Coding
Code Documentation Skeleton Generator
Code Documentation Skeleton Generator turns technical context into clear code, diagnosis, or implementation guidance with clear constraints and an explicit output.
The prompt
Code Documentation Skeleton Generator guides the model through a defined task while preserving the source prompt's useful structure and constraints. Use it when planning, writing, reviewing, or debugging software and you want a response that is easier to evaluate and act on.
text prompt
You are a technical writer.
Model guidance (ChatGPT GPT-5.2):
- If available, use GPT-5.2 Thinking for planning and GPT-5.2 Pro for the hardest steps.
- If your environment supports a reasoning effort setting, use xhigh only for the most complex parts, otherwise use the default.
Task:
Create documentation skeleton for {{system}}: overview, setup, runbook, troubleshooting, and FAQ. Provide section headings and bullet content to fill in.
Inputs:
- System or repo name: {{system}}
- Audience: {{audience}}
- Known docs gaps: {{knownGaps}}
Output requirements:
- Return JSON only. Do not wrap in Markdown or code fences.
- Do not add keys beyond the schema.
- Populate clarifying_questions if any critical input is missing or ambiguous, but still produce a best-effort deliverable.
- Keep explanations brief. Put the main work in deliverable_markdown.
JSON schema (example values only, keep the same keys):
{
"title": "Code Documentation Skeleton Generator",
"deliverable_type": "Documentation outline",
"clarifying_questions": [],
"assumptions": [],
"deliverable_markdown": "",
"quality_checks": [
"Outline matches audience needs",
"Includes setup and troubleshooting sections",
"Defines what to document vs what to link",
"Includes an ownership and update cadence suggestion"
],
"next_steps": []
}
Tune the prompt, not the plumbing.
Every control comes from this prompt’s content schema. Changes stay in your browser and update instantly.
Customized prompt1339 characters
You are a technical writer.
Model guidance (ChatGPT GPT-5.2):
- If available, use GPT-5.2 Thinking for planning and GPT-5.2 Pro for the hardest steps.
- If your environment supports a reasoning effort setting, use xhigh only for the most complex parts, otherwise use the default.
Task:
Create documentation skeleton for a specific system: overview, setup, runbook, troubleshooting, and FAQ. Provide section headings and bullet content to fill in.
Inputs:
- System or repo name: a specific system
- Audience: busy small-business owners
- Known docs gaps: a specific known gaps
Output requirements:
- Return JSON only. Do not wrap in Markdown or code fences.
- Do not add keys beyond the schema.
- Populate clarifying_questions if any critical input is missing or ambiguous, but still produce a best-effort deliverable.
- Keep explanations brief. Put the main work in deliverable_markdown.
JSON schema (example values only, keep the same keys):
{
"title": "Code Documentation Skeleton Generator",
"deliverable_type": "Documentation outline",
"clarifying_questions": [],
"assumptions": [],
"deliverable_markdown": "",
"quality_checks": [
"Outline matches audience needs",
"Includes setup and troubleshooting sections",
"Defines what to document vs what to link",
"Includes an ownership and update cadence suggestion"
],
"next_steps": []
}
Useful structure, room to move.
Code Documentation Skeleton Generator guides the model through a defined task while preserving the source prompt's useful structure and constraints. Use it when planning, writing, reviewing, or debugging software and you want a response that is easier to evaluate and act on.
The prompt establishes the job first, then supplies concrete decisions a model can act on. The variables preserve that structure while letting you change the subject, context, or output.
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