adlc-levelup-specify

A tool for extracting and organizing reusable knowledge from arbitrary text using LLM-based NLP techniques.

Updated Apr 21, 2026

Search Tags:#specification #engineering #workflows #enrichment #extract #cdr

Ready to run

npx skills add https://github.com/chaimt/GarminShabbatMode --skill adlc-levelup-specify

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: adlc-levelup-specify
Source: https://github.com/chaimt/GarminShabbatMode/tree/main/.cursor/skills/adlc-levelup-specify
Command: npx skills add https://github.com/chaimt/GarminShabbatMode --skill adlc-levelup-specify

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Extract Context Directive Records (CDRs) from the current feature specification context to enable systematic knowledge capture and reuse across teams.

Core Features & Use Cases

Extract Context Directive Records (CDRs) from specs, plans, and task artifacts to form a searchable pattern library.
Enrich existing CDRs with feature-specific evidence, traces, and implementation links for better traceability.
Apply to feature development workflows that follow a spec-kit style structure to maintain consistent documentation and learning.

Quick Start

Invoke this skill on the current feature context to extract CDRs and update the .specify/drafts/cdr.md with enriched evidence.

Frequently Asked Questions about adlc-levelup-specify

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

FAQPage Schema

How do I extract reusable patterns from feature specifications?▼

Extract reusable patterns from feature specifications by reading artifacts like spec.md, plan.md, and trace.md to generate Context Directive Records. This process captures architectural patterns into a searchable library within .specify/drafts/cdr.md.

What are Context Directive Records in software engineering workflows?▼

Context Directive Records in software engineering workflows are structured artifacts that capture reusable implementation patterns and decisions from feature specs. They link specifications to commits and tasks, forming a systematic knowledge library for team reuse.

How do I enrich existing CDRs with implementation evidence and traces?▼

Enrich existing CDRs by applying this skill to your current feature context, which reads trace artifacts and links to commits. It updates the .specify/drafts/cdr.md file with feature-specific evidence blocks and enrichment metadata for better traceability.

Does CDR extraction work with any project structure or do I need a spec-kit setup?▼

CDR extraction requires a spec-kit style project structure to function correctly. It specifically relies on the specs/*, .specify/, and trace artifact directories to read feature specifications and output the enriched draft records.

Can I link feature specifications to commits and tasks during CDR extraction?▼

You can link feature specifications to commits and tasks during CDR extraction when those references are available in the context. The skill reads plan.md and trace.md artifacts, pulling available commit and task links into the output evidence blocks.

What are the limitations of extracting Context Directive Records from spec artifacts?▼

Limitations of extracting Context Directive Records include strict reliance on a spec-kit project structure and the presence of populated spec.md, plan.md, and trace.md artifacts. Without these structured inputs, the skill cannot generate meaningful enrichment metadata.