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Data chunking large datasets

Skill soden46/syarif-laravel-ai-skills/skills/data-chunking-large-datasets

Laravel guidance to process large datasets efficiently using chunk(), chunkById(), lazy(), and cursor() to reduce memory consumption and improve performanceFrom its SKILL.md

Install
npx -y skills add soden46/syarif-laravel-ai-skills --skill data-chunking-large-datasets

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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  • 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

2.0 KB, 358 tokens by cl100k_base, as published. Nobody here has run it

Data Chunking Large Datasets

Use this skill when a Laravel task involves data chunking large datasets.

This skill is adapted to the personal Laravel standards in this repository. It maps the public data-chunking-large-datasets topic from jpcaparas/superpowers-laravel into the local data-chunking-large-datasets catalog without copying third-party skill body text.

Syarif Defaults

  • Follow Laravel conventions before introducing custom abstractions.
  • Prefer project-local patterns when they are explicit and tested.
  • Keep controllers focused on HTTP orchestration.
  • Put validation, authorization, transactions, side effects, and integrations at clear boundaries.
  • Keep client names, credentials, internal URLs, provider secrets, and project-specific business rules out of reusable standards.
  • Verify important behavior with the smallest meaningful tests and quality checks.

Workflow

  1. Detect the Laravel version, PHP version, runner, package manager, and existing project conventions.
  2. Identify the smallest local skill set that overlaps this topic.
  3. Implement or review the change using Laravel-native APIs first.
  4. Add abstractions only when they reduce real complexity or protect a meaningful boundary.
  5. Run targeted tests and available quality checks before handoff.

Checkpoints

  • Authorization and validation boundaries are explicit.
  • Query shape, transactions, queues, cache, files, and external calls are intentional when touched.
  • User-facing behavior has feature, unit, browser, or integration tests at the right level.
  • Logs and errors are useful without exposing secrets or unnecessary personal data.
  • Documentation or proposals avoid importing source-project names or one-off business rules.

Related Skills

  • using-laravel-standards
  • architecture
  • testing
  • security

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most rag retrieval skills give in 358 tokens

Counted across 199 of the 213 authors here whose files we hold, read 2026-09-06

  • Enable caching for frequent queriesin 14 of 199, across 5 files
  • Enable MMR for diverse resultsin 12 of 199, across 5 files
  • Enable binary quantization to reduce memoryin 11 of 199, across 4 files
  • Initialize the database with dimensions matching the embedding modelin 11 of 199, across 4 files
  • Start the similarity threshold at 0.7in 11 of 199, across 4 files
  • Check database statistics when diagnosing slow searchin 11 of 199, across 4 files
  • Export and import vectors as JSONin 10 of 199, across 3 files
  • Match index dimension to the embedding modelin 10 of 199, across 9 files
  • Order filters cheap before expensivein 9 of 199, across 2 files
  • Generate a runnable scaffold in the user's stackin 9 of 199, across 2 files
  • Recommend multi-action scoring when frequent tuning is expectedin 9 of 199, across 2 files
  • Batch store documents for bulk insertsin 9 of 199, across 2 files

Said here and by no other author read

  • Detect Laravel version, PHP version, runner, and conventions
  • Identify the smallest overlapping local skill set
  • Implement changes using Laravel-native APIs first
  • Add abstractions only when they reduce real complexity
  • Run targeted tests and quality checks before handoff
  • Keep controllers focused on HTTP orchestration

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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Skills are one crate of 626,045. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.