flaqai/backlink_skills

Awesome skills for submitting url to free websites. Get more backlinks for your website to get more traffic.

What it solves

This project provides a structured workflow for managing backlink submissions and SEO content production. It moves away from "one-click spamming" by using AI-driven skills to research target directories, handle submissions with human-in-the-loop verification, and generate platform-specific content that resonates with local audiences rather than duplicating the same text across the web.

How it works

The project is built as a set of "Codex Skills" (reusable instructions and scripts) that can be integrated into AI agents. It operates across three main pillars:

  1. Backlink Submission: Two distinct strategies are provided: a "Batch" version (V1) for high-efficiency processing of pre-screened URLs with breakpoint recovery and deduplication, and a "Quality" version (V2) for high-touch, small-batch submissions focused on audience relevance and long-term value.
  2. SEO Content Generation: A general SEO writer for blogs and tutorials, plus specialized writers for LinkedIn, Medium, and WeChat, each with unique research, structuring, and "humanization" workflows to avoid AI-generated templates.
  3. Curated Database: A list of 743 free backlink channels with descriptions and operational notes to serve as a starting point for research.

Who it’s for

  • Marketers and founders promoting AI products or SaaS tools.
  • SEO specialists looking for a systematic, non-spammy way to build backlinks.
  • Content creators who need to adapt SEO articles for different social and professional platforms.

Highlights

  • Human-in-the-Loop: Specifically handles CAPTCHAs, 2FA, and email verifications by queuing them for the user rather than attempting to bypass them.
  • Platform-Specific Writing: Tailors content structure and tone for B2B (LinkedIn), narrative-driven (Medium), and mobile-first (WeChat) audiences.
  • Auditability: Includes Python scripts to audit submission records and check for mechanical patterns in generated text.
  • Evidence-Based: Tracks detailed submission statuses (e.g., awaiting approval, published, ineligible) to ensure a verifiable trail of work.

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