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mytechsonamy
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repo-fingerprint

Identify languages, frameworks, and toolchains from package manifests and repository layout.

1|Updated Apr 12, 2026
Search Tags:#codebase#frameworks#build-tools#languages#risk-hotspots#fingerprint#module-layout
One-click install
npx skills add https://github.com/mytechsonamy/VibeFlow --skill repo-fingerprint
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: repo-fingerprint
Source: https://github.com/mytechsonamy/VibeFlow/tree/main/skills/repo-fingerprint
Command: npx skills add https://github.com/mytechsonamy/VibeFlow --skill repo-fingerprint

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Produces an evidence-backed snapshot of an existing codebase by identifying languages, frameworks, test runners, build tools, and module layout to guide VibeFlow adoption and risk assessment.

Core Features & Use Cases

  • Detects package manifests (e.g., package.json, pyproject.toml, go.mod, Cargo.toml, pom.xml, build.gradle) to establish the stack and guide integration planning.
  • Produces a compact fingerprint at .vibeflow/artifacts/repo-fingerprint.json with per-field evidence for audit and traceability.
  • Highlights hotspots and supports planning and test-strategy decisions during brownfield adoption.

Quick Start

Run the repo-fingerprint step during vibeflow:init on brownfield projects to generate the fingerprint artifact for planning.

Frequently Asked Questions about repo-fingerprint

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

FAQPage Schema
How do I generate a codebase fingerprint for brownfield adoption planning?▼

To generate a codebase fingerprint, run the repo-fingerprint step during vibeflow:init on brownfield projects. It identifies languages, frameworks, and toolchains, producing a structured fingerprint artifact at .vibeflow/artifacts/repo-fingerprint.json for planning.

What is a repository fingerprint and how does it support risk assessment?▼

A repository fingerprint is an evidence-backed snapshot of an existing codebase identifying languages, frameworks, test runners, build tools, and module layout. It supports risk assessment by highlighting hotspots to guide planning and test-strategy decisions.

Does the fingerprint analysis detect package manifests like pyproject.toml and go.mod?▼

Yes, the fingerprint analysis detects package manifests including package.json, pyproject.toml, go.mod, Cargo.toml, pom.xml, and build.gradle. Detecting these manifests establishes the stack and guides integration planning for the codebase.

Can I trace the evidence used to identify frameworks and toolchains in the fingerprint output?▼

Yes, you can trace evidence because the fingerprint includes per-field evidence for audit and traceability. The output artifact at .vibeflow/artifacts/repo-fingerprint.json provides structured data linking identified frameworks and toolchains to their source.

How do import graphs and repository layout factor into the fingerprinting process?▼

Import graphs and repository layout are scoped within the fingerprinting process to identify module structures and dependencies. Analyzing these components produces a comprehensive snapshot supporting brownfield adoption and test-strategy planning.

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