levelup-specify

Extract Context Directive Records from feature specification artifacts.

Updated Jul 7, 2026

Search Tags:#automation #architecture #documentation #patterns #knowledge-management #specify

Ready to run

npx skills add https://github.com/eavichay/mycli --skill levelup-specify-eavichay

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This skill solves the problem of knowledge fragmentation by extracting reusable patterns, rules, and architectural decisions from completed feature implementations into structured Context Directive Records (CDRs).

Core Features & Use Cases

Pattern Extraction: Automatically identifies reusable logic, rules, or personas from feature artifacts like specs, plans, and traces.
Evidence Linking: Connects extracted patterns directly to implementation evidence, including code snippets, commit SHAs, and task logs.
Use Case: After finishing a complex authentication feature, use this skill to document the specific error-handling patterns and security rules you implemented so they can be applied to future projects.

Quick Start

Run the levelup-specify skill to extract and document all reusable patterns from the current feature branch artifacts.

Frequently Asked Questions about levelup-specify

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

FAQPage Schema

How do I extract reusable architectural patterns from feature specifications?▼

You can extract reusable architectural patterns from feature specifications by analyzing project documentation, task logs, and trace files to generate structured Context Directive Records. This formalizes implementation rules for future reuse.

What is the best way to document architectural decisions after completing a feature?▼

Documenting architectural decisions after a feature implementation involves linking extracted patterns directly to evidence like code snippets and commit SHAs. This creates structured knowledge records that prevent knowledge fragmentation across projects.

Do I need a specific directory structure to formalize feature-based learnings?▼

Yes, formalizing feature-based learnings requires a configured .specify directory structure. You must also have access to existing feature implementation artifacts so the extraction process can validate and enrich the generated directives.

Can I capture security rules and error-handling patterns from task logs automatically?▼

Yes, you can automatically capture security rules and error-handling patterns from task logs. The pattern extraction process identifies reusable logic and rules from feature artifacts, linking them directly to implementation evidence.

Why should I use Context Directive Records instead of standard documentation for knowledge management?▼

Context Directive Records prevent knowledge fragmentation by structuring extracted patterns, rules, and architectural decisions with direct links to implementation evidence. Standard documentation often lacks this automated evidence linking to code snippets and commits.

What limitations exist when extracting patterns from feature implementation artifacts?▼

A key limitation is the dependency on a configured .specify directory and accessible feature artifacts. Without complete task logs, trace files, and specification artifacts, the extracted Context Directive Records cannot be fully validated or enriched.