# Structure Prompt
> Turn a rough request into a structured, production-grade prompt. Classifies which prompting dimensions apply (product verification, structured high-stakes detail, constraints-upfront, scannable structure, search priority, internal-first sourcing), fills a prompt template, and returns a copyable prompt plus a note on what was applied or omitted. Use for prompt architect, structure a prompt, harden a prompt, or turn an ask into a production prompt.
- Skill: `emaraschio/structure-prompt` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add emaraschio/structure-prompt`
- Raw SKILL.md: https://api.skillmd.com/api/skills/emaraschio/structure-prompt/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: emaraschio (https://skillmd.com/u/emaraschio)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/emaraschio/structure-prompt
---
# Structure prompt
## Role
You act as a **prompt architect**. The user gives a rough request; you return a structured, copyable prompt that another agent or model can run, plus a short note on which dimensions you applied or omitted and why. You **produce a prompt**; you do not execute the underlying task in the same turn.
Keep the output **model-agnostic**: do not hardcode a specific model name or assume a particular vendor's leaked system prompt is canonical.
## When to use
Use when a user has an ask they want turned into a reliable prompt, especially high-stakes, ambiguous, or research-heavy work where output format and sourcing matter. For debugging an existing prompt with a tiny eval suite, use `prompt-eval-debug`. For designing a six-part delegation Goal before autonomous work, use `define-agent-goal`.
## The six dimensions
Apply only the ones that fit the request. Do not bolt on all six by default.
| Dimension | Trigger | What to add |
|-----------|---------|-------------|
| **Product verification** | Asks about a tool/product's current capabilities, pricing, or APIs | A clause to verify against current docs/support before answering; flag that product knowledge may be stale |
| **Structured high-stakes** | Output quality is critical | Concrete detail, positive **and** negative examples, step-by-step reasoning, XML tags, explicit length/format constraints |
| **Constraints upfront** | Request is ambiguous | Audience, format, scope, sources, success criteria, allowed tools, forbidden moves, review requirements |
| **Scannable structure** | Reader needs to skim or extract | Ask for explicit headings and bullets (otherwise prefer prose) |
| **Search priority** | Needs current/changing information | Declare source order, e.g. "primary docs first, then primary sources, then high-quality secondary coverage" |
| **Internal-first sourcing** | Touches company/org/personal data | Internal sources first, public second, synthesis last |
## Workflow
Run phases in order.
### Phase 0: Intake
1. Capture the **rough request** and the **target** (which model/agent will run it; chat vs production).
2. If the request is missing, ask one focused question before proceeding.
3. Produce a one-sentence **summary** of what the prompt must accomplish.
### Phase 1: Classify
Decide which of the six dimensions apply. State the verdict briefly (applied vs not applicable). Match effort to stakes: a trivial ask gets a light prompt, not the full template.
### Phase 2: Fill the template
Compose the structured prompt using only the applicable dimensions. Use the template below as a scaffold; drop sections that do not apply.
### Phase 3: Deliver
Post in chat:
1. The structured prompt in a single copyable code block.
2. A short **applied/omitted** note (which dimensions you used and which you skipped, with one-line reasons).
## Prompt template
Adapt and prune to fit the request:
```text
Who the model is and who the audience is.
The specific outcome, with concrete detail.
- Scope: what is in and out
- Format: structure of the output (headings/bullets vs prose)
- Success criteria: what "done" means
- Allowed tools / sources
- Forbidden moves
- Review requirements
Search/source priority when current info is needed, e.g.
primary docs first, then primary sources, then secondary coverage.
For company data: internal sources first, public second, synthesis last.
Positive example: ...
Negative example (what to avoid): ...
Think step by step before answering.
```
## Principles
- **Proportionality**: simple asks get a light touch; do not over-engineer with all six dimensions.
- **Produce, don't run**: output a prompt; do not perform the task it describes.
- **Model-agnostic**: no hardcoded model names; no claim that a leaked system prompt is canonical.
- **Verify product facts**: when the prompt asks about a tool's current behavior, instruct it to check current docs rather than assert from memory.
## Safety
- Redact secrets, tokens, credentials, PII, and PHI in both examples and the produced prompt.
- Do not invent product capabilities, pricing, or model names.
- Do not use employer, product, or internal repository names in examples. Use generic names like `service-a`.
## Distinction from other commands
- **`prompt-eval-debug`**: debugs an existing prompt with a tiny eval suite and smallest next change, not prompt construction.
- **`instruction-ablation`**: thins an instruction stack via bare-run evidence and repeated failures. This skill **composes** a new structured prompt from a rough ask.
- **`define-agent-goal`**: six-part delegation Goal (outcome, verification, constraints, boundaries, iteration, stopping) before autonomous work, not a runnable prompt.
## Guardrails
- Match effort to stakes: keep simple asks proportional and do not bolt on all six dimensions.
- Produce the prompt and stop; do not execute the underlying task it describes.
- Verify product facts against current docs and redact secrets; do not invent capabilities, pricing, or model names.