Faolain Faolain

DuckDB SQL Console

Your are an LLM Assistant for the Hugging Face DuckDB SQL Console. The user will ask you questions about the data in the DuckDB database and you will answer them using SQL. Use the context provided to answer the user's questions and decide which tables to query. Only respond with SQL and comments if needed.

DuckDB Tips

DuckDB is largely compatible with Postgres SQL. Stick to Postgres / DuckDB SQL syntax.
DuckDB uses double quotes (") for identifiers that contain spaces or special characters, or to force case-sensitivity and single quotes (') to define string literals
DuckDB can extract parts of strings and lists using [start:end] or [start:end:step] syntax. Indexes start at 1. String slicing: `SELECT 'DuckDB'[1:4];` Array/List slicing: `SELECT [1, 2, 3, 4][1:3];`
DuckDB has a powerful way to select or transform multiple columns using patterns or functions. You can select columns matching a pattern: `SELECT COLUMNS('sales_.*') FROM sales_data;` or transform multiple colum

Claude Task Manager

A specialized tool to manage context isolation and focused task execution with Claude Code, solving the critical challenge of context length limitations and task focus when working with Claude on complex, multi-step projects.

What is Claude Task Manager?

Claude Task Manager solves a fundamental challenge when working with Large Language Models like Claude on complex projects: context length limitations and maintaining focus on the current task.

The Problem

There are a few ways to run an IPFS node in order to query the IPFS Network for the files you are looking for.

Local Native Installation

Install IPFS which requires installing Go if not already installed. Instructions are simple don't worry :) (Note: carefully read the instructions, choose arm for M1 Silicon Macbooks for example)
Initialize IPFS
In a separate terminal tab run ipfs daemon
Swarm Connect to dClimate IPFS node by runningipfs swarm connect /ip4/45.55.32.80/tcp/4001/ipfs/12D3KooWG7itEPAHut3xsVo7CwyD8sKeQXKgQizotNhPsToCssXQ in a different tab from where you ran ipfs daemon
Test IPNS name resolution of hashes living on the dClimate Node by runningipfs name resolve k2k4r8nyvzboy94mj0eebx9e4ux8qbwoej4q7d6wz1jwagx4rjk76kmw, you should get /ipfs/bafyreie7n7z4xd6go645fbrmyzrdrnaypx6ky4r4iekieyjzlmvo2aqgzm back
Within your

Boilerplate Proposal README file

Changes

-- Added SECURITY section

-- Added 'Libraries' to ## Terminology

-- Added explicit ## LICENSE section

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j

	import instructor
	import openai
	from pydantic import Field
	from typing import List, Optional
	import sqlite3

	from atomic_agents.agents.base_agent import BaseAgent, BaseAgentConfig, BaseIOSchema
	from atomic_agents.lib.components.system_prompt_generator import SystemPromptContextProviderBase, SystemPromptGenerator
	from atomic_agents.lib.base.base_tool import BaseTool, BaseToolConfig

	name: Deploy Pull Request to Ephemeral Environment
	on:
	pull_request:
	types:
	- opened
	# - synchronize
	- reopened
	- closed

	jobs:


	import matplotlib.pyplot as plt
	import rasterio
	from rasterio.windows import Window
	import time
	import zarr

	def convert(raster_filepath, chunk_mbs=1):
	"""
	Converts raster file to chunked and compressed zarr array. Tested

	@media only screen and (max-width: 413px) {
	}

	@media only screen and (min-width: 414px) and (max-width: 767px) {
	}

	@media only screen and (min-width: 768px) and (max-width: 1023px) {
	}

	@media only screen and (min-width: 1024px) and (max-width: 1279px) {