tldr-overview

Get a token-efficient overview of any project using file tree, code structure, and call graph analysis.

422 stars

Best use case

tldr-overview is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Get a token-efficient overview of any project using file tree, code structure, and call graph analysis.

Teams using tldr-overview 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

$curl -o ~/.claude/skills/tldr-overview/SKILL.md --create-dirs "https://raw.githubusercontent.com/vibeeval/vibecosystem/main/skills/tldr-overview/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/tldr-overview/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How tldr-overview Compares

Feature / Agenttldr-overviewStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Get a token-efficient overview of any project using file tree, code structure, and call graph analysis.

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 Project Overview

Get a token-efficient overview of any project using the TLDR stack.

## Trigger
- `/overview` or `/tldr-overview`
- "give me an overview of this project"
- "what's in this codebase"
- Starting work on an unfamiliar project

## Execution

### 1. File Tree (Navigation Map)
```bash
tldr tree . --ext .py    # or .ts, .go, .rs
```

### 2. Code Structure (What Exists)
```bash
tldr structure src/ --lang python --max 50
```
Returns: functions, classes, imports per file

### 3. Call Graph Entry Points (Architecture)
```bash
tldr calls src/
```
Returns: cross-file relationships, main entry points

### 4. Key Function Complexity (Hot Spots)
For each entry point found:
```bash
tldr cfg src/main.py main  # Get complexity
```

## Output Format

```
## Project Overview: {project_name}

### Structure
{tree output - files and directories}

### Key Components
{structure output - functions, classes per file}

### Architecture (Call Graph)
{calls output - how components connect}

### Complexity Hot Spots
{cfg output - functions with high cyclomatic complexity}

---
Token cost: ~{N} tokens (vs ~{M} raw = {savings}% savings)
```

## When NOT to Use
- Already familiar with the project
- Working on a specific file (use targeted tldr commands instead)
- Test files (need full context)

## Programmatic Usage

```python
from tldr.api import get_file_tree, get_code_structure, build_project_call_graph

# 1. Tree
tree = get_file_tree("src/", extensions={".py"})

# 2. Structure
structure = get_code_structure("src/", language="python", max_results=50)

# 3. Call graph
calls = build_project_call_graph("src/", language="python")

# 4. Complexity for hot functions
for edge in calls.edges[:10]:
    cfg = get_cfg_context("src/" + edge[0], edge[1])
```

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