How does this skill improve Langdock action performance?

It utilizes JavaScript's asynchronous capabilities to trigger multiple API requests simultaneously instead of waiting for each to finish one-by-one, significantly reducing total execution time.

What is the difference between Pattern A and Pattern B in this skill?

Pattern A uses Promise.all for 'fail-fast' scenarios where all data is required, while Pattern B uses Promise.allSettled for fault tolerance, allowing the action to succeed even if some requests fail.

Does this work with authenticated API calls?

Yes, all patterns demonstrate how to pass authentication headers and API keys from the Langdock data.auth object to multiple parallelized requests securely.

Can I use this for APIs with strict rate limits?

Yes, the skill includes patterns for dynamic batching and chunking to ensure you don't overwhelm external APIs while still maintaining execution efficiency.

Langdock Parallel Actions

Name: Langdock Parallel Actions
Author: mberto10

bymberto10

0•

API Development

Optimizes Langdock action performance by implementing parallel API execution patterns and fault-tolerant request handling.

This skill empowers developers to build high-performance Langdock actions by moving beyond slow, sequential API calls to efficient parallel execution patterns. It provides standardized implementations for independent parallel requests, fault-tolerant batching using Promise.allSettled, and complex sequential-parallel hybrid workflows. By reducing latency and organizing multi-endpoint data aggregation, it allows AI assistants to fetch comprehensive datasets—such as financial dashboards or portfolio summaries—in a single, robust action call while gracefully handling partial failures.

Key Features

01Standardized parallel execution patterns using Promise.all and Promise.allSettled

02Dynamic batching and request chunking to respect API rate limits

03Optimized response structures for clean data aggregation and metadata tracking

04Fault-tolerant request handling to prevent action failure on partial API errors

050 GitHub stars

06Hybrid sequential-parallel workflow structures for dependent data fetching

Use Cases

01Developing robust data aggregators that continue functioning even if a secondary API provider is down

02Building portfolio summary tools that fetch live data for dozens of assets in parallel batches

03Creating comprehensive financial dashboards that pull from multiple ticker endpoints simultaneously

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add mberto10/mberto-compound parallel-actions

For use in Claude.ai and ChatGPT

Key Features

01Standardized parallel execution patterns using Promise.all and Promise.allSettled

02Dynamic batching and request chunking to respect API rate limits

03Optimized response structures for clean data aggregation and metadata tracking

04Fault-tolerant request handling to prevent action failure on partial API errors

050 GitHub stars

06Hybrid sequential-parallel workflow structures for dependent data fetching

Use Cases

01Developing robust data aggregators that continue functioning even if a secondary API provider is down

02Building portfolio summary tools that fetch live data for dozens of assets in parallel batches

03Creating comprehensive financial dashboards that pull from multiple ticker endpoints simultaneously