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skf-forger

Compile code repositories into evidence-backed agent skills using AST analysis.

Updated May 5, 2026
Search Tags:#drift detection#agent skills#skill testing#ast analysis#skill compilation#code to skill#progressive tiers
One-click install
npx skills add https://github.com/devalexanderdaza/bmad-any --skill skf-forger-devalexanderdaza
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: skf-forger
Source: https://github.com/devalexanderdaza/bmad-any/tree/main/.cursor/skills/skf-forger
Command: npx skills add https://github.com/devalexanderdaza/bmad-any --skill skf-forger-devalexanderdaza

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manually creating consistent, verified agent skills from codebases and documentation is time-consuming, error-prone, and often results in skills with hallucinated claims or misaligned functionality. This skill automates the entire skill compilation lifecycle to eliminate those risks.

Core Features & Use Cases

  • AST-Backed Compilation: Automatically analyzes source code, documentation, and developer inputs to generate accurate, evidence-backed skills where every claim is tied to a specific code source, line number, and confidence tier, with zero tolerance for hallucination.
  • Progressive Capability Tiers: Supports Quick, Forge, Forge+, and Deep compilation tiers to match your available tools and use case, from fast prototype skills for personal use to full production-ready suites for team distribution.
  • Use Case: For example, if you maintain an open-source CLI tool and want to create a reusable agent skill that helps other developers use its commands correctly, this skill automates source analysis, skill briefing, compilation, integrity testing, and ecosystem-ready export for platforms like Claude Code and Cursor.

Quick Start

Use the skf-forger skill to compile a verified agent skill from your project's codebase and export it for use with your preferred AI agent IDE.

Frequently Asked Questions about skf-forger

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

FAQPage Schema
How do I create AI agent skills from my codebase without hallucinated functionality?▼

AST-backed compilation eliminates hallucinated claims by analyzing source code and documentation to generate agent skills where every functional claim is tied to a specific code source, line number, and confidence tier. This automated process ensures generated skills accurately reflect your actual codebase.

What's the best way to automate skill authoring from source code for multiple AI agent IDEs?▼

Automated skill compilation analyzes source code and developer discourse end-to-end, then exports ecosystem-ready skills compatible with Claude Code, GitHub Copilot, OpenCode, and Cursor. This workflow includes source analysis, guided skill briefing, compilation, and integrity testing.

How does AST analysis work when generating agent skills from code repositories?▼

AST analysis parses your source code structure to extract verified functionality and map agent skill capabilities to exact code locations. This evidence-backed mechanism ensures generated skills precisely match your actual codebase behavior and available commands.

Can I compile agent skills for different use cases, from quick prototypes to production-ready suites?▼

Progressive capability tiers support Quick, Forge, Forge+, and Deep compilation modes to match your available tools and scale. This ranges from fast prototype skills for personal use to full production-ready suites for team distribution.

Does this skill compilation workflow support drift detection between generated skills and updated source code?▼

Automated drift detection monitors for inconsistencies between compiled agent skills and your current source code. It identifies when skill definitions fall out of alignment with codebase updates, triggering transactional operations like rename or deprecation to maintain accuracy.

Why do manually created agent skills often contain misaligned functionality and errors?▼

Manually creating consistent agent skills from codebases is time-consuming and error-prone, often resulting in skills with hallucinated claims or misaligned functionality. Automating the skill compilation lifecycle eliminates these risks by enforcing evidence-backed generation.

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