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home / skills / jeremylongshore / claude-code-plugins-plus-skills / processing-api-batches

processing-api-batches skill

/plugins/api-development/api-batch-processor/skills/processing-api-batches

This skill helps optimize bulk API operations by batching, throttling, and parallel execution to improve throughput and reliability.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill processing-api-batches

Review the files below or copy the command above to add this skill to your agents.

Files (4)
SKILL.md
2.4 KB
---
name: processing-api-batches
description: |
  Optimize bulk API requests with batching, throttling, and parallel execution.
  Use when processing bulk API operations efficiently.
  Trigger with phrases like "process bulk requests", "batch API calls", or "handle batch operations".
  
allowed-tools: Read, Write, Edit, Grep, Glob, Bash(api:batch-*)
version: 1.0.0
author: Jeremy Longshore <[email protected]>
license: MIT
---

# Processing Api Batches

## Overview


This skill provides automated assistance for api batch processor tasks.
This skill provides automated assistance for the described functionality.

## Prerequisites

Before using this skill, ensure you have:
- API design specifications or requirements documented
- Development environment with necessary frameworks installed
- Database or backend services accessible for integration
- Authentication and authorization strategies defined
- Testing tools and environments configured

## Instructions

1. Use Read tool to examine existing API specifications from {baseDir}/api-specs/
2. Define resource models, endpoints, and HTTP methods
3. Document request/response schemas and data types
4. Identify authentication and authorization requirements
5. Plan error handling and validation strategies
1. Generate boilerplate code using Bash(api:batch-*) with framework scaffolding
2. Implement endpoint handlers with business logic
3. Add input validation and schema enforcement
4. Integrate authentication and authorization middleware
5. Configure database connections and ORM models
1. Write integration tests covering all endpoints


See `{baseDir}/references/implementation.md` for detailed implementation guide.

## Output

- `{baseDir}/src/routes/` - Endpoint route definitions
- `{baseDir}/src/controllers/` - Business logic handlers
- `{baseDir}/src/models/` - Data models and schemas
- `{baseDir}/src/middleware/` - Authentication, validation, logging
- `{baseDir}/src/config/` - Configuration and environment variables
- OpenAPI 3.0 specification with complete endpoint definitions

## Error Handling

See `{baseDir}/references/errors.md` for comprehensive error handling.

## Examples

See `{baseDir}/references/examples.md` for detailed examples.

## Resources

- Express.js and Fastify for Node.js APIs
- Flask and FastAPI for Python APIs
- Spring Boot for Java APIs
- Gin and Echo for Go APIs
- OpenAPI Specification 3.0+ for API documentation

Overview

This skill optimizes bulk API operations by batching requests, applying throttling policies, and executing work in parallel to maximize throughput while protecting upstream services. It helps design, implement, and test scalable batch processors and generates the artifacts needed for deployment and documentation. Use it to turn large-volume API tasks into efficient, predictable pipelines.

How this skill works

The skill inspects API requirements, identifies resources and endpoints suitable for batching, and proposes batching strategies (fixed-size, time-window, or adaptive). It generates implementation guidance for request grouping, concurrency controls, exponential backoff, and circuit-breaker patterns. It also produces integration test plans and OpenAPI-compatible documentation to validate behavior under load.

When to use it

  • Processing large lists of API requests in a single workflow
  • Reducing per-request overhead by grouping similar operations
  • Preventing upstream rate-limit or quota violations with throttling
  • Parallelizing independent tasks while keeping overall concurrency bounded
  • Implementing retry and error aggregation for bulk operations

Best practices

  • Choose batching size based on payload size, latency goals, and downstream limits
  • Apply backpressure and bounded concurrency to avoid resource exhaustion
  • Use idempotent operations or deduplication to make retries safe
  • Expose batch-level status and partial-failure reporting in responses
  • Document rate limits and retry semantics in the API spec

Example use cases

  • Bulk user onboarding: validate and create hundreds of users with controlled concurrency
  • Mass-update records: apply schema-safe updates across many database rows via batched API calls
  • Event forwarding: buffer high-rate events, batch them, and forward to downstream services
  • Third-party API integration: aggregate client calls into fewer upstream requests to respect external quotas
  • ETL ingestion: parallelize data transformation tasks with coordinated batching and retries

FAQ

How do I choose a batch size?

Start with conservative sizes based on payload and latency targets, then load-test and tune. Monitor success rate, latency, and downstream error rates to adjust batch size dynamically.

How are partial failures handled?

Return batch-level results that indicate per-item success or failure, retry only failed and idempotent items, and surface diagnostics for manual review when needed.