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SKILL verified MIT Self-run

Seo Content

skill-zubair-trabzada-dataforseo-claude-seo-content · by zubair-trabzada

Content topical authority and gap analysis powered by the DataForSEO Labs API. Clusters a domain's ranked keywords into topics, identifies strong vs weak vs missing topic clusters versus competitors, and returns a Content Score (0-100) plus the highest-leverage content opportunities to write next.

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Install

$ agentstack add skill-zubair-trabzada-dataforseo-claude-seo-content

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets Used
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
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2mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.

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About

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= DATAFORSEO_PASSWORD= ``

  1. chmod 600 ~/.claude/skills/seo/.env
  2. Run a verification call: ~/.claude/skills/seo/scripts/keyword_research.py volume "test"
  3. If verification succeeds (real JSON returned): tell the user "✅ Credentials verified. Running your command now..." and proceed with the original request.
  4. If status 40104 — Please verify your account: tell the user to verify their account at https://app.dataforseo.com/, then say "continue" to retry.
  5. 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.


SEO Content Authority Skill

> Powered by: DataForSEO API — Labs ranked_keywords + Labs competitors_domain + Labs domain_intersection. > Cost: ~$0.05 per run.

Run

Pull what the target ranks for, plus the top competitor's ranked keywords:

~/.claude/skills/seo/scripts/domain_overview.py ranked --target  --limit 200
~/.claude/skills/seo/scripts/domain_overview.py competitors --target  --limit 5
~/.claude/skills/seo/scripts/domain_overview.py content_gap --you  --competitors   

Topic clustering

Group keywords into topical clusters using common stems / shared head terms. Don't be too granular — aim for 8-15 clusters max for a typical site.

Example clusters for an SEO tool site:

  • "keyword research" (head: keyword)
  • "rank tracking" (head: rank, ranking)
  • "backlinks" (head: backlink, link building)
  • "site audit" (head: audit, technical)
  • ...

For each cluster, classify it

| Status | Definition | |--------|------------| | Strong | 5+ keywords ranking top 10, total est. traffic > 100/mo | | Building | Some keywords top 30, none top 10 yet | | Weak | Keywords ranking but all below position 30 | | Missing | Competitors rank, you don't (from content_gap) |

Content Score (0-100)

content_score = round(
    50 * (strong_clusters / total_clusters) +
    25 * (1 - missing_clusters / total_clusters) +
    25 * (avg_position_top_quartile_score)
)

Highest-leverage content moves

Surface the top 5 specific articles to write next. Pick from the "Missing" and "Building" clusters, prioritizing keywords with:

  • Search volume > 200/mo
  • Difficulty < competitor's domain rank
  • Commercial or transactional intent

For each, suggest: working title, target keyword, related keywords to include, estimated word count.

Return JSON shape

{
  "content_score": 64,
  "strong_topics": [{"cluster": "...", "keywords": 12, "avg_position": 5.2}],
  "weak_topics": [{"cluster": "...", "keywords": 18, "avg_position": 42.1}],
  "missing_topics": [{"cluster": "...", "competitor": "...", "keyword_count": 24}],
  "content_recommendations": [
    {"title": "...", "target_keyword": "...", "volume": 880, "difficulty": 22, "intent": "commercial"}
  ]
}

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.