"domain": "...", "rank": 612, "backlinks": 24, "dofollow": true}],
  "anchor_distribution": {
    "branded": 0.51, "exact_match": 0.08, "partial_match": 0.18,
    "naked_url": 0.12, "generic": 0.11
  },
  "toxicity_flags": ["..."]
}