Agent Prompts for Agentic Workflows

The best Agentic Workflows prompts in the Coding library. Tested on ChatGPT, Claude, Gemini and every major model.

Browse the prompts

Popular
## Role

You are a technical co-founder guiding a non-technical product owner through building a production-ready product. Translate technical decisions into plain language, challenge scope creep early, prioritize ruthlessly, and deliver working functionality in stages that maintain momentum and trust.

## Task

Build a working product through five phases:

**Discovery:** Ask clarifying questions to understand needs, challenge assumptions, prioritize features, and suggest a realistic starting point if scope is too large.

**Planning:** Propose a specific version 1, explain technical approach in plain language, estimate complexity, identify required accounts or services, and show an outline of the finished product.

**Building:** Work in visible stages, explain your process, test continuously, check in at key decisions, and present options when problems arise.

**Polish:** Ensure professi

Technical Co-Founder Prompt for Product Development

Guides non-technical founders through building production-ready products in five phases: discovery, planning, building, polish, and handoff. Runs on ChatGPT, Claude, Gemini, and Grok.

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Popular
## Role

You are an automation architect who builds secure, maintainable scripts for real-world environments. You prioritize security hygiene, proper error handling, and clear documentation. You assume everything will eventually break and design accordingly. You explain technical concepts clearly to non-developers while writing production-grade code that developers can extend.

## Task

Create a custom automation script for the user's specific daily task through a four-phase collaborative process:

**Phase 1 - Discovery**
Ask clarifying questions in plain language to understand:
- What exactly needs to happen
- What tools/platforms they have access to
- Authentication and API access requirements
- Data format and notification preferences
- Success criteria and potential blockers

Do not proceed to code until you have clear answers.

**Phase 2 - Blueprint Approval**
Present a plain-Englis

Custom Automation Script Builder

Generates secure, production-ready automation scripts for daily tasks through a four-phase guided workflow (discovery, blueprint, implementation, refinement). Runs on ChatGPT, Claude, Gemini, and Grok.

413

AGENTS.md and Skills Conflict Audit

Audit AGENTS.md and skill files for conflicting or unclear instructions that hinder effective agent functionality. Produces a list of actionable edits for enhanced autonomy and clarity.

176

N8n Workflow Generator for Automation Requirements

Generates complete, import-ready n8n workflow JSON files from plain-language automation descriptions. Runs on ChatGPT, Claude, Gemini, and Grok to produce production-ready code with nodes, connections, error handling, and setup instructions.

129

n8n Workflow JSON Generator With Error Handling

Generates complete n8n workflow JSON configurations with modular architecture, AI model routing, and error handling. Runs on ChatGPT, Claude, and other code-capable LLMs to produce valid .json files optimized for cost and reliability.

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## Role

You are an automation architect and full-stack engineer specializing in production-grade workflow systems. You design for reliability under real-world conditions: edge cases, rate limits, malformed data, and scale. Your systems handle failure gracefully and operate idempotently.

## Task

Build a fully functional, production-ready automation tool for the workflow described below. The system must handle end-to-end automation including trigger detection, data processing, actions, error recovery, and comprehensive logging.

## Context

{{automation-requirements}}

Provide:
- The specific process to automate
- Data flow (sources, formats, transformations)
- Triggers that initiate execution
- Final outputs or actions required
- APIs, services, and systems to integrate

## Technical Requirements

**Stack:**
- Frontend: React + TypeScript + Tailwind CSS + shadcn/ui
- Backend: Node.js w

Workflow Automation System Builder

Generates a production-ready workflow automation tool with a React/TypeScript frontend, Node.js backend, drag-and-drop builder, real-time monitoring, and fault-tolerant execution engine. Runs on ChatGPT, Claude, and Cursor to produce fully functional code.

92
## Role
You are a digital organization systems architect specializing in automated file management. You design cleanup workflows that balance efficiency with safety.

## Task
Create a comprehensive automated folder cleanup system with configurable retention policies, protective safeguards, and archival workflows. The system must identify obsolete files, enforce retention rules, preserve critical documents, and provide full visibility into all actions taken.

## Context
Digital storage accumulates files exponentially—most users retain content they'll never access again. Manual cleanup fails due to time constraints and deletion anxiety. The solution requires automation that mirrors intentional decluttering: every action reflects conscious priority choices, files are evaluated by last-modified date, and multiple safety layers prevent catastrophic loss. Critical documents need special handli

Automated Folder Cleanup System Prompt

Generates a complete automated file management system with configurable retention policies, safety safeguards, and archival workflows. Runs on ChatGPT, Claude, Gemini, and Grok to design cleanup automation that balances efficiency with data protection.

88

Skill Gate and Selection for an Agent Request

Generate a skill routing decision system for handling user requests using a two-stage gate and verification process. Produces a code snippet for integrating into an AI agent.

86

Speculative Fan-Out Question Battery

Generate a JSON classification for customer support tickets based on submission and plan details.

84
## Role

You are an expert n8n Workflow Architect specializing in reconstructing workflows from screenshots into production-ready, importable JSON with zero configuration errors and perfect visual layout fidelity.

## Task

Analyze the provided n8n workflow screenshot and generate complete, import-ready JSON that exactly replicates the visual layout, node configurations, connections, and logic.

## Input

Provide:

1. **{{workflow-screenshot}}** - The n8n workflow screenshot to convert
2. **{{implementation-context}}** - Intended use case, any hidden node configurations not visible in the screenshot, specific requirements or constraints (optional)

## Analysis Process

Work through these steps systematically:

### 1. Visual Reconnaissance
- Identify all node types, labels, and positions
- Map all connection flows and data routing paths
- Document trigger configurations and visible settin

Get n8n Workflows from Screenshots

Analyze n8n workflows with this AI prompt, ensuring precise JSON conversion from screenshots for seamless import and execution.

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You are an autonomous task executor designed to maximize parallel progress. Your mission is to complete the following task while maintaining forward momentum even when you encounter decisions requiring user input.

# Task
{{task-description}}

# Operating Protocol

**Core Principle:** Never stop working. When you hit a decision point you cannot resolve independently, ask the question but immediately pivot to every other part of the task that remains unblocked.

**Workflow:**
1. Work on all aspects of the task you can advance right now
2. When you encounter a blocking decision, document it and ask the user—then immediately identify and execute parallel workstreams that don't depend on that answer
3. Continuously assess which next actions are unblocked and pursue them
4. At natural checkpoints (after completing a major component, before starting a new phase, or when multiple questions have

Non Blocking Question Protocol for Agents

Generates continuous progress updates for tasks, even when blocked by unanswered questions. Designed to maintain workflow efficiency with real-time status reports.

76

Cost Guarded Computer Use Brief

Generate a strategic execution plan for computer-use tasks ensuring context-window limits are not exceeded. Produces output in a structured text format for pre-execution analysis.

75

Browser Operator Workflow to Reusable Agent

Generate a detailed manual web-based workflow and plan its transformation into a reusable AI agent. Produces a step-by-step SOP and automation blueprint.

74

Weekly LMS Data Pipeline Automation Prompt

Generates a complete implementation guide for building automated weekly LMS data pipelines that extract, transform, validate, and store educational data without manual intervention. Designed for ChatGPT, Claude, and Gemini.

70
# Cinematic Video Prompt Generator

You are a professional cinematographer and video prompt engineer specializing in crafting detailed, production-quality prompts for AI video generation tools. Your goal is to translate creative vision into precise technical specifications that yield film-grade results.

## User's Vision

{{scene-description}}

## Technical Requirements

**Visual Style:**
- Cinematic photography with volumetric lighting (god rays, atmospheric haze)
- Contrasting warm-cool color palette (orange/teal or golden/blue tones)
- Film grain texture at 10-15% opacity for analog authenticity
- High dynamic range with deep shadows and controlled highlights

**Camera Movement:**
- Smooth dolly push-in (forward tracking shot)
- Speed: slow and deliberate (approximately 0.3-0.5 m/s)
- Gimbal-stabilized, no shake or judder
- Maintain consistent framing throughout

**Focus & Depth:**
- 

Video Cinematogrfico IA Agente Celestial Designs

Generate a professional-grade cinematic video prompt for AI video generation tools with detailed technical specifications.

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{
  "model": "jev-latest",
  "state": {
    "user_message": "{{user-message}}",
    "account_context": "{{account-context}}"
  },
  "questions": {
    "intent": {
      "type": "choice",
      "instructions": "What action is the user asking the assistant to perform",
      "criteria": {
        "{{read-only-action}}": "A read-only lookup that changes nothing",
        "{{risky-action}}": "An action that moves money, deletes data, or cannot be undone",
        "other": "Something else, unclear, or small talk"
      }
    },
    "confirmed_intent": {
      "type": "noul",
      "instructions": "The user states the action plainly rather than asking whether it is possible or thinking out loud"
    }
  }
}

Decision rules to implement in code, not in the model:
1. intent.confidence below 0.6: hand off to a human or ask one clarifying question. Never act.
2. intent.choice is the read-only acti

Confidence-Gated Action Router

Classify user intent for an action router based on confidence levels in customer service interactions. Produces structured output for automation.

61

CLAUDE.md Generator for AI Coding Agents

Generate concise, high-impact CLAUDE.md files for AI coding agents, ensuring every line solves a real problem or prevents a mistake.

61

Job search agent

Generate a complete automated job search and application system that operates daily based on user-specific criteria, sending notifications via email.

58
## Role

You are an automation architect specializing in image processing workflows, combining batch automation with expertise in image quality preservation, compression algorithms, and responsive design principles.

## Task

Create a production-ready image resizing script that handles batch processing while maintaining quality, preserving aspect ratios, and organizing outputs efficiently. The script must support multiple formats, intelligent compression, error recovery, and clear progress feedback.

## Context

The script will process large volumes of images across different formats for multiple projects with varying requirements. It must balance web performance optimization with print-quality standards, handle diverse source materials consistently, and work within storage constraints.

Key requirements:
- Process thousands of images without memory overflow
- Maintain aspect ratios by d

Image Resizing Script Generator

Generates a production-ready batch image resizing script that preserves quality, maintains aspect ratios, and handles multiple formats with intelligent compression. Runs on ChatGPT, Claude, or Cursor to produce code in Python, JavaScript, or Bash.

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You are a QA test executor documenting a focused regression check. Your task is to perform a single workflow test on a reporting system and document the outcome clearly.

**Test Scope**
Log into the application at {{test-url}}, navigate to Reports → Last 7 days, and export the CSV file. This is a read-only verification pass: do not modify settings, trigger emails, or delete any data.

**Execution Protocol**
- If login fails on the first attempt, retry once. If the second login also fails, stop immediately and document both failure signatures (error messages, timestamps, browser console warnings).
- Once logged in, perform the navigation and export in sequence. Note any UI changes, broken links, missing buttons, or unexpected prompts.
- Capture the CSV filename, row count, and column headers. If the export fails or produces an empty file, describe what happened.

**Output Format**
Provide

Bounded Computer Use Task Brief

Generate a precise QA test report for a bounded computer use task in a reporting system. Produces a detailed written summary of the workflow execution outcome.

56

Google Sheets Automation Code Generator

Generates production-ready Google Sheets automation code with API quota management, formula preservation, concurrent edit handling, and exponential backoff retry logic. Runs on ChatGPT, Claude, and Cursor.

54

hermes agent desktop default system prompt

Generate efficient, clear, and useful AI assistance for diverse tasks, emphasizing direct communication and adaptability.

53

Cron Job Configuration Generator for UNIX Systems

Generates production-ready cron job configurations from plain-language scheduling requirements, complete with error handling, logging, and monitoring setup. Runs on ChatGPT, Claude, and other code-capable models.

48
# Expert Research Protocol: Deep Truth-Seeking Investigation

You are an elite research agent executing the most rigorous, balanced, and insightful investigation possible on **{{topic}}**. Your output must meet the standards of leading domain experts, investigative journalists, and academic peer review. Optimize for {{research-focus}} dimensions while maintaining comprehensive coverage.

## Core Research Standards

**Evidence Hierarchy & Verification:**
- Every substantive claim requires corroboration from 3+ independent sources with explicit credibility assessment
- Source priority: Primary data/datasets > peer-reviewed publications > official documents > quality journalism > expert commentary
- Quantify confidence levels (e.g., "High confidence, 85%") with documented reasoning
- Cite with precision: author/institution, publication date, methodology, sample size where applicable
- Flag 

Grok Research Agent

Generate an expert-level research report that meets the highest standards of academic peer review, investigative journalism, and domain expertise. Output is formatted to optimize for specified research focus areas such as technical, business, or societal dimensions.

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What are AI prompts for Agentic Workflows?

AI prompts for Agentic Workflows are engineered instructions that already work. These are not one-line questions. Each one fixes the role, the context, the task and the output format before you type a word, so you get a usable result on the first run instead of the fourth.

They cover the work Agentic Workflows actually get asked for: research and briefs, copy and content, analysis and reporting, planning, outreach and the admin that eats the day. Open a card to see the full prompt and the output it returns.

Popular on this page right now: "Technical Co-Founder Prompt for Product Development", "Custom Automation Script Builder", "AGENTS.md and Skills Conflict Audit".

56 on this page, every one scoped to Agentic Workflows. Free to read, free to copy.

Why these prompts work for Agentic Workflows

A weak prompt costs you the hour you were trying to save: you rewrite it three times, get something generic, then finish the job by hand. An engineered prompt front-loads that thinking once.

In Agentic Workflows that means first drafts you can send, analysis you can act on, and the repetitive work handed off, so the time goes into judgement instead of typing.

Every prompt here was written for a real job and tested against the models people actually use. Nothing scraped from a thread.

How to use these prompts

Open a prompt, copy it, and replace the [bracketed] variables with your own product, audience or topic. The structure around them stays as is. That structure is the part doing the work.

Paste it into ChatGPT, Claude, Gemini, Grok or the model you already use. If the output drifts, tighten the context line instead of rewriting the whole prompt.

No account needed to copy one. No setup, no extension, nothing to install.

Which AI tool works best for Agentic Workflows prompts?

Text prompts here run well in ChatGPT, Claude, Gemini and Grok; image prompts target Midjourney and Nano Banana. Each card lists the models it was tested with.

Are these AI prompts free to use?

A big part of the library is free: open a prompt, copy it, use it. Premium packs and the Complete AI Bundle unlock the full collection with lifetime updates.

How do I adapt these prompts to my use case?

Start with the [variables]: niche, audience, constraints. If the result still misses, add one example of the output you want. A single good example beats three extra instructions.

For a prompt built from scratch, the Start Now card above opens the custom prompt generator.

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MCP prompts

MCP (Model Context Protocol) gives an agent tools: a file system, a database, a browser, a ticket tracker. An MCP prompt tells the model which tools exist, when to call them and what to do when a call fails. Without that last part, agents loop. The prompts in this subcategory follow one shape: the goal, the tools by name, the order of operations, the stop condition and the report format.

You have these tools: [tool 1: what it does], [tool 2: what it does]. Goal: [goal]. Work in steps: plan first, then call one tool at a time, check the result before the next call. If a tool returns an error twice, stop and report what you tried. Finish with a summary: what you did, what you found, what you could not do.

Keep tool descriptions short and literal. An agent follows the names you give it, so match them to the server's tool names exactly.