| name | description |
|---|---|
orchestrating-swarms |
Master multi-agent orchestration using Claude Code's TeammateTool and Task system. Use when coordinating multiple agents, running parallel code reviews, creating pipeline workflows with dependencies, building self-organizing task queues, or any task benefiting from divide-and-conquer patterns. |
Master multi-agent orchestration using Claude Code's TeammateTool and Task system.
This is not a proposal. This documents existing but hidden functionality found in Claude Code v2.1.19 binary, plus speculation on how it could be used.
TeammateTool already exists in Claude Code. We extracted this from the compiled binary at ~/.local/share/claude/versions/2.1.19 using strings analysis. The feature is fully implemented but gated behind feature flags (I9() && qFB()).
Create a production-ready, visually stunning website with a futuristic luxury travel theme.
GOAL Build a single-page (plus optional “Destination” detail route) website for a fictional brand: “AURORA LUXE TRAVEL” — ultra-premium, concierge-level trips.
TECH STACK (use exactly this unless something breaks)
| description | argument-hint | model | |
|---|---|---|---|
Interview me about the plan |
|
opus |
Read this plan file $1 and interview me in detail using the AskUserQuestionTool about literally anything: technical implementation, UI & UX, concerns, tradeoffs, etc. but make sure the questions are not obvious.
| # /// script | |
| # requires-python = ">=3.12" | |
| # dependencies = [ | |
| # "parakeet-mlx", | |
| # "sounddevice", | |
| # "rich", | |
| # ] | |
| # /// | |
| import queue |
| """ | |
| a simple script that reads tweets inside a json file, uses openai to compute embeddings and creates two files, metadata.tsv and output.tsv, which cam be used to visualise the tweets and their embeddings in TensorFlow Projector (https://projector.tensorflow.org/) | |
| """ | |
| # obtain tweets.json from https://gist.github.com/gd3kr/948296cf675469f5028911f8eb276dbc | |
| import pandas as pd | |
| import json | |
| from openai import OpenAI |
| /* Enhancements to the Twitter Scraping Script: | |
| This update to the script introduces a more robust mechanism for extracting detailed interaction data from tweets as they are scraped from Twitter. Previously, the script focused on collecting basic content such as the tweet's text. Now, it has been augmented to include a comprehensive extraction of interaction metrics, including replies, reposts, likes, bookmarks, and views, for each tweet. | |
| Key Changes: | |
| 1. Improved Data Extraction: | |
| - The script now searches through all elements within a tweet that have an `aria-label` attribute, filtering for labels that contain key interaction terms (replies, reposts, likes, bookmarks, views). This ensures that only relevant `aria-labels` are considered for data extraction. | |
| 2. Flexible Interaction Data Parsing: |
| //there are issues with how we manage to skip pixels. | |
| //accounts are getting skipped. | |
| let following = []; // Initialize an empty array to hold all elements of people you follow | |
| let formattedFollowers = [] | |
| const scrollInterval = 2000; | |
| const scrollStep = 2000; // Pixels to scroll on each step | |
| let previousFollowingCount = 0; |
| /* | |
| the twitter api is stupid. it is stupid and bad and expensive. hence, this. | |
| Literally just paste this in the JS console on the bookmarks tab and the script will automatically scroll to the bottom of your bookmarks and keep a track of them as it goes. | |
| When finished, it downloads a JSON file containing the raw text content of every bookmark. | |
| for now it stores just the text inside the tweet itself, but if you're reading this why don't you go ahead and try to also store other information (author, tweetLink, pictures, everything). come on. do it. please? | |
| */ |
