tldr-deep — Claude Code skill: what it does, how to install
Runs a 5-layer code analysis on a specific function using the tldr CLI. Helpful when debugging or refactoring complex logic.
npx skills add parcadei/Continuous-Claude-v3 --skill tldr-deep -a claude-codeWhat tldr-deep does
The skill locates the target function with tldr search and executes five structural checks. First, it extracts the file AST (tldr extract), followed by a two-level call graph (tldr context). Next, it builds control flow (CFG) to measure cyclomatic complexity and data flow (DFG) to trace variable definitions. A final program slice (tldr slice) isolates the lines affecting a specific statement.
Your system needs the tldr command-line utility or the tldr.api Python module installed beforehand. Without that CLI tool, the subcommands will fail.
What it helps with
- calculate cyclomatic complexity for a complex function
- map incoming and outgoing function call graphs
- trace variable definitions and usage across data flow
- extract a program slice for a specific line of code
Example prompts for the agent
- /tldr-deep process_data
- analyze function handle_request in detail
- deeply inspect how calculate_tax works
How to install tldr-deep
1Skills CLI (Vercel)
npx skills add parcadei/Continuous-Claude-v3 --skill tldr-deep -a claude-code2Manually: put the files in place
~/.claude/skills/tldr-deep/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/tldr-deep/
1Skills CLI (Vercel)
npx skills add parcadei/Continuous-Claude-v3 --skill tldr-deep -a codex2Manually: put the files in place
.agents/skills/tldr-deep/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add parcadei/Continuous-Claude-v3 --skill tldr-deep -a cursor2Manually: put the files in place
.cursor/skills/tldr-deep/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install parcadei/Continuous-Claude-v3 tldr-deep2Manually: put the files in place
.github/skills/tldr-deep/SKILL.md1Skills CLI (Vercel)
npx skills add parcadei/Continuous-Claude-v3 --skill tldr-deep -a gemini-cli2Platform CLI
gemini skills install https://github.com/parcadei/Continuous-Claude-v3 --path .claude/skills/tldr-deepManually: put the files in place
.devin/skills/tldr-deep/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add parcadei/Continuous-Claude-v3 --skill tldr-deep -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about tldr-deep
How to install tldr-deep in Claude Code?
Run npx skills add parcadei/Continuous-Claude-v3 --skill tldr-deep -a claude-code. The skill file will be placed in ~/.claude/skills/tldr-deep/SKILL.md.
What does tldr-deep do?
It inspects a single function across five distinct layers: AST file structure, call graph navigation, CFG complexity, DFG variable flows, and dependency slicing.
What dependencies does tldr-deep require?
It relies on the tldr CLI or Python tldr.api package to run commands like tldr extract, tldr cfg, and tldr slice.
Source text
SKILL.md · MIT
---
name: tldr-deep
description: Full 5-layer analysis of a specific function. Use when debugging or deeply understanding code.
---
# 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)
```
© parcadei · MIT · parcadei/Continuous-Claude-v3