tldr-code
Token-efficient code analysis via 5-layer stack (AST, Call Graph, CFG, DFG, PDG). 95% token savings.
Install
mkdir -p .claude/skills/tldr-code && curl -L -o skill.zip "https://mcp.directory/api/skills/download/4576" && unzip -o skill.zip -d .claude/skills/tldr-code && rm skill.zipInstalls to .claude/skills/tldr-code
About this skill
TLDR-Code: Complete Reference
Token-efficient code analysis. 95% savings vs raw file reads.
Quick Reference
| Task | Command |
|---|---|
| File tree | tldr tree src/ |
| Code structure | tldr structure . --lang python |
| Search code | tldr search "pattern" . |
| Call graph | tldr calls src/ |
| Who calls X? | tldr impact func_name . |
| Control flow | tldr cfg file.py func |
| Data flow | tldr dfg file.py func |
| Program slice | tldr slice file.py func 42 |
| Dead code | tldr dead src/ |
| Architecture | tldr arch src/ |
| Imports | tldr imports file.py |
| Who imports X? | tldr importers module_name . |
| Affected tests | tldr change-impact --git |
| Type check | tldr diagnostics file.py |
| Semantic search | tldr semantic search "auth flow" |
The 5-Layer Stack
Layer 1: AST ~500 tokens Function signatures, imports
Layer 2: Call Graph +440 tokens What calls what (cross-file)
Layer 3: CFG +110 tokens Complexity, branches, loops
Layer 4: DFG +130 tokens Variable definitions/uses
Layer 5: PDG +150 tokens Dependencies, slicing
───────────────────────────────────────────────────────────────
Total: ~1,200 tokens vs 23,000 raw = 95% savings
CLI Commands
Navigation
# File tree
tldr tree [path]
tldr tree src/ --ext .py .ts # Filter extensions
tldr tree . --show-hidden # Include hidden files
# Code structure (codemaps)
tldr structure [path] --lang python
tldr structure src/ --max 100 # Max files to analyze
Search
# Text search
tldr search <pattern> [path]
tldr search "def process" src/
tldr search "class.*Error" . --ext .py
tldr search "TODO" . -C 3 # 3 lines context
tldr search "func" . --max 50 # Limit results
# Semantic search (natural language)
tldr semantic search "authentication flow"
tldr semantic search "error handling" --k 10
tldr semantic search "database queries" --expand # Include call graph
File Analysis
# Full file info
tldr extract <file>
tldr extract src/api.py
tldr extract src/api.py --class UserService # Filter to class
tldr extract src/api.py --function process # Filter to function
tldr extract src/api.py --method UserService.get # Filter to method
# Relevant context (follows call graph)
tldr context <entry> --project <path>
tldr context main --project src/ --depth 3
tldr context UserService.create --project . --lang typescript
Flow Analysis
# Control flow graph (complexity)
tldr cfg <file> <function>
tldr cfg src/processor.py process_data
# Returns: cyclomatic complexity, blocks, branches, loops
# Data flow graph (variable tracking)
tldr dfg <file> <function>
tldr dfg src/processor.py process_data
# Returns: where variables are defined, read, modified
# Program slice (what affects line X)
tldr slice <file> <function> <line>
tldr slice src/processor.py process_data 42
tldr slice src/processor.py process_data 42 --direction forward
tldr slice src/processor.py process_data 42 --var result
Codebase Analysis
# Build cross-file call graph
tldr calls [path]
tldr calls src/ --lang python
# Reverse call graph (who calls this function?)
tldr impact <func> [path]
tldr impact process_data src/ --depth 5
tldr impact authenticate . --file auth # Filter by file
# Find dead/unreachable code
tldr dead [path]
tldr dead src/ --entry main cli test_ # Specify entry points
tldr dead . --lang typescript
# Detect architectural layers
tldr arch [path]
tldr arch src/ --lang python
# Returns: entry layer, middle layer, leaf layer, circular deps
Import Analysis
# Parse imports from file
tldr imports <file>
tldr imports src/api.py
tldr imports src/api.ts --lang typescript
# Reverse import lookup (who imports this module?)
tldr importers <module> [path]
tldr importers datetime src/
tldr importers UserService . --lang typescript
Quality & Testing
# Type check + lint
tldr diagnostics <file|path>
tldr diagnostics src/api.py
tldr diagnostics . --project # Whole project
tldr diagnostics src/ --no-lint # Type check only
tldr diagnostics src/ --format text # Human-readable
# Find affected tests
tldr change-impact [files...]
tldr change-impact # Auto-detect (session/git)
tldr change-impact src/api.py # Explicit files
tldr change-impact --session # Session-modified files
tldr change-impact --git # Git diff files
tldr change-impact --git --git-base main # Diff against branch
tldr change-impact --run # Actually run affected tests
Caching
# Pre-build call graph cache
tldr warm <path>
tldr warm src/ --lang python
tldr warm . --background # Build in background
# Build semantic index (one-time)
tldr semantic index [path]
tldr semantic index . --lang python
tldr semantic index . --model all-MiniLM-L6-v2 # Smaller model (80MB)
Daemon (Faster Queries)
The daemon holds indexes in memory for instant repeated queries.
Daemon Commands
# Start daemon (backgrounds automatically)
tldr daemon start
tldr daemon start --project /path/to/project
# Check status
tldr daemon status
# Stop daemon
tldr daemon stop
# Send raw command
tldr daemon query ping
tldr daemon query status
# Notify file change (for hooks)
tldr daemon notify <file>
tldr daemon notify src/api.py
Daemon Features
| Feature | Description |
|---|---|
| Auto-shutdown | 30 minutes idle |
| Query caching | SalsaDB memoization |
| Content hashing | Skip unchanged files |
| Dirty tracking | Incremental re-indexing |
| Cross-platform | Unix sockets / Windows TCP |
Daemon Socket Protocol
Send JSON to socket, receive JSON response:
// Request
{"cmd": "search", "pattern": "process", "max_results": 10}
// Response
{"status": "ok", "results": [...]}
All 22 daemon commands:
ping, status, shutdown, search, extract, impact, dead, arch,
cfg, dfg, slice, calls, warm, semantic, tree, structure,
context, imports, importers, notify, diagnostics, change_impact
Semantic Search (P6)
Natural language code search using embeddings.
Setup
# Build index (downloads model on first run)
tldr semantic index .
# Default model: bge-large-en-v1.5 (1.3GB, best quality)
# Smaller model: all-MiniLM-L6-v2 (80MB, faster)
tldr semantic index . --model all-MiniLM-L6-v2
Search
tldr semantic search "authentication flow"
tldr semantic search "error handling patterns" --k 10
tldr semantic search "database connection" --expand # Follow call graph
Configuration
In .claude/settings.json:
{
"semantic_search": {
"enabled": true,
"auto_reindex_threshold": 20,
"model": "bge-large-en-v1.5"
}
}
Languages Supported
| Language | AST | Call Graph | CFG | DFG | PDG |
|---|---|---|---|---|---|
| Python | Yes | Yes | Yes | Yes | Yes |
| TypeScript | Yes | Yes | Yes | Yes | Yes |
| JavaScript | Yes | Yes | Yes | Yes | Yes |
| Go | Yes | Yes | Yes | Yes | Yes |
| Rust | Yes | Yes | Yes | Yes | Yes |
| Java | Yes | Yes | - | - | - |
| C/C++ | Yes | Yes | - | - | - |
| Ruby | Yes | - | - | - | - |
| PHP | Yes | - | - | - | - |
| Kotlin | Yes | - | - | - | - |
| Swift | Yes | - | - | - | - |
| C# | Yes | - | - | - | - |
| Scala | Yes | - | - | - | - |
| Lua | Yes | - | - | - | - |
| Elixir | Yes | - | - | - | - |
Ignore Patterns
TLDR respects .tldrignore (gitignore syntax):
# .tldrignore
.venv/
__pycache__/
node_modules/
*.min.js
dist/
First run creates .tldrignore with sensible defaults.
Use --no-ignore to bypass.
When to Use TLDR vs Other Tools
| Task | Use TLDR | Use Grep |
|---|---|---|
| Find function definition | tldr extract file --function X | - |
| Search code patterns | tldr search "pattern" | - |
| String literal search | - | grep "literal" |
| Config values | - | grep "KEY=" |
| Cross-file calls | tldr calls | - |
| Reverse deps | tldr impact func | - |
| Complexity analysis | tldr cfg file func | - |
| Variable tracking | tldr dfg file func | - |
| Natural language query | tldr semantic search | - |
Python API
from tldr.api import (
# L1: AST
extract_file, extract_functions, get_imports,
# L2: Call Graph
build_project_call_graph, get_intra_file_calls,
# L3: CFG
get_cfg_context,
# L4: DFG
get_dfg_context,
# L5: PDG
get_slice, get_pdg_context,
# Unified
get_relevant_context,
# Analysis
analyze_dead_code, analyze_architecture, analyze_impact,
)
# Example: Get context for LLM
ctx = get_relevant_context("src/", "main", depth=2, language="python")
print(ctx.to_llm_string())
Bug Fixing Workflow (Navigation + Read)
Key insight: TLDR navigates, then you read. Don't try to fix bugs from summaries alone.
The Pattern
# 1. NAVIGATE: Find which files matter
tldr imports file.py # What does buggy file depend on?
tldr impact func_name . # Who calls the buggy function?
tldr calls . # Cross-file edges (follow 2-hop for models)
# 2. READ: Get actual code for critical files (2-4 files, not all 50)
# Use Read tool or tldr search -C for code with context
tldr search "def buggy_func" . -C 20
Why This Works
For cross-file bugs (e.g., wrong field name, type mismatch), you need to see:
- The file with the bug (handler accessing
task.user_id) - The file with the contract (model defining
owner_id)
TLDR finds which files matter. Then you read them.
Getting More Context
If TLDR output isn't enough:
tldr search "pattern" . -C 20- Get actual code with 20 lines contexttldr imports file.py- See what a file depends on- Read the file directly if you ne
Content truncated.
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