🤖 AI Agents & Workflows

Project Breakdown

📁 AI Agents & Workflows 👤 Contributed by @magisterluditreintaytres@gmail.com 🗓️ Updated 2026-04-19

The prompt

ROLE: Act as a Senior Project Manager certified in PMP and Agile Scrum Master with Fortune 500 experience. INPUT: My current project is: "${describe_project}". GOAL: I need a fail-proof execution plan. REASONING STEPS (CHAIN OF THOUGHT): Deconstruction: Break down the project into Logical Phases (Phase 1: Foundation, Phase 2: Development, Phase 3: Launch/Delivery). Critical Path: Identify the tasks that, if delayed, delay the entire project. Mark them as ${critical}. Resource Allocation: For each phase, list the tools, skills, and human capital required. Pre-mortem Analysis: Imagine the project has failed 3 months from now. List 5 probable reasons for failure and generate a mitigation strategy for each one NOW. FORMAT: Markdown table for the schedule and bulleted list for the risk analysis.

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🚀 Open in ChatGPT ✨ Open in Claude 💎 Open in Gemini

How to use this prompt

Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.

Which AI model works best

Claude excels at agent workflows thanks to its long context window (up to 1M tokens) and nuanced instruction-following. ChatGPT has native Actions (tool-calling) built in. Gemini integrates best with Google Workspace data. For autonomous workflows, Claude Sonnet 4.6 is the current sweet-spot for quality and cost.

How to customize this prompt

Adjust the agent's role and constraints to your environment. If the prompt mentions specific tools (search, file I/O, code execution), remove what you don't have and add what you need. Add guardrails: "Always ask for confirmation before writing files." Define success criteria explicitly.

Common use cases

Building autonomous research assistants for a specific domain
Creating chatbots with defined personalities and knowledge limits
Orchestrating multi-step workflows (research → draft → review → publish)
Defining system prompts for custom GPTs or Claude Projects
Building agent loops that call tools and self-correct

Variations

Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).