A curated archive of 313 AI prompts for ChatGPT, Claude, Gemini, and anywhere else you need them. Search, filter by category, and copy with one click.
Defines Python package dependencies with version constraints for a Generative AI Streamlit application.
google-generativeai==0.8.3 streamlit==1.40.2 agno>=2.2.10
Automated GTM agent that discovers companies, finds decision makers, and generates personalized cold emails using AI.
### 🎓 FREE Step-by-Step Tutorial **👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-an-ai-email-gtm-outreach-agent-team) and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.** An intelligent, fully automated B2B outreach system that discovers companies, finds decision makers, researches company
Specifies minimum version dependencies for agno, streamlit, pydantic, and openai packages.
agno>=2.0.4 streamlit>=1.32.0 pydantic>=2.0.0 openai>=1.0.0
Autonomous agent cross-referencing public records to detect childcare provider fraud via building data, licensing, and imagery.
## 🔍 AI Fraud Investigation Agent An AI-powered autonomous fraud investigation agent that cross-references childcare provider licensing records against physical building data to detect anomalies. The agent uses public data — Cook County property records, Illinois DCFS licensing, Google Maps, and the Secretary of State — to find facilities where the physical evidence doesn't match the paperwork.
Specifies minimum version dependencies for agno, openai, streamlit, requests, and beautifulsoup4.
agno>=2.5.9 openai>=2.23.0 streamlit>=1.54.0 requests>=2.32.5 beautifulsoup4>=4.14.3
Streamlit app using GPT-4o to generate personalized budgets, investment strategies, and savings plans.
## 💰 AI Personal Finance Planner This Streamlit app is an AI-powered personal finance planner that generates personalized financial plans using OpenAI GPT-4o. It automates the process of researching, planning, and creating tailored budgets, investment strategies, and savings goals, empowering you to take control of your financial future with ease. ### Features - Set your financial goals and prov
Lists Python package dependencies for a Streamlit app using Agno, OpenAI, and Google Search Results.
streamlit agno>=2.2.10 openai google-search-results
Streamlit app using Claude 3.5 Sonnet to automate script writing, casting, and movie concept generation.
## 🎬 AI Movie Production Agent ### 🎓 FREE Step-by-Step Tutorial **👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-an-ai-movie-production-agent-with-claude-3-5-sonnet) and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.** This Streamlit app is an AI-powered movie production assistant that help
A minimal requirements file listing Python dependencies for a Streamlit app with Agno and Google search.
streamlit agno>=2.2.10 google-search-results lxml_html_clean
Two OpenAI-powered AI agents play chess against each other with move validation via Autogen and Streamlit.
### 🎓 FREE Step-by-Step Tutorial **👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-a-multi-agent-chess-game) and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.** An advanced Chess game system where two AI agents play chess against each other using Autogen in a streamlit app. It is built with
Python package requirements for a Streamlit chess app using Autogen, CairoSVG, and Pillow.
streamlit chess==1.11.1 autogen==0.6.1 cairosvg pillow
Two AI agents using different LLMs compete in Tic-Tac-Toe, coordinated by a referee agent via Agno and Streamlit.
An interactive Tic-Tac-Toe game where two AI agents powered by different language models compete against each other built on Agno Agent Framework and Streamlit as UI. This example shows how to build an interactive Tic Tac Toe game where AI agents compete against each other. The application showcases how to: - Coordinate multiple AI agents in a turn-based game - Use different language models for d
Auto-generated Python dependency lock file for a tic-tac-toe app using Streamlit and multiple AI providers.
# ./generate_requirements.sh
agno>=2.2.10
# via -r cookbook/examples/apps/tic_tac_toe/requirements.in
altair==5.5.0
# via streamlit
annotated-types==0.7.0
# via pydantic
anthropic==0.47.1
# via -r cookbook/examples/apps/tic_tac_toe/requirements.in
anyio==4.8.0
# via
# anthropic
# groq
# httpx
# openai
attrs==25.1.0
# via
# jsonschema
#
Multi-agent system generating and visualizing PyGame 3D code using DeepSeek R1, GPT-4o, and browser automation.
### 🎓 FREE Step-by-Step Tutorial **👉 [Click here to follow our complete step-by-step tutorial](https://www.theunwindai.com/p/build-an-ai-3d-pygame-visualizer-with-deepseek-r1) and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.** This Project demonstrates R1's code capabilities with a PyGame code generator and visualizer with browser use. T
A minimal requirements file listing Python packages for an AI agent application.
agno>=2.2.10 langchain-openai browser-use streamlit
Multi-agent 6-week sprint runbook for building and launching a startup MVP from idea to live product.
> **Mode**: NEXUS-Sprint | **Duration**: 4-6 weeks | **Agents**: 18-22 --- ## Scenario You're building a startup MVP — a new product that needs to validate product-market fit quickly. Speed matters, but so does quality. You need to go from idea to live product with real users in 4-6 weeks. ## Agent Roster ### Core Team (Always Active) | Agent | Role | |-------|------| | Agents Orchestrator |
Orchestrates a 2-4 week multi-agent marketing campaign across social platforms with strategy, content, and analytics roles.
> **Mode**: NEXUS-Micro to NEXUS-Sprint | **Duration**: 2-4 weeks | **Agents**: 10-15 --- ## Scenario You're launching a coordinated marketing campaign across multiple channels. Content needs to be platform-specific, brand-consistent, and data-driven. The campaign needs to drive measurable acquisition and engagement. ## Agent Roster ### Campaign Core | Agent | Role | |-------|------| | Social
Multi-agent runbook covering detection, triage, mitigation, and post-mortem for P0–P3 production incidents.
> **Mode**: NEXUS-Micro | **Duration**: Minutes to hours | **Agents**: 3-8 --- ## Scenario Something is broken in production. Users are affected. Speed of response matters, but so does doing it right. This runbook covers detection through post-mortem. ## Severity Classification | Level | Definition | Examples | Response Time | |-------|-----------|----------|--------------| | **P0 — Critical*
Multi-agent runbook for 6-12 week enterprise feature development with compliance, QA, and stakeholder alignment.
> **Mode**: NEXUS-Sprint | **Duration**: 6-12 weeks | **Agents**: 20-30 --- ## Scenario You're adding a major feature to an existing enterprise product. Compliance, security, and quality gates are non-negotiable. Multiple stakeholders need alignment. The feature must integrate seamlessly with existing systems. ## Agent Roster ### Core Team | Agent | Role | |-------|------| | Agents Orchestrat
Ongoing operational playbook for post-launch product management with multi-agent cadences and continuous improvement loops.
> **Duration**: Ongoing | **Agents**: 12+ (rotating) | **Governance**: Studio Producer --- ## Objective Sustained operations with continuous improvement. The product is live — now make it thrive. This phase has no end date; it runs as long as the product is in market. ## Pre-Conditions - [ ] Phase 5 Quality Gate passed (stable launch) - [ ] Phase 5 Handoff Package received - [ ] Operational c
Coordinates go-to-market execution across 12 agents for maximum launch impact, covering technical deployment and marketing activation.
> **Duration**: 2-4 weeks (T-7 through T+14) | **Agents**: 12 | **Gate Keepers**: Studio Producer + Analytics Reporter --- ## Objective Coordinate go-to-market execution across all channels simultaneously. Maximum impact at launch. Every marketing agent fires in concert while engineering ensures stability. ## Pre-Conditions - [ ] Phase 4 Quality Gate passed (Reality Checker READY verdict) - [
Multi-agent playbook for final quality hardening, evidence collection, and production readiness gating.
> **Duration**: 3-7 days | **Agents**: 8 | **Gate Keeper**: Reality Checker (sole authority) --- ## Objective The final quality gauntlet. The Reality Checker defaults to "NEEDS WORK" — you must prove production readiness with overwhelming evidence. This phase exists because first implementations typically need 2-3 revision cycles, and that's healthy. ## Pre-Conditions - [ ] Phase 3 Quality Ga
Multi-agent orchestration playbook for continuous Dev↔QA sprint loops across parallel build tracks.
> **Duration**: 2-12 weeks (varies by scope) | **Agents**: 15-30+ | **Gate Keeper**: Agents Orchestrator --- ## Objective Implement all features through continuous Dev↔QA loops. Every task is validated before the next begins. This is where the bulk of the work happens — and where NEXUS's orchestration delivers the most value. ## Pre-Conditions - [ ] Phase 2 Quality Gate passed (foundation ver
Multi-agent playbook for building CI/CD pipelines, infrastructure, and frontend scaffolding in 3-5 days.
> **Duration**: 3-5 days | **Agents**: 6 | **Gate Keepers**: DevOps Automator + Evidence Collector --- ## Objective Build the technical and operational foundation that all subsequent work depends on. Get the skeleton standing before adding muscle. After this phase, every developer has a working environment, a deployable pipeline, and a design system to build with. ## Pre-Conditions - [ ] Phas
Multi-agent playbook for defining project architecture, strategy, and technical foundations before coding begins.
> **Duration**: 5-10 days | **Agents**: 8 | **Gate Keepers**: Studio Producer + Reality Checker --- ## Objective Define what we're building, how it's structured, and what success looks like — before writing a single line of code. Every architectural decision is documented. Every feature is prioritized. Every dollar is accounted for. ## Pre-Conditions - [ ] Phase 0 Quality Gate passed (GO deci
Multi-agent playbook for pre-build opportunity validation covering market, UX, data, legal, and tech discovery.
> **Duration**: 3-7 days | **Agents**: 6 | **Gate Keeper**: Executive Summary Generator --- ## Objective Validate the opportunity before committing resources. No building until the problem, market, and regulatory landscape are understood. ## Pre-Conditions - [ ] Project brief or initial concept exists - [ ] Stakeholder sponsor identified - [ ] Budget for discovery phase approved ## Agent Act
A comprehensive deployment doctrine for coordinating AI specialist agents across all project phases with defined handoffs and quality gates.
## The Agency's Complete Operational Playbook for Multi-Agent Orchestration > **NEXUS** transforms The Agency's independent AI specialists into a synchronized intelligence network. This is not a prompt collection — it is a **deployment doctrine** that turns The Agency into a force multiplier for any project, product, or organization. --- ## Table of Contents 1. [Strategic Foundation](#1-strate
Standardized templates for agent-to-agent handoffs, QA pass/fail verdicts, and retry loops in the NEXUS pipeline.
> Standardized templates for every type of agent-to-agent handoff in the NEXUS pipeline. Consistent handoffs prevent context loss — the #1 cause of multi-agent coordination failure. --- ## 1. Standard Handoff Template Use for any agent-to-agent work transfer. ```markdown # NEXUS Handoff Document ## Metadata | Field | Value | |-------|-------| | **From** | [Agent Name] ([Division]) | | **To**
Ready-to-use prompt templates for orchestrating and activating specialized agents within the NEXUS multi-agent pipeline.
> Ready-to-use prompt templates for activating any agent within the NEXUS pipeline. Copy, customize the `[PLACEHOLDERS]`, and deploy. --- ## Pipeline Controller ### Agents Orchestrator — Full Pipeline ``` You are the Agents Orchestrator executing the NEXUS pipeline for [PROJECT NAME]. Mode: NEXUS-[Full/Sprint/Micro] Project specification: [PATH TO SPEC] Current phase: Phase [N] — [Phase Name]
Quick-start guide for orchestrating multi-agent AI pipelines across Full, Sprint, and Micro deployment modes.
> **Get from zero to orchestrated multi-agent pipeline in 5 minutes.** --- ## What is NEXUS? **NEXUS** (Network of EXperts, Unified in Strategy) turns The Agency's AI specialists into a coordinated pipeline. Instead of activating agents one at a time and hoping they work together, NEXUS defines exactly who does what, when, and how quality is verified at every step. ## Choose Your Mode | I wan