OperationsMaster PromptAdvanced

SOP Builder

Create a repeatable SOP from a messy workflow — so a new team member can follow it without guessing or pinging you every five minutes.

Best ModelChatGPT GPT-5.5 / Claude Sonnet 4.6Process to playbook
Brevity ModeDetailed
DifficultyAdvanced
AutomationNeeds user context

Use This When

Onboarding new hires, scaling agency delivery, documenting tribal knowledge before a key person leaves, removing yourself as the bottleneck.

Inputs Needed

Process, people, tools, inputs, approvals, failure points.

Expected Output

SOP, RACI, checklist, handoff rules, escalation rules, automation opportunities, QA checks.

The Workflow Prompt

prompt.md21 lines
You are a operations consultant and SOP architect.

Objective:
Create a repeatable SOP from a messy workflow.

Inputs I will provide:
Workflow description, tools used, people involved, start/end triggers, approvals, common failures, desired outcome.

Instructions:
- Start by identifying the business objective and the likely leverage point.
- Ask up to 5 focused questions only if required. If enough context exists, proceed and label assumptions.
- Produce client-ready work, not generic advice.
- Use concrete examples, templates, and priority order.
- Mention risks, dependencies, and what must be verified before launch.
- End with a short QA checklist and next 3 actions.

Required output:
SOP, RACI, checklist, handoff rules, escalation rules, automation opportunities, QA checks.

Quality bar:
A new team member should be able to follow it without guessing.
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QA Follow-Up Checklist

After the AI returns its output, verify against:

  1. 1Output is specific to the provided business and context.
  2. 2Assumptions are clearly labeled.
  3. 3No unsupported claims without source checks.
  4. 4Next actions are clear and usable.

Follow-Up Prompt

refinementRun after first output
Now turn the result for 'SOP Builder' into a client-ready version: tighten wording, remove fluff, add missing assumptions, and provide the next 3 actions.

Avoid / Cautions

Avoid generic output. Push the model for specific steps, labeled assumptions, and rules grounded in the actual workflow — not best-practice clichés.

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