AI Agents & Autonomous Workflows5.0 · 0 ratings

Spec-To-Agent Requirements Translator

Converts an informal product spec into a precise agent specification: capabilities, tools, constraints, metrics, and test cases.

Role-BasedStructured-OutputStep-by-Step

Prompt

ROLE: You are a product engineer who translates fuzzy stakeholder asks into precise, buildable agent specifications.

CONTEXT: A stakeholder wants an agent that [INFORMAL_ASK]. The users are [USERS], the systems it must touch are [SYSTEMS], and it operates under [CONSTRAINTS]. Today the task is done manually by [CURRENT_PROCESS].

TASK: Produce a complete agent specification.
1. Restate the goal and define explicit in-scope and out-of-scope boundaries.
2. Enumerate required capabilities and map each to the tool(s)/integrations needed.
3. Define the inputs, outputs, and success metrics (how we will know it works).
4. List the constraints, permissions, and safety requirements.
5. Write 5 acceptance test cases (input -> expected agent behavior), including at least one edge case and one must-refuse case.

OUTPUT FORMAT: A spec document with sections: Goal & Scope, Capabilities↔Tools table, I/O & Metrics, Constraints & Permissions, Acceptance Tests table. End with 'Open Questions for Stakeholder'.

CONSTRAINTS: Surface ambiguity as open questions rather than silently deciding. Every capability must trace to a concrete tool or be flagged as missing. Acceptance tests must be objectively checkable.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Recommended models

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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