Generate machine-verifiable PRDs with user stories and acceptance criteria.
npx skills add https://github.com/andyzengmath/quantum-loop --skill ql-spec
Please help me install this Agent Skill. Skill: ql-spec Source: https://github.com/andyzengmath/quantum-loop/tree/main/skills/ql-spec Command: npx skills add https://github.com/andyzengmath/quantum-loop --skill ql-spec
Quantum-Loop's ql-spec formalizes informal feature ideas or approved designs into a machine-verifiable Product Requirements Document (PRD) so downstream agents and reviewers can unambiguously implement and verify work. It prevents ambiguous specs, scope creep, and unverifiable acceptance claims by enforcing structured questions, a nine-section PRD, and strict machine-verifiable criteria.
Generate a formal PRD by providing any existing design context and answering the clarifying multiple-choice questions the skill asks.
High-intent search queries and answers about installing and using this skill.
To generate a machine-verifiable PRD from a feature description, provide your design context to the tool. It will ask 5-8 multiple-choice clarifying questions and output a nine-section PRD with explicit user stories, acceptance criteria, and functional requirements saved to tasks/prd-<feature-name>.md.
Machine-verifiable acceptance criteria are explicit, unambiguous test conditions embedded in a PRD. They allow autonomous agents and reviewers to programmatically confirm that a user story or functional requirement has been successfully implemented without subjective interpretation.
Yes, you can convert a one-line feature request into user stories for autonomous agents. The tool enforces 5-8 clarifying multiple-choice questions to resolve ambiguity, then generates user stories specifically sized for single-agent execution within a formal PRD.
Prevent scope creep when creating product requirement documents by enforcing mandatory non-goals and a strict nine-section PRD structure. This formalizes informal ideas into explicit functional requirements with lifecycle handling, preventing ambiguous specs and unrequested features.
No, the PRD generation process does not start implementation automatically. It strictly saves the formalized product requirements document to tasks/prd-<feature-name>.md for downstream planning and review, ensuring no code is written before spec approval.
The best way to structure a PRD for downstream coding agents is using a nine-section format with numbered functional requirements, lifecycle checklists, and machine-verifiable acceptance criteria. This ensures unambiguous implementation and verification by autonomous agents.
chenziyang110Reverse-extract current-state PRD suites from repository evidence.
ruska-aiGenerate a structured Product Requirements Document from a feature description.
quangkmhdGenerate a PRD from conversation context and submit it as a GitHub issue.
deepnoodle-aiGenerate structured PRD documents with goals, scope, and acceptance criteria.
