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bloat-detector膨胀检测器

Agent Skill

bloat-detector 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

1,236

周安装

52

GitHub Stars

264

下载量

433
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:bloat-detector(膨胀检测器)
来源仓库:https://github.com/athola/claude-night-market
仓库路径:skills/bloat-detector
安装命令:
npx skills add https://github.com/athola/claude-night-market --skill bloat-detector
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/athola/claude-night-market --skill bloat-detector

简介

bloat-detector 系统性识别并清理代码库中的冗余与低效内容,涵盖代码、文档、依赖与 Git 历史四大类。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中优化项目健康度,提升可维护性与构建性能。
  • 提供三级扫描策略:快速扫描、靶向分析与深度清理,按需选择工具链组合。
  • 无需复杂工具即可启动 Tier 1 检查,适合初步评估;高阶分析需配合静态扫描器与依赖图谱。
  • 使用前应备份关键分支,避免误删活跃代码或破坏 CI/CD 流水线稳定性。

SKILL.md

Bloat Detector

Systematically detect and eliminate codebase bloat through progressive analysis tiers.

Bloat Categories

CategoryExamples
CodeDead code, God classes, Lava flow, duplication
AI-GeneratedTab-completion bloat, vibe coding, hallucinated deps
DocumentationRedundancy, verbosity, stale content, slop
DependenciesUnused imports, dependency bloat, phantom packages
Git HistoryStale files, low-churn code, massive single commits

Quick Start

Tier 1: Quick Scan (2-5 min, no tools)

/bloat-scan

Detects: Large files, stale code, old TODOs, commented blocks, basic duplication

Tier 2: Targeted Analysis (10-20 min, optional tools)

/bloat-scan --level 2 --focus code   # or docs, deps

Adds: Static analysis (Vulture/Knip), git churn hotspots, doc similarity

Tier 3: Deep Audit (30-60 min, full tooling)

/bloat-scan --level 3 --report audit.md

Adds: Cross-file redundancy, dependency graphs, readability metrics

When To Use

DoDon't
Context usage > 30%Active feature development
Quarterly maintenanceTime-sensitive bugs
Pre-release cleanupCodebase < 1000 lines
Before major refactoringTools unavailable (Tier 2/3)

When NOT To Use

  • Active feature development
  • Time-sensitive bugs
  • Codebase < 1000 lines

Confidence Levels

LevelConfidenceAction
HIGH90-100%Safe to remove
MEDIUM70-89%Review first
LOW50-69%Investigate

Prioritization

Priority = (Token_Savings × 0.4) + (Maintenance × 0.3) + (Confidence × 0.2) + (Ease × 0.1)

Module Architecture

Tier 1 (always available):

  • See modules/quick-scan.md - Heuristics, no tools
  • See modules/git-history-analysis.md - Staleness, churn, vibe coding signatures
  • See modules/growth-analysis.md - Growth velocity, forecasts, threshold alerts

Tier 2 (optional tools):

  • See modules/code-bloat-patterns.md - Anti-patterns (God class, Lava flow)
  • See modules/ai-generated-bloat.md - AI-specific patterns (Tab bloat, hallucinations)
  • See modules/documentation-bloat.md - Redundancy, readability, slop detection
  • See modules/static-analysis-integration.md - Vulture, Knip

Shared:

  • See modules/remediation-types.md - DELETE, REFACTOR, CONSOLIDATE, ARCHIVE

Ecosystem-Level Detection

Patterns that span plugin boundaries or manifest configuration, discovered through ecosystem-wide audits.

alwaysApply Accumulation

Flag plugins with 3+ skills where alwaysApply: true. Each always-on skill injects its full text into every session, creating a baseline token floor before the user types anything. Sum the estimated_tokens fields to report total per-session cost.

Hook Registration Gaps

Compare hooks declared in plugin.json or openpackage.yml against entries in hooks.json. A hook present in hooks.json but absent from the manifest is invisible to the plugin loader and cannot be audited, versioned, or disabled through normal plugin management.

Boilerplate Footer Detection

Scan skill files for identical multi-line text blocks repeated across 10+ files (e.g., generic troubleshooting sections like "Command not found / Permission errors / Unexpected behavior"). These are copy-paste artifacts that inflate token cost without adding skill-specific value.

ToC Bloat in Skills

Skills loaded into model context gain nothing from HTML-style Tables of Contents. Detect ## Table of Contents followed by bulleted anchor-link lists. These waste tokens since the model reads sequentially, not via hyperlinks.

Unregistered Module Subdirectories

Compare files on disk in skills/*/modules/ against the modules: list in each skill's SKILL.md frontmatter. Files that exist on disk but are not listed in the manifest are invisible to progressive loading and may be dead weight or missing from the load path.

Auto-Exclusions

Always excludes: .venv, __pycache__, .git, node_modules, dist, build, vendor

Also respects: .gitignore, .bloat-ignore

Safety

  • Never auto-delete - all changes require approval
  • Dry-run support - --dry-run for previews
  • Backup branches - created before bulk changes

Related

  • bloat-auditor agent - Executes scans
  • unbloat-remediator agent - Safe remediation
  • context-optimization skill - MECW principles

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

27.66%
按下载量换算120

OpenCode

24.59%
按下载量换算106

Cursor

20.47%
按下载量换算89

Codex

13.71%
按下载量换算59

Antigravity

8.79%
按下载量换算38

Gemini CLI

3.55%
按下载量换算15

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

权限需确认

当前来源未能明确判断权限范围,默认进入异常复核队列。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

继续浏览同类 Skills