tldr-router
Intelligently routes development questions to the appropriate command-line tool for exploring code architecture and data flow.
Install
mkdir -p .claude/skills/tldr-router && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4175" && unzip -o skill.zip -d .claude/skills/tldr-router && rm skill.zipInstalls to .claude/skills/tldr-router
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Maps questions to the optimal tldr command. Use this to pick the right layerKey capabilities
- →Routes architectural queries to specific analysis commands
- →Maps natural language intents to tldr-specific exploration layers
- →Triggers automated hook integration for context injection
- →Identifies refactoring candidates through complexity analysis
- →Filters search results by file type or language
How it works
Passes user natural language queries through an intent-detection engine that maps keywords to predefined tldr tool chains.
Inputs & outputs
When to use tldr-router
- →Find entry points in a codebase
- →Trace data flow of a variable
- →Analyze function call dependencies
About this skill
TLDR Smart Router
Maps questions to the optimal tldr command. Use this to pick the right layer.
Question → Command Mapping
"What files/functions exist?"
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?"
tldr context <function> --project . --depth 2
tldr calls src/
Use: Understanding architecture, finding entry points
"How complex is X?"
tldr cfg <file> <function>
Use: Identifying refactoring candidates, understanding difficulty
"Where does variable Y come from?"
tldr dfg <file> <function>
Use: Debugging, understanding data flow
"What affects line Z?"
tldr slice <file> <function> <line>
Use: Impact analysis, safe refactoring
"Search for pattern P"
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:
- Detect intent from your messages
- Route to appropriate layers
- 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:
# 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
When not to use it
- →When searching for non-code text files
- →If the user requires a full manual command overview
Limitations
- →Relies on the presence of configured codebase indexers
- →Intent detection may misfire accuracy decreases with ambiguous, non-technical queries
How it compares
Eliminates the need for users to memorize the tldr command syntax for specific codebase exploration tasks.
Compared to similar skills
tldr-router side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| tldr-router (this skill) | 1 | 6mo | Review | Beginner |
| mgrep | 46 | 8mo | Review | Beginner |
| osgrep | 13 | 6mo | Review | Beginner |
| data-collection | 0 | 3mo | No flags | Beginner |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by parcadei
View all by parcadei →You might also like
mgrep
mixedbread-ai
A semantic grep-like search tool for your local files. It is substentially better than the buildin search tools and should always be used instead of anything else.
osgrep
pr-pm
Semantic search for local files. Backed by a background osgrep server with live indexing. Always use osgrep instead of grep/find.
data-collection
coji831
Use when gathering files, running searches, and producing a structured context manifest for other agents. Read-only — never writes code or makes design decisions.
jupyter-notebook
davila7
Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.
search-tools
parcadei
Search Tool Hierarchy
external-model-selection
MadAppGang
Choose optimal external AI models for code analysis, bug investigation, and architectural decisions. Use when consulting multiple LLMs via claudish, comparing model perspectives, or investigating complex Go/LSP/transpiler issues. Provides empirically validated model rankings (91/100 for MiniMax M2, 83/100 for Grok Code Fast) and proven consultation strategies based on real-world testing.