Agent skill

Tldr Code

by parcadei in parcadei/Continuous-Claude-v3

Token-efficient code analysis via 5-layer stack (AST, Call Graph, CFG, DFG, PDG).

MITAuto-check: notesWriting & Content

Install Tldr Code

skills CLI
$ npx skills add parcadei/Continuous-Claude-v3 --skill tldr-code -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install parcadei/Continuous-Claude-v3 tldr-code --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/tldr-code .claude/skills/tldr-code && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
tldr-code
GitHub stars
3.9k
Used in
2 other repos
Token cost
~2.7k tokens
SKILL.md length
451 words
Files
2
Skills in repo
141
Repo updated
First seen
Licence
MIT

At a glance

Token-efficient code analysis via 5-layer stack (AST, Call Graph, CFG, DFG, PDG).

  • Tasks that involve Summarization
  • SKILL.md covers Quick Reference, The 5-Layer Stack, CLI Commands and Daemon (Faster Queries), plus 7 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve Codebase onboarding

What it does

Tldr Code is an agent skill from parcadei/Continuous-Claude-v3. Token-efficient code analysis via 5-layer stack (AST, Call Graph, CFG, DFG, PDG). 95% token savings.

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `rules.md`).

It sits in Writing & Content, covering Summarization and Codebase onboarding. The repository describes itself as: Context management for Claude Code. Hooks maintain state via ledgers and handoffs. MCP execution without context pollution. Agent orchestration with isolated context windows. The licence is MIT.

When your agent uses it

  • Tasks that involve Summarization
  • Tasks that involve Codebase onboarding

Example prompts

  • “/tldr-code”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Bash

What it can do on your machine

Read from SKILL.md and the folder at commit d07ff4b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash, json, gitignore and python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Tldr Code loads about 2.7k tokens when it runs. Until then it costs about 28 tokens; SKILL.md has 451 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~28
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from parcadei/Continuous-Claude-v3 at commit d07ff4b, republished under its MIT licence (© parcadei). 451 words, ~2,662 tokens.

Download SKILL.mdSave it as .claude/skills/tldr-code/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
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

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
bash
# 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
bash
# 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
bash
# 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
bash
# 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
bash
# 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
bash
# 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
bash
# 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
bash
# 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
bash
# 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:

json
// 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
bash
# 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
bash
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:

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):

gitignore
# .tldrignore
.venv/
__pycache__/
node_modules/
*.min.js
dist/

First run creates .tldrignore with sensible defaults. Use --no-ignore to bypass.


Show full SKILL.md (187 more words)Show less

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

python
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
bash
# 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.

© parcadei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in .claude/skills/tldr-code of parcadei/Continuous-Claude-v3.

  • SKILL.md
  • rules.md

Open the folder on GitHubat commit d07ff4b

Used in 2 other repositories

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in parcadei/Continuous-Claude-v3, which our catalogue first saw on October 7, 2026.

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Vss Search ArchiveNVIDIA-AI-Blueprints/video-search-and-summarization1.9k—~3.6kAutomated safety check: PassApache-2.0

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Questions about Tldr Code

What does Tldr Code do?

Token-efficient code analysis via 5-layer stack (AST, Call Graph, CFG, DFG, PDG). Tldr Code is an agent skill from parcadei/Continuous-Claude-v3. Token-efficient code analysis via 5-layer stack (AST, Call Graph, CFG, DFG, PDG).

When should I use Tldr Code?

Tldr Code fits situations like: tasks that involve Summarization; tasks that involve Codebase onboarding.

How do I install Tldr Code in Claude Code?

Run `npx skills add parcadei/Continuous-Claude-v3 --skill tldr-code -a claude-code`. Or copy the skill folder (.claude/skills/tldr-code in parcadei/Continuous-Claude-v3) into .claude/skills/tldr-code in your project. Claude Code loads it when a task matches its description.

How do I install Tldr Code in Codex?

Run `npx skills add parcadei/Continuous-Claude-v3 --skill tldr-code -a codex`. Or copy the skill folder (.claude/skills/tldr-code in parcadei/Continuous-Claude-v3) into .agents/skills/tldr-code in your project. Codex loads it when a task matches its description.

Can I use Tldr Code in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add parcadei/Continuous-Claude-v3 --skill tldr-code -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tldr-code, .gemini/skills/tldr-code, .github/skills/tldr-code and .opencode/skills/tldr-code in your project.

What does Tldr Code need to run?

SKILL.md names no scripts, command-line tools or credentials: Tldr Code is instructions for the agent only. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash.

Does Tldr Code access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Tldr Code safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Tldr Code use?

Tldr Code is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Tldr Code use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Tldr Code?

Skills that share tags, products or a category with Tldr Code: Omh Content Operator (rlaope/oh-my-hermes, 3.2k stars), Cy Workflow Memory (compozy/compozy, 2.8k stars), Tldr CLI (majiayu000/claude-skill-registry, 666 stars) and News Aggregator Skill (cclank/news-aggregator-skill, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tldr Code?

parcadei (a GitHub user) maintains it in parcadei/Continuous-Claude-v3, which has 3,940 GitHub stars. The repository holds 141 skills in this directory. The repository was last updated on January 26, 2026.

Source: parcadei/Continuous-Claude-v3 on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.