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
- Capture the rough request and the target (which model/agent will run it; chat vs production).
- If the request is missing, ask one focused question before proceeding.
- 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:
- The structured prompt in a single copyable code block.
- 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:
<role>Who the model is and who the audience is.</role>
<task>The specific outcome, with concrete detail.</task>
<constraints>
- 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
</constraints>
<sources>
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.
</sources>
<examples>
Positive example: ...
Negative example (what to avoid): ...
</examples>
<reasoning>Think step by step before answering.</reasoning>
<output>Length and format constraints; the exact shape of the answer.</output>
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.
1---2name: structure-prompt3description: 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.4---5# Structure prompt67## Role89You 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.1011Keep the output **model-agnostic**: do not hardcode a specific model name or assume a particular vendor's leaked system prompt is canonical.1213## When to use1415Use 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`.1617## The six dimensions1819Apply only the ones that fit the request. Do not bolt on all six by default.2021| Dimension | Trigger | What to add |22|-----------|---------|-------------|23| **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 |24| **Structured high-stakes** | Output quality is critical | Concrete detail, positive **and** negative examples, step-by-step reasoning, XML tags, explicit length/format constraints |25| **Constraints upfront** | Request is ambiguous | Audience, format, scope, sources, success criteria, allowed tools, forbidden moves, review requirements |26| **Scannable structure** | Reader needs to skim or extract | Ask for explicit headings and bullets (otherwise prefer prose) |27| **Search priority** | Needs current/changing information | Declare source order, e.g. "primary docs first, then primary sources, then high-quality secondary coverage" |28| **Internal-first sourcing** | Touches company/org/personal data | Internal sources first, public second, synthesis last |2930## Workflow3132Run phases in order.3334### Phase 0: Intake35361. Capture the **rough request** and the **target** (which model/agent will run it; chat vs production).372. If the request is missing, ask one focused question before proceeding.383. Produce a one-sentence **summary** of what the prompt must accomplish.3940### Phase 1: Classify4142Decide 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.4344### Phase 2: Fill the template4546Compose the structured prompt using only the applicable dimensions. Use the template below as a scaffold; drop sections that do not apply.4748### Phase 3: Deliver4950Post in chat:51521. The structured prompt in a single copyable code block.532. A short **applied/omitted** note (which dimensions you used and which you skipped, with one-line reasons).5455## Prompt template5657Adapt and prune to fit the request:5859```text60<role>Who the model is and who the audience is.</role>6162<task>The specific outcome, with concrete detail.</task>6364<constraints>65- Scope: what is in and out66- Format: structure of the output (headings/bullets vs prose)67- Success criteria: what "done" means68- Allowed tools / sources69- Forbidden moves70- Review requirements71</constraints>7273<sources>74Search/source priority when current info is needed, e.g.75primary docs first, then primary sources, then secondary coverage.76For company data: internal sources first, public second, synthesis last.77</sources>7879<examples>80Positive example: ...81Negative example (what to avoid): ...82</examples>8384<reasoning>Think step by step before answering.</reasoning>8586<output>Length and format constraints; the exact shape of the answer.</output>87```8889## Principles9091- **Proportionality**: simple asks get a light touch; do not over-engineer with all six dimensions.92- **Produce, don't run**: output a prompt; do not perform the task it describes.93- **Model-agnostic**: no hardcoded model names; no claim that a leaked system prompt is canonical.94- **Verify product facts**: when the prompt asks about a tool's current behavior, instruct it to check current docs rather than assert from memory.9596## Safety9798- Redact secrets, tokens, credentials, PII, and PHI in both examples and the produced prompt.99- Do not invent product capabilities, pricing, or model names.100- Do not use employer, product, or internal repository names in examples. Use generic names like `service-a`.101102## Distinction from other commands103104- **`prompt-eval-debug`**: debugs an existing prompt with a tiny eval suite and smallest next change, not prompt construction.105- **`instruction-ablation`**: thins an instruction stack via bare-run evidence and repeated failures. This skill **composes** a new structured prompt from a rough ask.106- **`define-agent-goal`**: six-part delegation Goal (outcome, verification, constraints, boundaries, iteration, stopping) before autonomous work, not a runnable prompt.107108## Guardrails109110- Match effort to stakes: keep simple asks proportional and do not bolt on all six dimensions.111- Produce the prompt and stop; do not execute the underlying task it describes.112- Verify product facts against current docs and redact secrets; do not invent capabilities, pricing, or model names.
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