Uses a 5-layer stack (AST, CFG, DFG, etc.) to analyze code with 95% token savings compared to raw file reading.

Install

mkdir -p .claude/skills/tldr-code && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4576" && unzip -o skill.zip -d .claude/skills/tldr-code && rm skill.zip

Installs to .claude/skills/tldr-code

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.

Token-efficient code analysis via 5-layer stack (AST, Call Graph, CFG, DFG, PDG). 95% token savings.
100 charsno explicit “when” trigger
Advanced

Key capabilities

  • Construct layered code representations (AST, CFG, DFG, PDG)
  • Perform call graph and impact analysis
  • Identify dead code branches
  • Execute semantic code searching
  • Generate file architecture maps

How it works

It performs static analysis to transform source code into a 5-layer stack of dependency representations, consuming 95% fewer tokens.

Inputs & outputs

You give it
Codebase path and analysis command
You get back
Condensed analysis report

When to use tldr-code

  • Analyzing complex code dependencies
  • Finding dead code in a project
  • Generating call graphs for refactoring
  • Performing semantic search across the codebase

About this skill

TLDR-Code: Complete Reference

Token-efficient code analysis. 95% savings vs raw file reads.

Quick Reference

TaskCommand
File treetldr tree src/
Code structuretldr structure . --lang python
Search codetldr search "pattern" .
Call graphtldr calls src/
Who calls X?tldr impact func_name .
Control flowtldr cfg file.py func
Data flowtldr dfg file.py func
Program slicetldr slice file.py func 42
Dead codetldr dead src/
Architecturetldr arch src/
Importstldr imports file.py
Who imports X?tldr importers module_name .
Affected teststldr change-impact --git
Type checktldr diagnostics file.py
Semantic searchtldr 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

FeatureDescription
Auto-shutdown30 minutes idle
Query cachingSalsaDB memoization
Content hashingSkip unchanged files
Dirty trackingIncremental re-indexing
Cross-platformUnix 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

LanguageASTCall GraphCFGDFGPDG
PythonYesYesYesYesYes
TypeScriptYesYesYesYesYes
JavaScriptYesYesYesYesYes
GoYesYesYesYesYes
RustYesYesYesYesYes
JavaYesYes---
C/C++YesYes---
RubyYes----
PHPYes----
KotlinYes----
SwiftYes----
C#Yes----
ScalaYes----
LuaYes----
ElixirYes----

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

TaskUse TLDRUse Grep
Find function definitiontldr extract file --function X-
Search code patternstldr search "pattern"-
String literal search-grep "literal"
Config values-grep "KEY="
Cross-file callstldr calls-
Reverse depstldr impact func-
Complexity analysistldr cfg file func-
Variable trackingtldr dfg file func-
Natural language querytldr 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 ne

Content truncated.

When not to use it

  • Modifying production code directly
  • Testing dynamic runtime outcomes

Prerequisites

Bash environment

Limitations

  • Analysis is limited by the current static state
  • Requires terminal access for command execution

How it compares

It abstracts the codebase into structured graph layers rather than sending raw file text to the LLM.

Compared to similar skills

tldr-code side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
tldr-code (this skill)17moReviewAdvanced
deepwiki-rs259moReviewIntermediate
ast-grep-find37moReviewIntermediate
naming-analyzer26moReviewBeginner

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