tldr-router
Map code questions to the optimal tldr command by detecting intent and routing to the right analysis layer.
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/tldr-router/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How tldr-router Compares
| Feature / Agent | tldr-router | 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?
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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