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 build-with-dhiraj/ai-workflow-framework-portability-kit --skill seo-backlinksgit clone --depth 1 https://github.com/build-with-dhiraj/ai-workflow-framework-portability-kitWrote 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/build-with-dhiraj/ai-workflow-framework-portability-kit/seo-backlinks)<a href="https://agentmods.dev/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/seo-backlinks"><img src="https://agentmods.dev/badge/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/seo-backlinks/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/build-with-dhiraj/ai-workflow-framework-portability-kit/seo-backlinks"><img src="https://agentmods.dev/badge/skills/build-with-dhiraj/ai-workflow-framework-portability-kit/seo-backlinks.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 | $0.00075 | $0.03133 |
| Opus 5.5 | $0.00030 | $0.01253 |
| Sonnet 5.5 | $0.00015 | $0.00627 |
| Haiku 4.5 | $0.00007 | $0.00313 |
Grade A, and why
seo-backlinks 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 1mo 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 seo-backlinks — 6 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 — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Backlink Profile Analysis
Source Detection
Before analysis, detect available data sources:
- DataForSEO MCP (premium): Check if
dataforseo_backlinks_summarytool is available - Moz API (free signup):
python scripts/backlinks_auth.py --check moz --json - Bing Webmaster (free signup):
python scripts/backlinks_auth.py --check bing --json - Common Crawl (always available): Domain-level graph with PageRank
- Verification Crawler (always available): Checks if known backlinks still exist
Run python scripts/backlinks_auth.py --check --json to detect all sources at once.
If no sources are configured beyond the always-available tier:
- Still produce a report using Common Crawl domain metrics
- Suggest: "Run
/seo backlinks setupto add free Moz and Bing API keys for richer data"
Quick Reference
| Command | Purpose |
|---|---|
/seo backlinks <url> |
Full backlink profile analysis (uses all available sources) |
/seo backlinks gap <url1> <url2> |
Competitor backlink gap analysis |
/seo backlinks toxic <url> |
Toxic link detection and disavow recommendations |
/seo backlinks new <url> |
New and lost backlinks (DataForSEO only) |
/seo backlinks verify <url> --links <file> |
Verify known backlinks still exist |
/seo backlinks setup |
Show setup instructions for free backlink APIs |
Analysis Framework
Produce all 7 sections below. Each section lists data sources in preference order.
1. Profile Overview
DataForSEO: dataforseo_backlinks_summary → total backlinks, referring domains, domain rank, follow ratio, trend.
Moz API: python scripts/moz_api.py metrics <url> --json → Domain Authority, Page Authority, Spam Score, linking root domains, external links.
Common Crawl: python scripts/commoncrawl_graph.py <domain> --json → in-degree (referring domain count), PageRank, harmonic centrality.
Scoring:
| Metric | Good | Warning | Critical |
|---|---|---|---|
| Referring domains | >100 | 20-100 | <20 |
| Follow ratio | >60% | 40-60% | <40% |
| Domain diversity | No single domain >5% | 1 domain >10% | 1 domain >25% |
| Trend | Growing or stable | Slow decline | Rapid decline (>20%/quarter) |
What ships with it
1 file 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.
- 1mo ago First seen · 265 lines · 75 tokens per session scan A 475230f5667f
seo-backlinks is a skill published in the GitHub repository build-with-dhiraj/ai-workflow-framework-portability-kit (5 stars, last pushed 24d ago), licensed MIT. It adds 75 tokens to every session and 3,133 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to seo-backlinks, differing in 6 lines, and is treated as a copy.
Other skills, from other repositories
codeql
Run CodeQL database creation and security queries, add data-extension models, or process CodeQL SARIF. Use when CodeQL is explicitly requested; use security-review for a broader manual security review.
huggingface-vision-trainer
Train object-detection, image-classification, or SAM segmentation models on Hugging Face Jobs. Use for vision fine-tuning and evaluation; use huggingface-llm-trainer for language models.
sharp-edges
Identify error-prone APIs, dangerous configuration, and footgun designs that enable security mistakes. Use for API ergonomics, misuse resistance, secure defaults, or pit-of-success reviews; use code-review for general defects.
code-quality
Agents should invoke this skill for code reviews, linting/formatting setup, maintainability checks, complexity concerns, warning cleanup, coding standards, or quality gates in Rust, TypeScript, Python, shell, and mixed repos.
code-reviewer
Review code for bugs, logic errors, security vulnerabilities, code quality issues, and adherence to project conventions, using confidence-based filtering to report only high-priority issues that truly matter. Use this skill when reviewing a small set of changes locally (such as unstaged diff), when dispatched as a…
differential-review
Performs security-focused differential review of code changes (PRs, commits, diffs). Adapts analysis depth to codebase size, uses git history for context, calculates blast radius, checks test coverage, and generates comprehensive markdown reports. Automatically detects and prevents security regressions.