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performance

v0.2.0

Tune dlt pipeline performance — diagnose the bottleneck stage (extract, normalize, load) and apply the right lever: parallelism, workers, memory buffers, file rotation, and batching.

Claude Code1 Skill

By ScaleVector GmbHLicense: https://github.com/dlt-hub/dlthub-ai-workbench/blob/master/LICENSE59 GitHub starsUpdated 5 days ago

Directory evidence

Runtimes
Claude Code
Parsed components
1 skill or MCP entry
Source updated
Sep 18, 2026
Manifest status
Canonical path parsed

The directory validates manifest shape and source location. It does not execute the plugin or provide a security endorsement. Review the indexing methodology →

Install performance for Claude Code

Installs for the current user
claude plugin marketplace add IchenDEV/agent-plugin-mkt
claude plugin marketplace update agent-plugin-marketplace
claude plugin install performance@agent-plugin-marketplace

Paste and run these commands in a terminal with Claude Code. They add and refresh the PluginsMP catalog, then install this plugin.

The installer fetches third-party code from the source repository shown on this page. This directory validates manifest structure and source location, but does not perform a security audit; review the manifest, components, and source before installing.

Get the source manually
git clone https://github.com/dlt-hub/dlthub-ai-harness

Clone the source repository, then follow its setup instructions to add the plugin to a compatible client. The plugin root is workbench/performance/.

Plugin files

workbench/performance/
├── .claude-plugin/plugin.json
└── skills/optimize-performance/SKILL.md

Included Skills1

optimize-performanceskills/optimize-performance/SKILL.md

Make a dlt pipeline faster or lighter on memory. Use when the user says a pipeline is slow, takes too long, runs out of memory, uses too much RAM, or wants to optimize, speed up, parallelize, or increase throughput. Covers source-agnostic levers (parallelism, workers, buffers, file rotation); for source-specific tuning use the pipeline toolkit's own optimize skill. Also decides whether a dltHub platform job genuinely needs a larger instance (more memory/CPU) — the last resort after tuning.

Plugin manifests1

workbench/performance/.claude-plugin/plugin.json
{
  "name": "performance",
  "description": "Tune dlt pipeline performance — diagnose the bottleneck stage (extract, normalize, load) and apply the right lever: parallelism, workers, memory buffers, file rotation, and batching.",
  "version": "0.2.0",
  "author": {
    "name": "ScaleVector GmbH"
  },
  "homepage": "https://dlthub.com/docs",
  "repository": "https://github.com/dlt-hub/dlthub-ai-workbench",
  "license": "https://github.com/dlt-hub/dlthub-ai-workbench/blob/master/LICENSE",
  "keywords": [
    "dlthub",
    "performance",
    "optimization",
    "parallelism",
    "throughput",
    "memory"
  ]
}

For maintainers

If you maintain this plugin, link to this source-backed listing from your README so users can review its manifest and indexed components.

[performance on Agent Plugins Marketplace](https://pluginsmp.com/plugins/performance)