# onevcat/FengNiao

A command line tool for cleaning unused resources in Xcode.

Repository: https://github.com/onevcat/FengNiao
Canonical: https://ross.abutalabs.com/products/fengniao
Language: Swift
License: MIT
License Family: permissive
Topics: command-line-tool, resources, slim, xcode, images, cleanup, swift
Last push: 2026-05-09T06:43:43+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 81, release rhythm 59, longevity 100
- inputs: {"age_days": 3467, "days_push": 116, "days_rel": 116, "gap_med": 96, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3574, forks 246 (observed 2026-08-28T04:08:10.791039+00:00)

## What it is
FengNiao is a Swift command-line utility that scans an Xcode project to find and delete unused image and resource files. It searches resource references across source files and can also clean up .pbxproj references.

## Use cases
- find unused images in an Xcode project
- clean up unused assetsets from an iOS app
- slim down an iOS app bundle by removing dead resources
- delete unreferenced pngs and pdfs from a Swift project
- list unused resource files before deleting them

## When to choose
- you maintain an Xcode/iOS project with bloated asset catalogs
- you want a simple scriptable cleanup step before releases
- you prefer a lightweight Swift CLI over heavy static analysis tools

## When to avoid
- your project is not an Xcode project
- resources are referenced dynamically in ways the tool cannot detect (string-built names, server-driven assets)
- you have no version control or backup, since deletion is irreversible

## Facets
- artifact type: cli-tool
- maturity: maintenance
- function: cli, developer-tools, file-system
- domain: developer-tools, mobile-development
- platform: cli
- tags: xcode, unused-resources, image-cleanup, ios, swift, command-line, macos, linux

## Member repositories
- onevcat/FengNiao (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:10.791039+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:34:04.282372+00:00, confidence not recorded.
  - readme: https://github.com/onevcat/FengNiao (fetched 2026-08-28T04:08:10.791039+00:00, sha af7143a1014b)
- Data as of 2026-08-30T08:39:29.467469+00:00.