tldr-deep 函数深度分析工具：五层代码结构、控制流、数据流调试

tldr-deep by parcadei/continuous-claude-v3

195 周安装量

3,600 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/parcadei/continuous-claude-v3 --skill tldr-deep

自动化代码质量调试

🇨🇳中文介绍

TLDR 深度分析

对特定函数进行完整的五层分析。用于调试或深入理解代码。

触发方式

/tldr-deep <函数名>
"详细分析函数 X"
"我需要深入理解 Y 的工作原理"
调试复杂函数

分析层级

层级	目的	命令
L1: 抽象语法树	结构分析	`tldr extract <文件>`
L2: 调用图	导航分析	`tldr context <函数> --depth 2`
L3: 控制流图	复杂度分析	`tldr cfg <文件> <函数>`

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## 深度分析: {函数名}

### L1: 结构 (抽象语法树)
文件: {文件路径}
函数签名: {签名}
文档字符串: {文档字符串}

### L2: 调用图
调用函数: {此函数调用的函数列表}
被调用: {调用此函数的函数列表}

### L3: 控制流 (控制流图)
基本块: {N}
圈复杂度: {M}
[如果 M > 10 则标记为热点]
分支结构:
  - if: 第 X 行
  - for: 第 Y 行
  - ...

### L4: 数据流 (数据流图)
定义的变量:
  - {变量1} @ 第 X 行
  - {变量2} @ 第 Y 行
使用的变量:
  - {变量1} @ 第 [A, B, C] 行
  - {变量2} @ 第 [D, E] 行

### L5: 程序切片 (影响第 {目标行} 行)
切片中的行数: {N}
关键依赖关系:
  - 第 X 行 → 第 Y 行 (数据依赖)
  - 第 A 行 → 第 B 行 (控制依赖)

---
总计: ~{令牌数} 个令牌 (相比原始文件节省 95%)

🇺🇸English

TLDR Deep Analysis

Full 5-layer analysis of a specific function. Use when debugging or deeply understanding code.

Trigger

/tldr-deep <function_name>
"analyze function X in detail"
"I need to deeply understand how Y works"
Debugging complex functions

Layers

Layer	Purpose	Command
L1: AST	Structure	`tldr extract <file>`
L2: Call Graph	Navigation	`tldr context <func> --depth 2`
L3: CFG	Complexity	`tldr cfg <file> <func>`
L4: DFG	Data flow	`tldr dfg <file> <func>`
L5: Slice	Dependencies	`tldr slice <file> <func> <line>`

Execution

Given a function name, run all layers:

# First find the file
tldr search "def <function_name>" .

# Then run each layer
tldr extract <found_file>              # L1: Full file structure
tldr context <function_name> --project . --depth 2  # L2: Call graph
tldr cfg <found_file> <function_name>  # L3: Control flow
tldr dfg <found_file> <function_name>  # L4: Data flow
tldr slice <found_file> <function_name> <target_line>  # L5: Slice

Output Format

## Deep Analysis: {function_name}

### L1: Structure (AST)
File: {file_path}
Signature: {signature}
Docstring: {docstring}

### L2: Call Graph
Calls: {list of functions this calls}
Called by: {list of functions that call this}

### L3: Control Flow (CFG)
Blocks: {N}
Cyclomatic Complexity: {M}
[Hot if M > 10]
Branches:
  - if: line X
  - for: line Y
  - ...

### L4: Data Flow (DFG)
Variables defined:
  - {var1} @ line X
  - {var2} @ line Y
Variables used:
  - {var1} @ lines [A, B, C]
  - {var2} @ lines [D, E]

### L5: Program Slice (affecting line {target})
Lines in slice: {N}
Key dependencies:
  - line X → line Y (data)
  - line A → line B (control)

---
Total: ~{tokens} tokens (95% savings vs raw file)

When to Use

Debugging - Need to understand all paths through a function
Refactoring - Need to know what depends on what
Code review - Analyzing complex functions
Performance - Finding hot spots (high cyclomatic complexity)

Programmatic API

from tldr.api import (
    extract_file,
    get_relevant_context,
    get_cfg_context,
    get_dfg_context,
    get_slice
)

# All layers for one function
file_info = extract_file("src/processor.py")
context = get_relevant_context("src/", "process_data", depth=2)
cfg = get_cfg_context("src/processor.py", "process_data")
dfg = get_dfg_context("src/processor.py", "process_data")
slice_lines = get_slice("src/processor.py", "process_data", target_line=42)

Weekly Installs

195

Repository

parcadei/contin…laude-v3

GitHub Stars

3.6K

First Seen

Jan 24, 2026

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