tldr-router

Map code questions to the optimal tldr command by detecting intent and routing to the right analysis layer.

422 stars

Best use case

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

Map code questions to the optimal tldr command by detecting intent and routing to the right analysis layer.

Teams using tldr-router 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-router/SKILL.md --create-dirs "https://raw.githubusercontent.com/vibeeval/vibecosystem/main/skills/tldr-router/SKILL.md"

Manual Installation

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

How tldr-router Compares

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

Frequently Asked Questions

What does this skill do?

Map code questions to the optimal tldr command by detecting intent and routing to the right analysis layer.

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 Smart Router

Maps questions to the optimal tldr command. Use this to pick the right layer.

## Question → Command Mapping

### "What files/functions exist?"
```bash
tldr tree . --ext .py          # File overview
tldr structure src/ --lang python  # Function/class overview
```
**Use:** Starting exploration, orientation

### "What does X call / who calls X?"
```bash
tldr context <function> --project . --depth 2
tldr calls src/
```
**Use:** Understanding architecture, finding entry points

### "How complex is X?"
```bash
tldr cfg <file> <function>
```
**Use:** Identifying refactoring candidates, understanding difficulty

### "Where does variable Y come from?"
```bash
tldr dfg <file> <function>
```
**Use:** Debugging, understanding data flow

### "What affects line Z?"
```bash
tldr slice <file> <function> <line>
```
**Use:** Impact analysis, safe refactoring

### "Search for pattern P"
```bash
tldr search "pattern" src/
```
**Use:** Finding code, structural search

## Decision Tree

```
START
  │
  ├─► "What exists?" ──► tree / structure
  │
  ├─► "How does X connect?" ──► context / calls
  │
  ├─► "Why is X complex?" ──► cfg
  │
  ├─► "Where does Y flow?" ──► dfg
  │
  ├─► "What depends on Z?" ──► slice
  │
  └─► "Find something" ──► search
```

## Intent Detection Keywords

| Intent | Keywords | Layer |
|--------|----------|-------|
| Navigation | "what", "where", "find", "exists" | tree, structure, search |
| Architecture | "calls", "uses", "connects", "depends" | context, calls |
| Complexity | "complex", "refactor", "branches", "paths" | cfg |
| Data Flow | "variable", "value", "assigned", "comes from" | dfg |
| Impact | "affects", "changes", "slice", "dependencies" | slice/pdg |
| Debug | "bug", "error", "investigate", "broken" | cfg + dfg + context |

## Automatic Hook Integration

The `tldr-read-enforcer` and `tldr-context-inject` hooks automatically:
1. Detect intent from your messages
2. Route to appropriate layers
3. Inject context into tool calls

You don't need to manually run these commands - the hooks do it for you.

## Manual Override

If you need a specific layer the hooks didn't provide:

```bash
# Force specific analysis
tldr cfg path/to/file.py function_name
tldr dfg path/to/file.py function_name
tldr slice path/to/file.py function_name 42
```

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