cloud-serverless-designer_skill

This skill designs cross-cloud serverless deployments for AWS, Azure, and GCP, optimizing IAM, event sources, and cold starts for scalable functions.
  • Python

5

GitHub Stars

2

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill williamzujkowski/cognitive-toolworks --skill cloud-serverless-designer

  • CHANGELOG.md1.1 KB
  • SKILL.md13.2 KB

Overview

This skill designs serverless function deployments across AWS Lambda, Azure Functions, and Google Cloud Functions. It produces platform-specific function definitions, event source mappings, IAM or identity configurations, and optimizations for cold start and cost. The skill supports basic (T1), production-ready (T2), and advanced orchestration (T3) outputs tailored to your inputs and validation rules.

How this skill works

Given the cloud_provider, runtime, trigger_type, memory_mb, and timeout_seconds, the skill validates inputs against provider limits and supported runtimes. It generates required artifacts: function configuration, event source wiring, deployment commands, and—at higher tiers—least-privilege IAM/managed identity, VPC settings, cold-start optimizations, and CI/CD or orchestration templates. Outputs follow a clear contract (function_config, event_source, deployment_command) and expand with iam_policy, vpc_config, cold_start_optimization, cost_estimate, orchestration, CI/CD, and observability sections when requested.

When to use it

  • Design event-driven architectures or convert app logic to serverless functions
  • Configure triggers and event sources for new or migrated workloads
  • Create production-ready deployments with least-privilege IAM and VPC access
  • Optimize functions for cold starts, package size, and concurrency
  • Generate deployment templates (SAM/Serverless/GCloud) and CI/CD pipelines

Best practices

  • Validate runtime and trigger compatibility before generating artifacts
  • Apply least-privilege IAM or managed identities and avoid hardcoded secrets
  • Start with 512 MB if memory unspecified, then load-test to right-size
  • Minimize package size and use layers or dependency separation to reduce cold starts
  • Use provisioned concurrency or platform-specific features for latency-sensitive paths
  • Configure DLQs, logging retention, and alarms for error/throttle monitoring

Example use cases

  • T1: Generate AWS Lambda SAM function + API Gateway mapping for a simple HTTP API
  • T2: Produce SAM template with IAM policy, VPC config, provisioned concurrency, and cost estimate for production
  • T2: Create Azure Function with managed identity, Timer trigger, and encrypted environment placeholders
  • T3: Design Step Functions workflow chaining multiple Lambdas with retries and observability
  • T3: Produce GitHub Actions pipeline for canary deployment and automated integration tests

FAQ

Provide cloud_provider (aws|azure|gcp), runtime, trigger_type, memory_mb, and timeout_seconds; unspecified items trigger sensible defaults or follow-up questions.

How are cold starts addressed?

Recommendations include minimizing package size, using platform optimizations (provisioned concurrency, SnapStart), choosing ARM64 where supported, and adding connection pooling or shared layers.

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cloud-serverless-designer skill by williamzujkowski/cognitive-toolworks | VeilStrat