SKILL·38935D

lsp-architecture

blackwell-systems
更新于 3 days ago
1 次查看
154
18
154
在 GitHub 上查看
元general

关于

This Claude Skill generates a comprehensive architectural overview of a codebase using LSP analysis. It provides the big picture by identifying languages, package structure, entry points, dependency graphs, and hotspots in a single call. It's ideal for developers needing to quickly understand a new or complex project's structure without modifying files.

快速安装

Claude Code

推荐
主要方式
npx skills add blackwell-systems/agent-lsp -a claude-code
插件命令备选方式
/plugin add https://github.com/blackwell-systems/agent-lsp
Git 克隆备选方式
git clone https://github.com/blackwell-systems/agent-lsp.git ~/.claude/skills/lsp-architecture

在 Claude Code 中复制并粘贴此命令以安装该技能

技能文档

Requires the agent-lsp MCP server.

lsp-architecture

Generate a structural architecture overview of any codebase: language distribution, package hierarchy, entry points, dependency flow, and hotspot files. One invocation for the big picture.

Read-only; does not modify any files.

Invocation: User provides the workspace root path (e.g. "/home/user/myproject"). If omitted, use the current working directory.


Depth Controls (hard limits)

These limits are strict constraints. Never exceed them:

  • Package enumeration: cap at 30 packages
  • Hotspot analysis: cap at 10 files
  • Workspace symbol queries: cap at 5 queries
  • Do NOT recurse into vendor/, node_modules/, .git/, or other dependency directories

Step 0 — Initialize

If LSP is not yet running, start it with the workspace root:

mcp__lsp__start_lsp({
  "workspace_root": "<workspace-root>"
})

Then detect which language servers are available:

mcp__lsp__detect_lsp_servers({
  "workspace_root": "<workspace-root>"
})
→ returns: list of detected servers with language names and file patterns

Record the available languages and their file globs. This determines which queries to run in later steps.


Step 1 — Language Detection

Scan the workspace to determine language distribution. Use file extension counts from the detected servers and supplement with a filesystem scan (via Glob tool) to count files per language.

For each detected language, report:

  • Language name
  • File count
  • Estimated lines of code (sample 3-5 representative files and extrapolate)

Skip files in vendor/, node_modules/, .git/, dist/, build/, and other common dependency or output directories.


Step 2 — Package Structure

Use find_symbol with broad queries to discover the package and module hierarchy. Tailor queries by language:

Go:

mcp__lsp__find_symbol({
  "query": "",
  "symbol_kind_filter": "Package"
})

Also query for top-level types and functions to fill in package-level detail:

mcp__lsp__find_symbol({
  "query": "",
  "symbol_kind_filter": "Function"
})

Python:

mcp__lsp__find_symbol({
  "query": "",
  "symbol_kind_filter": "Class"
})

TypeScript/JavaScript:

mcp__lsp__find_symbol({
  "query": "",
  "symbol_kind_filter": "Function"
})

From the returned symbols, extract the directory paths and build a tree of the package hierarchy. Group symbols by their containing directory. For each package/directory, note:

  • Path relative to workspace root
  • Brief description (inferred from symbol names and directory name)
  • Approximate symbol count

Cap at 30 packages. If more than 30 directories contain symbols, keep only the top 30 by symbol count and note that others were omitted.

Cap workspace symbol queries at 5 total across all of Step 2.


Step 3 — Entry Points

Use find_symbol to search for common entry point patterns:

mcp__lsp__find_symbol({
  "query": "main"
})

Also search for other common entry point names (use a single additional query if needed): "Run", "Serve", "Handler", "App", "Main".

Identify and categorize:

  • CLI entrypoints: main functions, Run or Execute commands
  • HTTP handlers: Handler, Serve, ListenAndServe patterns
  • Test suites: top-level test files (note count only, do not list individually)

List each entry point with file:line.


Step 4 — Hotspot Analysis

Identify the files with the highest symbol density (most exports), then measure their blast radius.

4a — Find candidate files

From the symbols discovered in Step 2, count exported symbols per file. Select the top 10 files by exported symbol count.

4b — Measure blast radius

For each candidate file, call blast_radius:

mcp__lsp__blast_radius({
  "changed_files": ["<absolute-path-to-file>"],
  "include_transitive": false
})
→ returns: affected_symbols, test_callers, non_test_callers

This tool uses a persistent cache, so repeated calls on the same file are instant.

4c — Rank hotspots

Rank files by total non_test_callers count (descending). Files with the most non-test callers are the architectural hotspots: changing them has the widest blast radius.


Step 5 — Output

Produce the architecture report in this format:

## Architecture Overview: <project-name>

### Languages
- Go: 150 files (~15K lines)
- TypeScript: 30 files (~3K lines)

### Package Map
cmd/agent-lsp/     (entrypoint, CLI routing)
internal/lsp/      (LSP client, process management)
internal/tools/    (MCP tool handlers)
internal/session/  (speculative execution sessions)
...

### Entry Points
- cmd/agent-lsp/main.go:55 (main)
- cmd/agent-lsp/server.go:276 (Run)

### Hotspots (most referenced files)
1. internal/lsp/client.go: 150+ callers across 30 files
2. internal/tools/helpers.go: 80 callers across 20 files
...

### Dependency Flow
cmd/ -> internal/tools/ -> internal/lsp/ -> (gopls subprocess)
         |-> internal/session/ -> internal/lsp/

Report sections

Languages: One line per language with file count and estimated LOC.

Package Map: Directory tree with a parenthetical description of each package's role. Cap at 30 entries.

Entry Points: Each with file:line and a parenthetical label (e.g. (main), (HTTP handler), (CLI command)).

Hotspots: Ranked list of the most-referenced files. For each, show the total non-test caller count and the number of distinct files containing callers.

Dependency Flow: A simple ASCII arrow diagram showing how the top-level packages depend on each other. Infer this from the hotspot caller data and package structure. Keep it concise: show the primary flow paths, not every edge.


Example

Goal: architecture overview of /home/user/agent-lsp

Step 0 — Initialize
  start_lsp: workspace_root="/home/user/agent-lsp"
  detect_lsp_servers:
  → Go (gopls): *.go
  → detected 1 language server

Step 1 — Language Detection
  Glob: **/*.go (excluding vendor/) → 85 files
  Sample 5 files, average 180 lines → estimate ~15K total lines
  → Go: 85 files (~15K lines)

Step 2 — Package Structure
  find_symbol: query="", symbol_kind_filter="Package"
  → 12 packages found

  find_symbol: query="", symbol_kind_filter="Function"
  → 240 functions across 12 packages

  Package map:
    cmd/agent-lsp/       (entrypoint, CLI routing, 15 symbols)
    internal/lsp/        (LSP client lifecycle, 45 symbols)
    internal/tools/      (MCP tool handlers, 60 symbols)
    internal/session/    (speculative execution, 25 symbols)
    internal/protocol/   (LSP protocol types, 30 symbols)
    skills/              (embedded skill definitions, 5 symbols)

Step 3 — Entry Points
  find_symbol: query="main"
  → cmd/agent-lsp/main.go:55 main()

  find_symbol: query="Run"
  → cmd/agent-lsp/server.go:276 Run()
  → cmd/agent-lsp/daemon.go:40 RunDaemon()

  Entry points:
  - cmd/agent-lsp/main.go:55 (main)
  - cmd/agent-lsp/server.go:276 (Run, server lifecycle)
  - cmd/agent-lsp/daemon.go:40 (RunDaemon, background mode)

Step 4 — Hotspot Analysis
  Top files by symbol count:
    1. internal/lsp/client.go (22 exported symbols)
    2. internal/tools/helpers.go (18 exported symbols)
    3. internal/protocol/types.go (15 exported symbols)

  blast_radius on each:
    internal/lsp/client.go → 150 non-test callers across 30 files
    internal/tools/helpers.go → 80 non-test callers across 20 files
    internal/protocol/types.go → 60 non-test callers across 15 files

Step 5 — Output

## Architecture Overview: agent-lsp

### Languages
- Go: 85 files (~15K lines)

### Package Map
cmd/agent-lsp/       (entrypoint, CLI routing)
internal/lsp/        (LSP client, process management)
internal/tools/      (MCP tool handlers)
internal/session/    (speculative execution sessions)
internal/protocol/   (LSP protocol types)
skills/              (embedded skill definitions)

### Entry Points
- cmd/agent-lsp/main.go:55 (main)
- cmd/agent-lsp/server.go:276 (Run)
- cmd/agent-lsp/daemon.go:40 (RunDaemon)

### Hotspots (most referenced files)
1. internal/lsp/client.go: 150 callers across 30 files
2. internal/tools/helpers.go: 80 callers across 20 files
3. internal/protocol/types.go: 60 callers across 15 files

### Dependency Flow
cmd/agent-lsp/ -> internal/tools/ -> internal/lsp/ -> (gopls subprocess)
                    |-> internal/session/ -> internal/lsp/
                    |-> internal/protocol/

GitHub 仓库

blackwell-systems/agent-lsp
路径: skills/lsp-architecture
0
agentskillsai-agentsai-toolingclaudeclaude-codecode-intelligence
FAQ

常见问题

什么是 lsp-architecture Skill?

lsp-architecture 是一个 Claude Skill,作者为 blackwell-systems。Skill 将 Claude 按需加载的说明和资源打包,让 Claude 无需额外提示即可执行与 lsp-architecture 相关的任务。

如何安装 lsp-architecture?

使用本页的安装命令:将 lsp-architecture 作为插件添加到 Claude Code,或将其仓库克隆到 skills 目录,然后重启 Claude 以加载该 Skill。

lsp-architecture 属于哪个分类?

lsp-architecture 属于元分类。

lsp-architecture 可以免费使用吗?

可以。lsp-architecture 已收录在 AIMCP,可免费安装。

相关推荐技能

content-collections
元

Content Collections 是一个 TypeScript 优先的构建工具,可将本地 Markdown/MDX 文件转换为类型安全的数据集合。它专为构建博客、文档站和内容密集型 Vite+React 应用而设计,提供基于 Zod 的自动模式验证。该工具涵盖从 Vite 插件配置、MDX 编译到生产环境部署的完整工作流。

查看技能
polymarket
元

这个Claude Skill为开发者提供完整的Polymarket预测市场开发支持,涵盖API调用、交易执行和市场数据分析。关键特性包括实时WebSocket数据流,可监控实时交易、订单和市场动态。开发者可用它构建预测市场应用、实施交易策略并集成实时市场预测功能。

查看技能
creating-opencode-plugins
元

该Skill帮助开发者创建OpenCode插件,用于接入命令、文件、LSP等25+种事件。它提供了插件结构、事件API规范和JavaScript/TypeScript实现模式,适合需要拦截操作、扩展功能或自定义事件处理的场景。开发者可通过它快速构建响应式模块来增强OpenCode AI助手的能力。

查看技能
sglang
元

SGLang是一个专为LLM设计的高性能推理框架,特别适用于需要结构化输出的场景。它通过RadixAttention前缀缓存技术,在处理JSON、正则表达式、工具调用等具有重复前缀的复杂工作流时,能实现极速生成。如果你正在构建智能体或多轮对话系统,并追求远超vLLM的推理性能,SGLang是理想选择。

查看技能