Submit Product Directories V1 Batch logo

Submit Product Directories V1 Batch

Organization
flaqai
submit-product-directories-v1-batch

SPD V1 Batch. Process large, user-supplied sets of legitimate product, software, startup, app, and AI-tool directory URLs across Windows, macOS, and Linux-capable environments through normalization, deduplication, execution sharding, verification-first queues, authorization-controlled form work, idempotent submission, recovery, and truthful throughput reporting. Use when coverage and operational throughput matter more than deep per-site quality analysis. Do not use for ranking manipulation, bulk link spam, invented data, CAPTCHA bypass, paid-link acquisition, forced reciprocal links, or routes prohibited by a site's terms.

Overview

Publisherflaqai
Repositorybacklink_skills
Skill namesubmit-product-directories-v1-batch
Stars
760
Forks
237
Bundled files
8
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.

  • 8 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 Submit Product Directories V1 Batch 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/submit-product-directories-v1-batch
  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.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/flaqai/backlink_skills.git /tmp/backlink_skills
mkdir -p .claude/skills
cp -r /tmp/backlink_skills/submit-product-directories-v1-batch .claude/skills/submit-product-directories-v1-batch
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Submit Product Directories V1 Batch 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 Submit Product Directories V1 Batch 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 Submit Product Directories V1 Batch 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.

SPD V1 Batch — large-batch directory operations

Version identity

  • Canonical name: SPD V1 Batch.
  • Invocation: $submit-product-directories-v1-batch.
  • Optimize for queue throughput, repeatability, verification handling, and recovery across large source lists.
  • Apply a fast legitimacy gate, not the deeper editorial and referral-value analysis used by $submit-product-directories-v2-quality.
  • Route campaigns requiring careful site selection, durable-placement analysis, or SEO-quality evidence to V2 Quality.

Load controls

  1. Read the verified product profile, brand rules, contact and credential aliases, approved assets, source list, batch authorization, and existing record.
  2. Read references/workflow.md before planning or browser work.
  3. Read references/status-model.md before writing or auditing records.
  4. Read references/browser-control-routing.md before any browser or app interaction. Run the Windows/macOS/Linux capability preflight and select the backend from the current environment; do not assume a specific browser, operating system, or Computer Use support.
  5. Copy assets/submission-record-template.md when no V1 Batch record exists.

Never invent product, company, founder, pricing, address, launch, ownership, contact, or legal facts. Keep optional unknowns blank and block required unknowns.

Apply the batch legitimacy gate

Reject or separate any route that is irrelevant to the product, unavailable, unreleased-only, paid-link-only, forced-reciprocal, a known low-quality directory network, or prohibited for automated form work. Do not select sites because they promise dofollow links, ranking gains, DA/DR, or backlink volume.

Use only the exact brand, product name, or naked canonical URL as public link text. Never request dofollow treatment or use repeated commercial exact-match anchors.

Build the queue

  1. Normalize hostnames and submission routes. Strip tracking parameters from the record while preserving required route parameters in controlled evidence.
  2. Derive an idempotency key from platform domain, product canonical ID, account alias, and route.
  3. Deduplicate before opening the browser. Never execute an idempotency key that is already submitted, awaiting approval, published, or outcome unknown.
  4. Assign stable queue IDs and execution shards. Treat shard size and maximum active tabs as operational settings, not SEO safety thresholds.
  5. Classify every site into direct form, account required, manual verification, email verification, paid/reciprocal, unavailable, ineligible, or unknown.
  6. Use batch-scoped authorization only when it names the allowed actions, source-list scope, approver alias, approval time, and expiry. Payments, reciprocal-site changes, DNS changes, and publication outside a directory require separate authorization.

Run the verification-first pipeline

  1. Run a read-only preflight over each shard before entering product-listing fields.
  2. Expose the earliest native CAPTCHA, Turnstile, image code, email check, login, or similar safeguard.
  3. Attempt only the site's ordinary native automatic verification. Never bypass, outsource, or weaken a safeguard.
  4. Move unresolved items to one manual queue and continue processing eligible sites.
  5. After the user completes the queue, recheck token validity and process short-lived tokens first.
  6. Do not hold more active challenge tabs than the configured browser capacity.

Execute forms at scale

  1. Process only sites that passed the legitimacy gate, authorization check, duplicate check, and verification prerequisite.
  2. Reuse approved field variants by length and category, while preserving exact public brand spelling and truthful meaning.
  3. Keep newsletters and optional promotions off unless authorized.
  4. Review plan, cost, URL, identity, category, agreements, uploads, and verification immediately before submission.
  5. Submit sequentially within a browser profile. Record the result before advancing the queue cursor.
  6. Never retry an ambiguous final action. Check the account backend, mailbox, and public page first.
  7. Save drafts, transient failures, manual actions, and terminal outcomes as distinct states so the campaign can resume without replaying completed work.

Protect records

  • Store aliases and controlled evidence IDs, not passwords, OTPs, recovery codes, cookies, OAuth parameters, magic links, raw session IDs, raw email addresses, phone numbers, or tokenized URLs.
  • Separate the shareable campaign record from controlled evidence.
  • Treat a click, registration, draft, cleared form, or generic thank-you URL as insufficient submission evidence.

Close and measure

Run:

On macOS or Linux:

bash
python3 scripts/audit_submission_record.py path/to/v1-batch-record.md
python3 scripts/audit_submission_record.py path/to/v1-batch-record.md --json

On Windows, use py -3 or an equivalent Python 3 launcher:

powershell
py -3 scripts/audit_submission_record.py path\to\v1-batch-record.md
py -3 scripts/audit_submission_record.py path\to\v1-batch-record.md --json

Report totals by queue state, verification state, shard, and outcome. Measure queue completion rate, verified submissions per operator hour, duplicate avoidance, recovery rate, and unresolved manual workload. Report published listings separately from submitted forms. Do not report submission volume as proof of SEO value.

Bundled resources

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 Submit Product Directories V1 Batch AI skill do?

SPD V1 Batch. Process large, user-supplied sets of legitimate product, software, startup, app, and AI-tool directory URLs across Windows, macOS, and Linux-capable environments through normalization, deduplication, execution sharding, verification-first queues, authorization-controlled form work, idempotent submission, recovery, and truthful throughput reporting. Use when coverage and operational throughput matter more than deep per-site quality analysis. Do not use for ranking manipulation, bulk link spam, invented data, CAPTCHA bypass, paid-link acquisition, forced reciprocal links, or rou...

Why use Submit Product Directories V1 Batch on TypingMind?

Because you install it once and use it with any model. Submit Product Directories V1 Batch 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 Submit Product Directories V1 Batch in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/flaqai/backlink_skills/tree/main/submit-product-directories-v1-batch. 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 Submit Product Directories V1 Batch?

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 Submit Product Directories V1 Batch?

As many as you like. As long as a model supports skills, you can use Submit Product Directories V1 Batch with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Submit Product Directories V1 Batch 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇