tldr-code

name	tldr-code
description	Token-efficient code analysis via 5-layer stack (AST, Call Graph, CFG, DFG, PDG). 95% token savings.
allowed-tools	["Bash"]
keywords	["debug","refactor","understand","complexity","call graph","data flow","what calls","how complex","search","explore","analyze","dead code","architecture","imports"]

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 context
• tldr imports file.py - See what a file depends on
• Read the file directly if you need the full implementation

---

Token Savings Evidence

Raw file read:    23,314 tokens
TLDR all layers:   1,189 tokens
─────────────────────────────────
Savings:              95%

The insight: Call graph navigates to relevant code, then layers give structured summaries. You don't read irrelevant code.