context-layer-generator

Name: context-layer-generator
Availability: InStock
Author: az9713

Generate MODULE_MANIFEST.md, BEHAVIORAL_CONTRACTS.md, and DECISION_LOG.md for a code module.

Updated Apr 13, 2026

Search Tags:#documentation #context #onboarding #decision-log #behavioral-contracts #module-manifest

Ready to run

npx skills add https://github.com/az9713/dark-code-skills --skill context-layer-generator

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: context-layer-generator
Source: https://github.com/az9713/dark-code-skills/tree/main/skills/context-layer-generator
Command: npx skills add https://github.com/az9713/dark-code-skills --skill context-layer-generator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Generates three durable context layer artifacts for a code module to permanently preserve tacit knowledge when authors have left or when documentation is missing.

Core Features & Use Cases

MODULE_MANIFEST.md — maps dependencies, data flows, ownership, and integration points.
BEHAVIORAL_CONTRACTS.md — details interface guarantees, idempotency, failure modes, and performance bounds.
DECISION_LOG.md — records why decisions were made and any warnings about reversals.
Use cases include onboarding new engineers, auditing legacy code, and guiding AI agent edits without breaking existing systems.

Quick Start

Provide the target module path to generate the three context artifacts.

Frequently Asked Questions about context-layer-generator

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

FAQPage Schema

How do I document a code module to preserve tacit knowledge for onboarding?▼

To document a code module for onboarding, generate durable context artifacts like a module manifest, behavioral contracts, and a decision log. These files map dependencies, interface guarantees, and decision rationale to permanently preserve tacit knowledge as code evolves.

What is the best way to prepare a legacy codebase for AI agent modifications?▼

The best way to prepare legacy code for AI agent modifications is generating context layer artifacts. Producing manifests, behavioral contracts, and decision logs ensures agents understand ownership, data flows, and failure modes before editing unfamiliar modules.

How do I create a decision log and behavioral contracts for an existing code module?▼

You create a decision log and behavioral contracts for an existing module by analyzing its codebase. This generates BEHAVIORAL_CONTRACTS.md detailing interface guarantees and failure modes, plus DECISION_LOG.md recording why architectural decisions were made.

Can I generate a module manifest that maps dependencies and data flows automatically?▼

Yes, you can generate a module manifest that maps dependencies and data flows automatically. By providing a target module path, the system produces a MODULE_MANIFEST.md detailing ownership, integration points, and explicit warnings for unknown connections.

Why does my code audit need behavioral contracts and decision logs?▼

Your code audit needs behavioral contracts and decision logs to prevent breaking existing systems during edits. These artifacts document interface idempotency, performance bounds, and decision reversal warnings that are critical when authors have left.