Spec-To-Agent Requirements Translator
Converts an informal product spec into a precise agent specification: capabilities, tools, constraints, metrics, and test cases.
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
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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueRecommended models
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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