Backlink Batch Expansion logo

Backlink Batch Expansion

Organization
flaqai
backlink-batch-expansion

Build and maintain a reusable backlink-submission candidate pool with global deduplication, fee and brand classification, configurable batch exports, and executor feedback. Use for large-scale directory, tool-listing, startup-database, community, or resource-page backlink expansion; do not use for editorial guest-post production or actual submission without separate authorization.

Overview

Publisherflaqai
Repositorybacklink_skills
Skill namebacklink-batch-expansion
Stars
760
Forks
237
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by flaqai on GitHub. Read the source before you install it.

Installation

Install the Backlink Batch Expansion AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/flaqai/backlink_skills/tree/main/双线外链挖掘矩阵/skills/backlink-batch-expansion
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Use it in TypingMind

Enable Backlink Batch Expansion in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Backlink Batch Expansion on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Backlink Batch Expansion is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

外链批量扩量

将大量来源整理成一个可持续更新的批量提交候选库,再从总库自动生成执行批次。核心目标是减少重复研究、重复查重和重复制表,同时明确区分候选、已核验、已提交和已收录。

在 backlink-expansion-matrix 端到端流程中,本技能只负责批量线筛选、分类和候选池,不直接修改最终工作簿;完成后交给 backlink-workbook-delivery 统一去重、冻结批次和制表。单独调用本技能且用户明确要求工作簿时,仍可按下述交付规则直接生成。

适用边界

  • 适用于工具目录、产品导航、创业项目库、社区资料页和资源推荐页等批量提交机会。
  • 文章投稿线单独处理,不把必须写正式文章或新闻稿的入口混入批量提交表。
  • 候选表不是提交授权,也不证明入口有效、链接可控、提交成功、页面公开或已被搜索引擎收录。
  • 未经用户授权,不注册、不登录、不付款、不提交、不联系站方。
  • 若筛选阶段不做逐站人工核验,必须把交付物明确标为“待执行候选”,并把确认成本交给执行人员在真实提交时记录。

启动前冻结范围

确认并记录:目标品牌、候选来源、现有工作簿、目标是“现有表补足到某个总数”还是“另建一个固定数量的新批次”、批次大小、批量提交线的排除条件、是否允许轻量公开页检测、是否允许子代理并行、输出目录和最终列名。用户已给出这些信息时直接执行,不重复询问。

“现有 A 条补足到 B 条”表示保留原记录并新增 B-A 条,不得解释成另外生成 B 条。若现有记录已经属于某个已导出批次,保留原有批次关系;新增记录只填补当前目标或进入尚未冻结的新批次。

工作流程

  1. 建立总候选库:保留原始来源,不直接从多个来源分别生成最终工作簿。
  2. 分片研究:只有用户明确要求并行时,才把互不重叠的来源或候选范围交给子代理。先登记分片编号、负责代理、来源范围、输出文件和状态。每个子代理只写自己的中间文件,不能同时修改总库或最终工作簿。
  3. 单点合并:主代理统一标准化域名和 URL、全局查重、分类、分配品牌并维护状态。
  4. 轻量入口识别:按授权检查公开首页、页脚和少量常见路径;不扫描全站,不绕过验证码或安全验证。没有发现入口不等于网站无机会,应转入备用池。
  5. 一次分类:费用、入口类型、品牌适配、候选池和风险只在总库中判断一次。只有找到明确提交入口或满足用户的宽松收录规则时才进入可执行池;仅有首页、联系页或待确认入口的记录进入备用池。
  6. 自动分批:批次大小作为参数。已导出的批次编号冻结;改变批次大小只影响尚未分配的记录。
  7. 机械导出:从总库生成每个批次的工作簿,避免逐批重新研究、查重和制表。
  8. 回写结果:执行人员提交时记录真实费用、登录要求、强制回链、有效性和提交结果,供下一批调整排序和排除规则。

候选来源可以包含 Semrush 优先候选、竞品引荐域名、历史候选表、资源推荐页、导航站出站域名和公开外链清单。来源只决定检查顺序,不替代具体入口判断。

详细字段、去重、费用和状态规则见 references/schema-and-rules.md。

开始并行前复制 assets/shard-registry.csv 作为分片登记表。合并总候选库后运行 scripts/validate_candidate_pool.py --input <总候选库.csv>,检查必需字段、固定枚举、URL、费用分类和重复项;修正错误后再生成批次。

候选保留规则

批量线以扩量为先,允许注册、登录、验证码、人工审核、较长产品资料和 nofollow。排除以下对象:

  • 必须发布正式文章或新闻稿;
  • 强制回链、强制徽章或存在明确删除风险;
  • 成人、赌博、恶意软件、明显垃圾或失效网站;
  • 没有任何可识别提交入口,且没有保留价值。

链接实力、自然流量和竞品覆盖主要决定检查顺序,不作为严格淘汰条件。高权重通用网站不自动等于可提交平台;小型垂直目录也不因权重低而自动删除。

费用与证据

  • 明确存在可用的免费基础路线才能进入免费组;允许同站另有可选付费升级。
  • 明确收费进入付费组。
  • 免费路线本身是否存在尚未确认时,不得因为“可能免费”或“未看到收费”写成免费。若交付结构只有“免费”和“付费”,暂放付费组,并在投递条件中写“费用待确认”;统计时不得把它与明确付费混为一谈。
  • 保存具体入口 URL 和简短证据说明。搜索结果、公开页面或可读表单只证明候选存在,不证明链接一定可控或会被收录。

工作簿要求

编辑或创建 Excel 文件时,使用环境中的电子表格技能和指定的工作簿工具。保留原文件并另存新版本。最终执行表只显示用户要求的列;为下拉列表保留的辅助列可以存在,但必须隐藏或设为零宽,不能让执行人员误以为它是交付字段。内部编号、来源、状态、证据和批次字段留在总候选库中。

若当前任务由 backlink-expansion-matrix 总控,本节由 backlink-workbook-delivery 执行,本技能只提供通过筛选的候选池、备用池和排除表。

交付前至少检查:

  • 免费与付费两个工作表及列名符合要求;
  • 总行数达到目标,且没有空域名、空 URL 或异常格式;
  • 完整 URL 全局唯一;同时从 URL 主机名重新计算根域名,不能只相信历史“域名”列;同一根域名的不同入口经过判断;
  • 不与历史已导出批次重复;
  • 每条至少适合一个目标品牌;
  • “费用待确认”没有被报告成明确免费;
  • 已导出批次和未分配候选状态清楚;
  • 工作簿重新导入、数据检查和视觉检查通过。

批次反馈

第一批开始执行后,按用户指定的抽样数收集反馈;用户未指定时,应根据批次规模给出建议并由用户确认。若无效率、强制付费率或入口失效率明显升高,先调整排序和识别规则,再生成下一批。分别统计候选数、实际提交数和成功收录数,三者不能混用。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Backlink Batch Expansion AI skill do?

Build and maintain a reusable backlink-submission candidate pool with global deduplication, fee and brand classification, configurable batch exports, and executor feedback. Use for large-scale directory, tool-listing, startup-database, community, or resource-page backlink expansion; do not use for editorial guest-post production or actual submission without separate authorization.

Why use Backlink Batch Expansion on TypingMind?

Because you install it once and use it with any model. Backlink Batch Expansion is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Backlink Batch Expansion in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/flaqai/backlink_skills/tree/main/双线外链挖掘矩阵/skills/backlink-batch-expansion. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Backlink Batch Expansion?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Backlink Batch Expansion?

As many as you like. As long as a model supports skills, you can use Backlink Batch Expansion with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Backlink Batch Expansion AI skill free?

Yes. It is published on GitHub by flaqai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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