Tldr Deep

parcadei/continuous-claude-v3/.claude/skills/tldr-deep

作者 parcadeid07ff4b06b62无许可证3.9K 个星标收录于 2026年10月8日更新于 2026年10月8日仓库8个月前更新

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

仅含说明Software Development

AI 生成的概览

使用 tldr 命令行工具对单个函数进行五层静态分析，辅助调试与代码理解。

功能: 该技能引导智能体对单个函数执行五层分析：AST 结构、调用图、控制流图、数据流图和程序切片。它给出每层对应的 tldr 命令，并规定结构化输出格式，涵盖函数签名、调用与被调用关系、圈复杂度、变量定义与使用以及切片依赖。文档还介绍了以编程方式运行这些层的 Python API。
适用场景: 适用于调试复杂函数、重构时了解依赖关系、审查复杂函数，以及通过圈复杂度定位性能热点。
运行要求: 需要安装并可用 tldr 命令行工具及其 Python API（tldr.api）；该技能本身不附带脚本，仅提供说明。

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:

bash

# First find the filetldr search "def <function_name>" .
# Then run each layertldr extract <found_file>              # L1: Full file structuretldr context <function_name> --project . --depth 2  # L2: Call graphtldr cfg <found_file> <function_name>  # L3: Control flowtldr dfg <found_file> <function_name>  # L4: Data flowtldr slice <found_file> <function_name> <target_line>  # L5: Slice

# First find the filetldr search "def <function_name>" .
# Then run each layertldr extract <found_file>              # L1: Full file structuretldr context <function_name> --project . --depth 2  # L2: Call graphtldr cfg <found_file> <function_name>  # L3: Control flowtldr dfg <found_file> <function_name>  # L4: Data flowtldr 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 GraphCalls: {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 YVariables 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)

## Deep Analysis: {function_name}
### L1: Structure (AST)File: {file_path}Signature: {signature}Docstring: {docstring}
### L2: Call GraphCalls: {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 YVariables 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

python

from tldr.api import (    extract_file,    get_relevant_context,    get_cfg_context,    get_dfg_context,    get_slice)
# All layers for one functionfile_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)

from tldr.api import (    extract_file,    get_relevant_context,    get_cfg_context,    get_dfg_context,    get_slice)
# All layers for one functionfile_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)

来源与署名

来源：parcadei/continuous-claude-v3位于.claude/skills/tldr-deep提交d07ff4b

许可证：无许可证

内容归原作者所有。SourceWeft 从公开仓库中收录这些内容。

举报或申请下架

Tldr Deep

TLDR Deep Analysis

Trigger

Layers

Execution

Output Format

When to Use

Programmatic API

来源与署名

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