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skmtc-architecture

Explain SKMTC architecture and guide infrastructure development around its code generation engine.

19|Updated Aug 27, 2025
Search Tags:#code generation#software architecture#provenance#infrastructure#skmtc#deno workers
One-click install
npx skills add https://github.com/skmtc/skmtc --skill skmtc-architecture
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: skmtc-architecture
Source: https://github.com/skmtc/skmtc/tree/main/deno/docs/skills/skmtc-architecture
Command: npx skills add https://github.com/skmtc/skmtc --skill skmtc-architecture

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill gives agents a precise mental model of SKMTC so they can design and extend infrastructure around the code generation engine without relying on incorrect assumptions about plugin registries, dependency graphs, worker execution, caching, or runtime behavior.

Core Features & Use Cases

  • Architecture Understanding: Explains the Parse, Generate, and Render pipeline, the engine and host layers, and the Deno Worker boundary.
  • Infrastructure Design: Guides development of hosted generation APIs, schema and generator registries, SaaS platforms, tracing systems, provenance tooling, and platform-level CI integrations.
  • Operational Contracts: Covers deterministic cold runs, memoization-based generator coordination, manifest diagnostics, attribution sidecars, generation maps, package dependencies, and security boundaries.
  • Use Case: Use this Skill when building a service around SKMTC that accepts OpenAPI or GraphQL schemas, executes bundled generators, caches complete results, persists manifests, and exposes provenance data for downstream tooling.

Quick Start

Ask the SKMTC architecture skill to explain how to build a hosted generation service around the toArtifacts engine while preserving its worker, manifest, caching, and provenance contracts.

Frequently Asked Questions about skmtc-architecture

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How does the Parse-Generate-Render pipeline work for code generation?▼

The Parse-Generate-Render pipeline processes schemas by parsing inputs, executing bundled generators, and rendering outputs within Deno Worker isolation to ensure deterministic code generation results. Memoization-based coordination and manifest diagnostics govern each stage transition.

How do I build a hosted generation API around a code generation engine?▼

Build a hosted generation API by accepting OpenAPI or GraphQL schemas, executing bundled generators in Deno Workers, caching complete deterministic results, persisting manifests, and exposing attribution sidecars alongside provenance data for downstream tooling.

What security boundaries does Deno Worker isolation provide for code generation?▼

Deno Worker isolation provides security boundaries by sandboxing the code generation engine, preventing unauthorized access to host resources while maintaining deterministic cold runs and memoization-based generator coordination across isolated execution environments.

Can I use schema registries and provenance tooling with a code generation engine?▼

Schema and generator registries integrate with the code generation engine by routing OpenAPI or GraphQL inputs through the Parse-Generate-Render pipeline, producing generation maps and attribution sidecars that provenance tooling and tracing systems consume.

What are the limitations of memoization-based coordination in code generation infrastructure?▼

Memoization-based coordination requires deterministic cold runs and complete result caching to function correctly; non-deterministic generators or broken manifest diagnostics will disrupt provenance tracking and invalidate cached generation outputs across the pipeline.

Does the code generation engine support platform-level CI integrations?▼

Platform-level CI integrations are supported by exposing generation maps, manifest diagnostics, and attribution sidecars from the Parse-Generate-Render pipeline, allowing CI systems to validate deterministic outputs and track provenance across code generation runs.

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