本文へ移動
cccskills
無料GitHub で公開

"algo-seo-backlink"

"Evaluate backlink quality using Domain Authority, Domain Rating, and trust metrics. Use this skill when the user needs to assess link profile health, identify toxic backlinks, or plan link building strategy — even if they say 'check my backlinks', 'link building', or 'domain authority analysis'.".

インストール方法を見る

含まれるファイル(4)

  • SKILL.md4.1 KB
  • examples/sample_scenario.md6.0 KB
  • references/competitor-gap.md8.3 KB
  • references/toxicity-scoring.md9.1 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Backlink Quality Assessment

Overview

Backlink analysis evaluates incoming links by quality metrics (DA/DR, relevance, anchor text diversity, toxicity) to assess a site's off-page SEO strength. Quality assessment is heuristic-based using third-party metrics (Moz DA, Ahrefs DR) as PageRank proxies.

When to Use

Trigger conditions:

  • Auditing a site's backlink profile for SEO health
  • Identifying and disavowing toxic or spammy links
  • Planning link building strategy based on competitor analysis

When NOT to use:

  • When optimizing on-page content (use content SEO)
  • When computing actual PageRank from raw link graphs (use PageRank algorithm)

Algorithm

IRON LAW: Backlink QUALITY Outweighs Quantity
One link from a high-authority, topically relevant domain is worth
more than hundreds from low-quality sites. Evaluate every link on:
1. Authority (DA/DR of linking domain)
2. Relevance (topical match between linking and target pages)
3. Placement (editorial in-content > footer/sidebar)
4. Anchor text (natural diversity > exact-match keyword stuffing)

Phase 1: Input Validation

Export backlink data from Ahrefs, Moz, or Search Console. Required fields: referring domain, DA/DR, anchor text, link type (dofollow/nofollow), first seen date. Gate: Complete backlink export with authority metrics.

Phase 2: Core Algorithm

  1. Deduplicate by referring domain (one link per domain for analysis)
  2. Score each link: authority (0-100) × relevance (0-1) × placement weight
  3. Flag toxic links: DA < 10, irrelevant foreign language, link farm patterns, PBN indicators
  4. Compute profile metrics: total referring domains, DR distribution, anchor text diversity index

Phase 3: Verification

Cross-reference flagged toxic links against known spam databases. Verify anchor text distribution follows natural pattern (branded > URL > keyword > misc). Gate: Toxic links identified, anchor profile analyzed.

Phase 4: Output

Return profile assessment with link quality distribution and action items.

Output Format

{
  "profile": {"referring_domains": 450, "avg_dr": 35, "toxic_count": 23, "anchor_diversity": 0.78},
  "actions": [{"type": "disavow", "domains": ["spam1.com"], "reason": "link farm pattern"}],
  "metadata": {"tool": "ahrefs", "export_date": "2025-01-15"}
}

Examples

Sample I/O

Input: 500 backlinks, 200 referring domains Expected: Distribution: 15% DR 60+, 40% DR 20-59, 45% DR 0-19. Flag 23 toxic domains for disavow.

Edge Cases

InputExpectedWhy
All links from one domainLow profile diversitySingle-source dependency is risky
90% exact-match anchorsAnchor text penalty riskUnnatural anchor pattern
Zero backlinksFocus on content firstCan't optimize what doesn't exist

Gotchas

  • DA/DR are third-party estimates: They approximate PageRank but are NOT Google metrics. Two tools often disagree on the same domain's authority.
  • Nofollow still matters: Google treats nofollow as a "hint." A nofollow link from a DR 90 site still has SEO value, just less than dofollow.
  • Disavow carefully: Google's disavow tool is a last resort. Disavowing legitimate links harms your own profile. Only disavow clearly toxic/spammy links.
  • Anchor text manipulation: Exact-match anchor text used to be a ranking factor; now it's a spam signal. Natural profiles have mostly branded and URL anchors.
  • Temporal patterns: Sudden spikes in backlinks (e.g., 100 links in one day) trigger spam filters. Natural link acquisition is gradual.

References

  • For link toxicity scoring methodology, see references/toxicity-scoring.md
  • For competitor backlink gap analysis, see references/competitor-gap.md

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

"Implement and select ad bidding strategies from manual CPC to automated target-CPA and target-ROAS. Use this skill when the user needs to choose a bidding strategy, set up automated bidding, or optimize bid parameters — even if they say 'what bidding strategy should I use', 'target CPA setup', or 'smart bidding configuration'.".

日本語の概要は準備中です。原文の説明を表示しています。

charlieviettq/awesome-agent-skill262026年7月20日 更新

"Optimize advertising budget allocation across campaigns using marginal returns analysis. Use this skill when the user needs to distribute budget across multiple campaigns, optimize spend pacing, or maximize overall ROAS under budget constraints — even if they say 'how to split my ad budget', 'campaign budget optimization', or 'diminishing returns on ad spend'.".

日本語の概要は準備中です。原文の説明を表示しています。

charlieviettq/awesome-agent-skill262026年7月20日 更新

"Build CTR prediction models for estimating ad click-through rates from features. Use this skill when the user needs to predict click probability, build an ad ranking model, or evaluate ad creative performance — even if they say 'predict click rate', 'ad relevance scoring', or 'which ad will get more clicks'.".

日本語の概要は準備中です。原文の説明を表示しています。

charlieviettq/awesome-agent-skill262026年7月20日 更新

"Implement Generalized Second Price auction for ad slot allocation and pricing. Use this skill when the user needs to understand search ad auctions, compute ad positions and costs-per-click, or analyze bidding dynamics — even if they say 'how does Google Ads auction work', 'ad rank calculation', or 'second price auction for ads'.".

日本語の概要は準備中です。原文の説明を表示しています。

charlieviettq/awesome-agent-skill262026年7月20日 更新

"Implement VCG mechanism for incentive-compatible ad slot allocation with truthful bidding. Use this skill when the user needs to design a truthful auction mechanism, compute externality-based payments, or understand why platforms may prefer GSP over VCG — even if they say 'truthful auction design', 'VCG payments', or 'incentive-compatible mechanism'.".

日本語の概要は準備中です。原文の説明を表示しています。

charlieviettq/awesome-agent-skill262026年7月20日 更新

"Explain blockchain fundamentals including distributed ledger architecture, consensus mechanisms, and block structure. Use this skill when the user needs to understand blockchain concepts, evaluate whether blockchain fits a use case, or design a blockchain-based solution — even if they say 'how does blockchain work', 'do I need blockchain', or 'distributed ledger'.".

日本語の概要は準備中です。原文の説明を表示しています。

charlieviettq/awesome-agent-skill262026年7月20日 更新

charlieviettq のスキルをすべて見る

このスキルの問題を報告する