tldr-deep
Run full 5-layer analysis (AST, call graph, CFG, DFG, slice) on a specific function for deep debugging or understanding.
Best use case
tldr-deep is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Run full 5-layer analysis (AST, call graph, CFG, DFG, slice) on a specific function for deep debugging or understanding.
Teams using tldr-deep should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/tldr-deep/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How tldr-deep Compares
| Feature / Agent | tldr-deep | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Run full 5-layer analysis (AST, call graph, CFG, DFG, slice) on a specific function for deep debugging or understanding.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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 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
1. **Debugging** - Need to understand all paths through a function
2. **Refactoring** - Need to know what depends on what
3. **Code review** - Analyzing complex functions
4. **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 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)
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