Prompt-as-Spec Builder
If your product uses AI, prompts are product decisions — not implementation details. The wording, persona, constraints, and output format shape user experience as much as any UI decision. This skill writes a versioned, production-quality system prompt and packages it as a handoff artefact for engineering.
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Context
Most teams treat prompts as engineering's responsibility. The result: the model behaves in ways nobody designed, and when something goes wrong, nobody owns it. This skill transfers prompt ownership to the PM — where it belongs.
A well-written prompt spec includes: persona, task, constraints, output format, edge cases, and version metadata.
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Step 1 — Gather the feature context
Ask for: what the AI feature does, who the end user is, the AI's role, hard constraints, output format, example of great output, and target model/platform.
Step 2 — Design the prompt architecture
Five layers: Persona, Task Definition, Constraints, Output Format, Edge Case Handling. Work through all five before writing prompt text.
Step 3 — Write the system prompt
Rules: second person, persona in first sentence, explicit section headers, constraints as explicit prohibitions, output format with examples, graceful refusal messages.
Step 4 — Write the prompt spec document
Package as a handoff artefact with: feature context, model target, complete system prompt, design decisions (why this persona, why these constraints, why this output format), test cases (typical, edge, out-of-scope, empty, adversarial), change log, and review sign-offs.
Step 5 — Versioning and ownership rules
Treat prompt changes like code changes. Test before deploy. Log prompt versions in deployment system. Own prompts in the PM ticket. Review prompts quarterly.