Agent SOP Builder

Turn vague tasks into verifiable steps that save tokens and improve reliability

Problem

Developers are frustrated that long AI agent tasks burn huge amounts of tokens because prompts are vague and lack acceptance criteria. A V2EX post summarizes a 4-step method to cut token burn, centered on 'write acceptance criteria first' and 'split into checkable steps'; another thread suggests negative skill files (NEGATIVESKILLS) to stop agents from repeating mistakes.

Solution

An editor plugin or web tool where users enter a vague task goal, and it automatically generates a structured acceptance checklist and decomposition steps, plus common anti-pattern reminders, exportable as an agent-readable SOP file.

Users

Developers, technical writers, and AI product teams who frequently use AI agents for complex tasks

MVP

Provide a task description input; generate acceptance criteria and step-by-step plans using rules and a lightweight LLM; include a built-in library of common anti-patterns; export SOP as Markdown or JSON; allow saving and reusing templates

Revenue

Free basic templates; paid tier at $9–19/month adds custom anti-pattern libraries, team sharing, and integrations with Claude Code/Cursor

Why now

Long agent tasks are becoming mainstream, but most users don't know how to structure them; big labs focus on model capability, not task-planning support layers