What is File-First architecture?

File-First is a principle where AI agents read original source files directly using tools like Grep or Glob, avoiding the loss of context often found in pre-processed summaries or RAG systems.

Will reading full files use more tokens?

While it can increase token usage initially, it significantly reduces errors, debugging time, and rework by ensuring the AI has the correct information from the start.

How does the project detection script work?

The included analyze_codebase.py script scans the repository to identify the project type (e.g., Python, Node.js, Rust) and suggests the most important files for the agent to read first based on best practices.

Does this skill work with different agent personas?

Yes, the File-First principle is designed to be integrated into various agent personas including developers, architects, and QA engineers to maintain consistency across the development lifecycle.

Why is this better than using a Vector Database or RAG?

Summaries and Vector DB snippets can omit crucial logic or architectural nuance. Reading raw files ensures the agent sees the ground truth of the code, leading to fewer hallucinations and more reliable code changes.

File-First Codebase Analysis

Name: File-First Codebase Analysis
Author: resolve-io

byresolve-io

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开发者工具

Prioritizes direct source file inspection over RAG-based summaries to ensure high-fidelity codebase understanding and accurate implementation.

The File-First skill implements a core principle of the PRISM framework, directing Claude to engage with raw source code directly using tools like Glob and Grep rather than relying on potentially lossy summaries or vector database snippets. By following a structured workflow—Detect, Locate, Read, Cite, and Iterate—this skill ensures that agents maintain high context fidelity when entering unfamiliar repositories or performing deep architectural analysis. It includes a dedicated codebase analyzer that automatically identifies project types and suggests an optimal reading order, making AI interactions with complex software systems more predictable, intentional, and grounded in reality.

主要功能

01Line-level citation requirements to ensure all AI claims are backed by source code

02Structured 'Detect-Locate-Read-Cite-Iterate' workflow for systematic discovery

03Progressive disclosure strategy for loading reference documentation only when needed

04Direct source file reading to eliminate RAG-induced hallucinations and context loss

05Automated codebase analysis for project type and architectural structure detection

061 GitHub stars

使用场景

01Analyzing project structures to determine implementation strategies for new features

02Performing deep-dive code audits where exact file references and line citations are required

03Onboarding an AI agent into a large or unfamiliar repository for the first time

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add resolve-io/.prism file-first

For use in Claude.ai and ChatGPT

Download Skill