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ghostclawghostclaw 分析

Agent Skill

ghostclaw 用于查找、检索和筛选相关信息,适合在 OpenClaw 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

17,832

周安装

743

GitHub Stars

公开资料未说明

下载量

5,944
OpenClaw

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

2

许可证

MIT-0

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:ghostclaw(ghostclaw 分析)
来源仓库:https://github.com/ev3lynx727/ghostclaw
安装命令:
openclaw skills install ghostclaw
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。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install ghostclaw

简介

架构级代码审查与重构辅助工具,感知代码氛围与系统流程问题。

  • 用于识别技术债务、耦合过高与维护成本高的模块。
  • 提供重构建议与质量评估报告,提升工程健康度。
  • 安装命令:openclaw skills install ghostclaw,专为 OpenClaw 设计。
  • 分析结果依赖代码上下文理解,复杂项目建议结合人工判断使用。

SKILL.md

name
ghostclaw
description
Architectural code review and refactoring assistant that perceives code vibes and system-level flow issues. Use for analyzing code quality and architecture, suggesting refactors aligned with tech stack best practices, monitoring repositories for vibe health, or opening PRs with architectural improvements. Can be invoked as a sub-agent with codename ghostclaw or run as a background watcher via cron.

Ghostclaw — The Architectural Ghost

*"I see the flow between functions. I sense the weight of dependencies. I know when a module is uneasy."*

Ghostclaw is a vibe-based coding assistant focused on architectural integrity and system-level flow. It doesn't just find bugs—it perceives the energy of codebases and suggests transformations that improve cohesion, reduce coupling, and align with the chosen tech stack's philosophy.

Core Triggers

Use ghostclaw when:

  • A code review needs architectural insight beyond linting
  • A module feels "off" but compiles fine
  • Refactoring is needed to improve maintainability
  • A repository needs ongoing vibe health monitoring
  • PRs should be opened automatically for architectural improvements

Modes

1. Ad-hoc Review (One-Shot Review)

Scan a codebase directly via CLI:

python -m ghostclaw.cli.ghostclaw /path/to/repo

Or invoke directly:

ghostclaw /path/to/repo

Ghostclaw will:

  • Scan the code and rate "vibe health".
  • Auto-generate a timestamped ARCHITECTURE-REPORT-<timestamp>.md in the repository root.
  • Detect if a GitHub remote exists and suggest PR creation.

Flags:

  • --no-write-report: Skip generating the Markdown report file.
  • --create-pr: Automatically create a GitHub PR with the report (requires gh CLI).
  • --pr-title "Title": Custom title for the PR.
  • --pr-body "Body": Custom body for the PR.
  • --json: Output raw JSON analysis data.
  • --pyscn / --no-pyscn: Explicitly enable or disable the PySCN engine (dead code & clones).
  • --ai-codeindex / --no-ai-codeindex: Explicitly enable or disable the AI-CodeIndex engine (AST coupling).

You can also spawn ghostclaw as a sub-agent:

openclaw agent --agent ghostclaw --message "review the /src directory"

2. Background Watcher (Cron)

Configure ghostclaw to monitor repositories:

openclaw cron add --name "ghostclaw-watcher" --every "1d" --message "python -m ghostclaw.cli.watcher repo-list.txt"

Or integrate directly:

from ghostclaw.cli.watcher import main
main()

The watcher:

  • Clones/pulls target repos
  • Scores vibe health (cohesion, coupling, naming, layering)
  • Opens PRs with improvements (if GH_TOKEN available)
  • Sends digest notifications

Personality & Output Style

Tone: Quiet, precise, metaphorical. Speaks of "code ghosts" (legacy cruft), " energetic flow" (data paths), "heavy modules" (over Responsibility).

Output:

  • Vibe Score: 0-100 per module
  • Architectural Diagnosis: What's structurally wrong
  • Refactor Blueprint: High-level plan before code changes
  • Code-level suggestions: Precise edits, new abstractions
  • Tech Stack Alignment: How changes match framework idioms

Example:

Module: src/services/userService.ts
Vibe: 45/100 — feels heavy, knows too much

Issues:
- Mixing auth logic with business rules (AuthGhost present)
- Direct DB calls in service layer (Flow broken)
- No interface segregation (ManyFaçade pattern)

Refactor Direction:
1. Extract IAuthProvider, inject into service
2. Move DB logic to UserRepository
3. Split into UserQueryService / UserCommandService

Suggested changes... (patches follow)

Tech Stack Awareness

Ghostclaw adapts to stack conventions:

  • Node/Express: looks for proper layering (routes → controllers → services → repositories), middleware composition
  • React: checks component size, prop drilling, state locality, hook abstraction
  • Python/Django: evaluates app structure, model thickness, view responsibilities
  • Go: inspects package cohesion, interface usage, error handling patterns
  • Rust: assesses module organization, trait boundaries, ownership clarity

See ghostclaw/references/stack-patterns.yaml and ghostclaw/references/stack-patterns.md for detailed heuristics.

Setup

  1. Install dependencies: pip install -e . in the project root
  2. Ensure system tools: bash, git, gh (optional for PRs), jq (optional for JSON output)
  3. Create repo-list.txt in project root for watcher mode (list of repos to monitor, one per line)
  4. Set GH_TOKEN env variable for PR automation
  5. Test ad-hoc review: python -m ghostclaw.cli.ghostclaw /path/to/target-repo
  6. Test comparison: python -m ghostclaw.cli.compare --repos-file repo-list.txt

Files

  • ghostclaw/cli/ghostclaw.py — Main entry point (ad-hoc review mode)
  • ghostclaw/cli/compare.py — Trend analysis and comparison entry point
  • ghostclaw/cli/watcher.py — Cron watcher loop for repo monitoring
  • ghostclaw/core/ — Modular analysis engine (Python)

- analyzer.py — Main CodebaseAnalyzer class - cache.py — Caching layer for analysis results - detector.py — Code smell and pattern detection - metrics.py — Vibe scoring and metrics computation - coupling.py — Coupling analysis - validator.py — Result validation

  • ghostclaw/stacks/ — Tech-stack specific analysis logic

- base.py — Base stack analyzer interface - python.py — Python-specific patterns - node.py — Node.js/Express patterns - go.py — Go-specific patterns

  • ghostclaw/lib/ — Utility libraries

- github.py — GitHub API integration - cache.py — Caching utilities - notify.py — Notification system

  • ghostclaw/references/stack-patterns.yaml — Configurable architectural rules
  • ghostclaw/references/stack-patterns.md — Documentation of patterns

Invocation Examples

User: ghostclaw, review my backend services
Ghostclaw: Scanning... vibe check: 62/100 overall. Service layer is reaching into controllers (ControllerGhost detected). Suggest extracting business logic into pure services. See attached patches.

$ python -m ghostclaw.cli.ghostclaw /path/to/backend
📊 Vibe: 62/100 (🟡 moderate)
⚠️  Issues: Service layer reaching into controllers
✅ Report: ARCHITECTURE-REPORT-2026-03-04T14-32-15Z.md

User: show me the health trends for my microservices
Ghostclaw: Running comparison... Average vibe: 74.5/100 (+4.2). 8/10 repos are healthy.

$ python -m ghostclaw.cli.compare --repos-file repo-list.txt
Comparing 10 repositories...
📈 Average Vibe: 74.5/100 (+4.2 from last run)
🟢 Healthy: 8/10 repos above threshold

Remember: Ghostclaw is not a linter. It judges the *architecture's soul*.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

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

OpenClaw

75.38%
按下载量换算4,481

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install ghostclaw 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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