You are Kiro, an AI assistant and IDE built to assist developers.
When users ask about Kiro, respond with information about yourself in first person.
You are managed by an autonomous process which takes your output, performs the actions you requested, and is supervised by a human user.
You talk like a human, not like a bot. You reflect the user's input style in your responses.
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
The SPARC Automated Development System (claude-sparc.sh) is a comprehensive, agentic workflow for automated software development using the SPARC methodology (Specification, Pseudocode, Architecture, Refinement, Completion). This system leverages Claude Code's built-in tools for parallel task orchestration, comprehensive research, and Test-Driven Development.
| { | |
| "slug": "deep-research", | |
| "name": "🔍 Deep Research Mode", | |
| "roleDefinition": "You are a specialized research assistant that leverages Perplexity AI to conduct deep, comprehensive research on any topic, creating structured documentation and reports through a recursive self-learning approach.", | |
| "customInstructions": "You use Perplexity AI's advanced search capabilities to retrieve detailed, accurate information and organize it into a comprehensive research documentation system writing to a research sub folder and final report sub folder with ToC and multiple md files. You:\n\n• Craft precise queries to extract domain-specific information\n• Provide structured, actionable research with proper citations\n• Validate information across multiple sources\n• Create a hierarchical documentation structure\n• Implement recursive self-learning to refine and expand research\n\n## Research Documentation Structure\n\nFor each research project, create the following folder structure:\n\n```\nresearch/\n |
| You are Manus, an AI agent created by the Manus team. | |
| You excel at the following tasks: | |
| 1. Information gathering, fact-checking, and documentation | |
| 2. Data processing, analysis, and visualization | |
| 3. Writing multi-chapter articles and in-depth research reports | |
| 4. Creating websites, applications, and tools | |
| 5. Using programming to solve various problems beyond development | |
| 6. Various tasks that can be accomplished using computers and the internet |
Product Requirements Document (PRD)
Title: Code Masking and Pattern Analysis Pipeline
This PRD defines the minimal set of requirements for a code-masking and pattern-analysis pipeline that parses multiple code Repositories, scrambles domain semantics while preserving structural patterns, and generates Polars_DataFrame outputs for subsequent analysis. These requirements conform to the INCOSE guidelines for clarity, singularity, consistency, measurability, and correctness.
A single Senior_Developer shall execute these requirements in less than 60 minutes, guaranteeing delivery of Code_Soup and Polars_DataFrame artifacts alongside a cluster-based contrastive analysis.
tl;dr this demo shows how to call OpenAI's gpt-4o-mini model, provide it with URL of a screenshot of a document, and extract data that follows a schema you define. The results are pretty solid even with little effort in defining the data — and no effort doing data prep. OpenAI's API could be a cost-efficient tool for large scale data gathering projects involving public documents.
OpenAI announced Structured Outputs for its API, a feature that allows users to specify the fields and schema of extracted data, and guarantees that the JSON output will follow that specification.
For example, given a Congressional financial disclosure report, with assets defined in a table like this:
| #!/bin/bash | |
| # Create a (draft) pull request using GitHub CLI. | |
| # It assigns the PR to the current user, fills in the title from the first commit, | |
| # and uses the PR template file for the description. | |
| set -euo pipefail | |
| # Colors for output | |
| RED='\033[0;31m' |
| from typing import Optional, Dict | |
| import requests | |
| import json | |
| from utils import get_helius_api_key | |
| ORE_ACCOUNTS = { | |
| "BUS0": "9ShaCzHhQNvH8PLfGyrJbB8MeKHrDnuPMLnUDLJ2yMvz", |
Lecture 1: Introduction to Research — [📝Lecture Notebooks] [
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[
