Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add charlieviettq/awesome-agent-skill --skill algo-seo-backlinkgit clone --depth 1 https://github.com/charlieviettq/awesome-agent-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/algo-seo-backlink)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/algo-seo-backlink"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-seo-backlink/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/algo-seo-backlink"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-seo-backlink.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00065 | $0.00972 |
| Opus 5 | $0.00032 | $0.00486 |
| Sonnet 5 | $0.00013 | $0.00194 |
| Haiku 4.5 | $0.00006 | $0.00097 |
Grade A, and why
"algo-seo-backlink" scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
95% identical to algo-seo-backlink — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- Deduplicate by referring domain (one link per domain for analysis)
- Score each link: authority (0-100) × relevance (0-1) × placement weight
- Flag toxic links: DA < 10, irrelevant foreign language, link farm patterns, PBN indicators
- 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.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 88 lines · 65 tokens per session scan A fc9578f8b67a
"algo-seo-backlink" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (25 stars, last pushed 1mo ago), licensed MIT. It adds 65 tokens to every session and 972 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to algo-seo-backlink, differing in 8 lines, and is treated as a copy.
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