saf-feature-dev

Automate SAF feature development across frontend, core analysis, Python SDK, and CLI.

69|5|Updated Apr 7, 2026
One-click install
npx skills add https://github.com/Static-Analyzer-Factory/static-analyzer-factory --skill saf-feature-dev
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: saf-feature-dev
Source: https://github.com/Static-Analyzer-Factory/static-analyzer-factory/tree/main/skills/saf-feature-dev/claude-code/skills/saf-feature-dev
Command: npx skills add https://github.com/Static-Analyzer-Factory/static-analyzer-factory --skill saf-feature-dev

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill provides a disciplined, end-to-end workflow for SAF feature development, ensuring deterministic, AIR-only analysis and reproducible results across frontends, core analysis, Python SDK, and CLI work.

Core Features & Use Cases

  • 8-phase development workflow: from planning through wrap-up with artifacts like PROGRESS updates and traceable decisions.
  • Invariant enforcement: hard rules for determinism, AIR-only IR, and deterministic IDs to ensure cross-crate stability.
  • Guided exploration & validation: tool-mapped prompts, references, and checklists to structure exploration, design, testing, and review.
  • Use Case: when adding a new feature (e.g., a frontend or Python SDK extension), follow this workflow to plan, implement, test, and document changes, with dockerized experiments and CI-friendly steps.

Quick Start

Follow the eight-phase SAF feature development workflow to plan and implement SAF features from inception through validation.

Frequently Asked Questions about saf-feature-dev

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

FAQPage Schema
How do I structure a feature development workflow for multi-crate static analysis environments?

Feature development workflow for multi-crate static analysis environments is structured into eight phases from planning through wrap-up, generating PROGRESS updates and traceable decisions. It guides exploration, design, testing, and review with tool-mapped prompts and checklists.

What is AIR-only analysis and how does it enforce determinism in static analysis features?

AIR-only analysis enforces determinism by restricting intermediate representations to AIR-only IR and generating deterministic BLAKE3 IDs. This ensures reproducible results and cross-crate stability across frontend, core analysis, Python SDK, and CLI components.

Can I use this workflow for bug fixes and frontend extensions in a Python SDK?

Yes, the workflow applies to SAF feature additions, bug fixes, and workflow governance across frontend, core analysis, Python SDK, and CLI work. It uses dockerized experiments and CI-friendly steps to validate changes in these environments.

What's the best way to ensure documentation and tests are included in static analysis feature development?

The best way to ensure documentation and tests in static analysis feature development is following an invariant enforcement workflow that hard-requires both. It enforces determinism, AIR-only IR, and deterministic IDs while mandating documentation and tests across all phases.

Why do I need deterministic BLAKE3 IDs for cross-crate static analysis stability?

Deterministic BLAKE3 IDs are needed for cross-crate static analysis stability because they enforce invariant rules ensuring reproducible results. They guarantee that analysis outputs remain consistent and traceable across frontend, core analysis, Python SDK, and CLI work.

When should I not use an 8-phase structured workflow for feature development?

You should not use the 8-phase structured workflow for rapid prototyping or exploratory changes that do not require strict determinism, AIR-only analysis, mandatory documentation, or cross-crate stability enforcement in a multi-crate static analysis environment.