Agent skill

Tldr Overview

by parcadei in parcadei/Continuous-Claude-v3

Get a token-efficient overview of any project using the TLDR stack

MITAuto-check passedWriting & Content

Install Tldr Overview

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

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

GitHub CLI
$ gh skill install parcadei/Continuous-Claude-v3 tldr-overview --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-overview .claude/skills/tldr-overview && 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-overview
GitHub stars
3.9k
Used in
1 other repo
Token cost
~470 tokens
SKILL.md length
103 words
Files
1
Skills in repo
141
Repo updated
First seen
Licence
MIT

At a glance

Get a token-efficient overview of any project using the TLDR stack

  • Works in 4 steps: File Tree (Navigation Map) → Code Structure (What Exists) → Call Graph Entry Points (Architecture) → …
  • Tasks that involve Summarization
  • SKILL.md covers Trigger, Execution, Output Format and When NOT to Use, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Tldr Overview is an agent skill from parcadei/Continuous-Claude-v3. Get a token-efficient overview of any project using the TLDR stack

Its SKILL.md is about 470 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Writing & Content, covering Summarization. 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

Example prompts

  • “/tldr-overview”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. File Tree (Navigation Map)
  2. Code Structure (What Exists)
  3. Call Graph Entry Points (Architecture)
  4. Key Function Complexity (Hot Spots)

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 nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    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 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 Overview loads about 470 tokens when it runs. Until then it costs about 20 tokens; SKILL.md has 103 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~20
When it runs · the whole SKILL.md, loaded when a task matches
~470

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 passed

The automated check found no risky patterns in SKILL.md.

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). 103 words, ~470 tokens.

Download SKILL.mdSave it as .claude/skills/tldr-overview/SKILL.md (or your agent's skills folder).
name
tldr-overview
description
Get a token-efficient overview of any project using the TLDR stack

TLDR Project Overview

Get a token-efficient overview of any project using the TLDR stack.

Trigger

  • /overview or /tldr-overview
  • "give me an overview of this project"
  • "what's in this codebase"
  • Starting work on an unfamiliar project

Execution

1. File Tree (Navigation Map)
bash
tldr tree . --ext .py    # or .ts, .go, .rs
2. Code Structure (What Exists)
bash
tldr structure src/ --lang python --max 50

Returns: functions, classes, imports per file

3. Call Graph Entry Points (Architecture)
bash
tldr calls src/

Returns: cross-file relationships, main entry points

4. Key Function Complexity (Hot Spots)

For each entry point found:

bash
tldr cfg src/main.py main  # Get complexity

Output Format

## Project Overview: {project_name}

### Structure
{tree output - files and directories}

### Key Components
{structure output - functions, classes per file}

### Architecture (Call Graph)
{calls output - how components connect}

### Complexity Hot Spots
{cfg output - functions with high cyclomatic complexity}

---
Token cost: ~{N} tokens (vs ~{M} raw = {savings}% savings)

When NOT to Use

  • Already familiar with the project
  • Working on a specific file (use targeted tldr commands instead)
  • Test files (need full context)

Programmatic Usage

python
from tldr.api import get_file_tree, get_code_structure, build_project_call_graph

# 1. Tree
tree = get_file_tree("src/", extensions={".py"})

# 2. Structure
structure = get_code_structure("src/", language="python", max_results=50)

# 3. Call graph
calls = build_project_call_graph("src/", language="python")

# 4. Complexity for hot functions
for edge in calls.edges[:10]:
    cfg = get_cfg_context("src/" + edge[0], edge[1])

© 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

Just SKILL.md in .claude/skills/tldr-overview of parcadei/Continuous-Claude-v3.

Open the folder on GitHubat commit d07ff4b

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in parcadei/Continuous-Claude-v3, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Tldr Overview next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Tldr Overview compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Tldr Overview this skillparcadei/Continuous-Claude-v33.9k1 repos~470Automated safety check: PassMIT
News Aggregator Skillcclank/news-aggregator-skill1.3k—~2.1kAutomated safety check: PassNone
AI Daily Newsgeekjourneyx/ai-daily-skill235—~2.3kAutomated safety check: PassNone
Vss Search ArchiveNVIDIA-AI-Blueprints/video-search-and-summarization1.9k—~3.6kAutomated safety check: PassApache-2.0
AnalyzeriBigQiang/feedgrab614—~1kAutomated safety check: PassMIT
Reportmicrosoft/data-formulator18k—~1.5kAutomated safety check: PassMIT

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Questions about Tldr Overview

What does Tldr Overview do?

Get a token-efficient overview of any project using the TLDR stack. Tldr Overview is an agent skill from parcadei/Continuous-Claude-v3.

When should I use Tldr Overview?

Tldr Overview fits situations like: tasks that involve Summarization.

How do I install Tldr Overview in Claude Code?

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

How do I install Tldr Overview in Codex?

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

Can I use Tldr Overview 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-overview -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-overview, .gemini/skills/tldr-overview, .github/skills/tldr-overview and .opencode/skills/tldr-overview in your project.

What does Tldr Overview need to run?

SKILL.md names no scripts, command-line tools or credentials: Tldr Overview is instructions for the agent only. Our summary lists: Python 3.

Does Tldr Overview 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 Overview safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Tldr Overview use?

Tldr Overview 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 Overview use?

About 470 tokens (SKILL.md is roughly 1.9k 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 Overview?

Skills that share tags, products or a category with Tldr Overview: News Aggregator Skill (cclank/news-aggregator-skill, 1.3k stars), AI Daily News (geekjourneyx/ai-daily-skill, 235 stars), Vss Search Archive (NVIDIA-AI-Blueprints/video-search-and-summarization, 1.9k stars) and Analyzer (iBigQiang/feedgrab, 614 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tldr Overview?

parcadei (a GitHub user) maintains it in parcadei/Continuous-Claude-v3, which has 3,943 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.