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Agent skill

Submit Product Directories V1 Batch

by flaqai in flaqai/backlink_skills

SPD V1 Batch. An agent skill from flaqai/backlink_skills.

MITAuto-check passedDatabases

Install Submit Product Directories V1 Batch

skills CLI
$ npx skills add flaqai/backlink_skills --skill submit-product-directories-v1-batch -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install flaqai/backlink_skills submit-product-directories-v1-batch --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/flaqai/backlink_skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/submit-product-directories-v1-batch .claude/skills/submit-product-directories-v1-batch && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
submit-product-directories-v1-batch
GitHub stars
756
Token cost
~1.7k tokens
SKILL.md length
717 words
Files
9 (incl. scripts, references, assets)
Skills in repo
19
Repo updated
First seen
Licence
MIT

At a glance

SPD V1 Batch. An agent skill from flaqai/backlink_skills.

  • Works in 5 steps: Read the verified product profile, brand… → Read references/workflow.md before… → Read references/status-model.md before… → …
  • Coverage and operational throughput matter more than deep per-site quality analysis
  • SKILL.md covers Version identity, Load controls, Apply the batch legitimacy gate and Build the queue, plus 5 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Submit Product Directories V1 Batch is an agent skill from flaqai/backlink_skills. 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…

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 13 other files, including scripts, reference files and assets (for example `agents/openai.yaml`, `assets/submission-record-template.md` and `references/browser-control-routing.md`).

It sits in Databases, covering Database schema design, Database administration and Data cleaning. It works with Linux and macOS. The repository describes itself as: Awesome skills for submitting url to free websites. Get more backlinks for your website to get more traffic. The licence is MIT.

When your agent uses it

  • Coverage and operational throughput matter more than deep per-site quality analysis
  • Ranking manipulation
  • Paid-link acquisition
  • Forced reciprocal links

Example prompts

  • “/submit-product-directories-v1-batch”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Read the verified product profile, brand rules, contact and credential aliases, approved assets, source list, batch authorization, and…
  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…
  5. Copy assets/submission-record-template.md when no V1 Batch record exists.

What it can do on your machine

Read from SKILL.md and the folder at commit 3c56c94. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Submit Product Directories V1 Batch loads about 1.7k tokens when it runs, and up to ~6.2k if it reads all its reference files. Until then it costs about 167 tokens; SKILL.md has 717 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~167
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from flaqai/backlink_skills at commit 3c56c94, republished under its MIT licence (© flaqai). 717 words, ~1,714 tokens.

Download SKILL.mdSave it as .claude/skills/submit-product-directories-v1-batch/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
submit-product-directories-v1-batch
description
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.

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.
Show full SKILL.md (267 more words)Show less

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

© flaqai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (scripts, references, assets) in submit-product-directories-v1-batch of flaqai/backlink_skills.

  • SKILL.md
  • agents/openai.yaml
  • assets/submission-record-template.md
  • references/browser-control-routing.md
  • references/status-model.md
  • references/workflow.md
  • scripts/audit_submission_record.py
  • tests/test_audit_submission_record.py
  • tests/test_browser_control_routing.py

Open the folder on GitHubat commit 3c56c94

Compare with similar skills

Submit Product Directories V1 Batch next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Submit Product Directories V1 Batch compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Submit Product Directories V1 Batch this skillflaqai/backlink_skills756—~1.7kAutomated safety check: PassMIT
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Vitess for PlanetScaleplanetscale/database-skills7081 repos~1.2kAutomated safety check: PassMIT
DeepChat Data Import HelperThinkInAIXYZ/deepchat6.4k—~738Automated safety check: PassApache-2.0
Database Expertcin12211/orca-q224—~2.8kAutomated safety check: PassMIT
PlanetScale Neki Overviewplanetscale/database-skills708—~2.1kAutomated safety check: PassMIT

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Works with

Categories

Questions about Submit Product Directories V1 Batch

What does Submit Product Directories V1 Batch do?

SPD V1 Batch. An agent skill from flaqai/backlink_skills. Submit Product Directories V1 Batch is an agent skill from flaqai/backlink_skills. SPD V1 Batch.

When should I use Submit Product Directories V1 Batch?

Submit Product Directories V1 Batch fits situations like: coverage and operational throughput matter more than deep per-site quality analysis; ranking manipulation; paid-link acquisition; forced reciprocal links.

How do I install Submit Product Directories V1 Batch in Claude Code?

Run `npx skills add flaqai/backlink_skills --skill submit-product-directories-v1-batch -a claude-code`. Or copy the skill folder (submit-product-directories-v1-batch in flaqai/backlink_skills) into .claude/skills/submit-product-directories-v1-batch in your project. Claude Code loads it when a task matches its description.

How do I install Submit Product Directories V1 Batch in Codex?

Run `npx skills add flaqai/backlink_skills --skill submit-product-directories-v1-batch -a codex`. Or copy the skill folder (submit-product-directories-v1-batch in flaqai/backlink_skills) into .agents/skills/submit-product-directories-v1-batch in your project. Codex loads it when a task matches its description.

Can I use Submit Product Directories V1 Batch in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add flaqai/backlink_skills --skill submit-product-directories-v1-batch -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/submit-product-directories-v1-batch, .gemini/skills/submit-product-directories-v1-batch, .github/skills/submit-product-directories-v1-batch and .opencode/skills/submit-product-directories-v1-batch in your project.

What does Submit Product Directories V1 Batch need to run?

Going by SKILL.md and its folder, Submit Product Directories V1 Batch needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Submit Product Directories V1 Batch access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Submit Product Directories V1 Batch safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Submit Product Directories V1 Batch use?

Submit Product Directories V1 Batch is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Submit Product Directories V1 Batch use?

About 1.7k tokens (SKILL.md is roughly 6.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.5k tokens, read only when the agent opens those files.

What are the alternatives to Submit Product Directories V1 Batch?

Skills that share tags, products or a category with Submit Product Directories V1 Batch: MySQL Schema and Query Tuning (planetscale/database-skills, 708 stars), Vitess for PlanetScale (planetscale/database-skills, 708 stars), DeepChat Data Import Helper (ThinkInAIXYZ/deepchat, 6.4k stars) and Database Expert (cin12211/orca-q, 224 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Submit Product Directories V1 Batch?

flaqai (a GitHub organization) maintains it in flaqai/backlink_skills, which has 756 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on September 24, 2026.

Source: flaqai/backlink_skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.