From Vague Idea to Testable Spec: Mini-Program Product Spec Skill

Where this fits

You’re building an agent that helps a client turn a one-line request like “a mini-program for booking gym slots” into something developers can actually build. Without structure, the agent either invents features to fill gaps or produces a vague list of screens. This skill steps in to convert that raw input into an evidence-calibrated product specification—complete with target users, core problem, version-one scope, main and exception flows, state matrices, and testable acceptance criteria. It’s the missing bridge between a messy idea and a stable handoff before architecture or implementation begins.

Why agents benefit

• Separates facts from assumptions and unknowns – The agent explicitly tags each piece of information as confirmed, assumed, or unknown, so nothing is silently invented.
• Produces testable acceptance criteria – Every flow and state gets a concrete “when X, then Y” condition that can be verified during QA.
• Defines state matrices without guessing – Empty, loading, failure, permission-denied states are described based on the product logic, not on what would look nice.
• Enforces disciplined scope – The skill forces a clear split between “in version one,” “not in version one,” and “future candidates,” preventing scope creep.
• Creates a stable handoff artifact – The output is a structured spec that architects and developers can consume directly, without requiring the agent to make code-level decisions.

Practical scenarios

MVP definition from a chat message

A user tells your agent: “We need a mini-program for neighborhood grocery delivery, but only for registered users and with cash on delivery.” The skill extracts the core loop, identifies missing decisions (e.g., delivery fees, time slots), and produces a spec with acceptance criteria for the order flow.

Clarifying a partially documented prototype

Your agent receives a screenshot plus a few bullet points about a loyalty points mini-program. The skill detects ambiguous states (e.g., what happens when points expire) and marks them as decision points instead of inventing a rule.

Preparing a handoff before architecture review

An agent is asked to draft a product spec for a team about to start backend design. The skill ensures the spec only defines user-visible behavior and explicitly avoids API shapes or database schemas, so the architecture team can start with a clean slate.

Add it to your agent workflow

Install the skill with:

npx skills add NocodeMrLi/mini-program-engineering-skill-suite --skill mini-program-product-spec-skill

Then trigger it in an agent prompt like:

Use the mini-program-product-spec-skill to convert this idea into a product spec: "A mini-program for booking shared bikes, with a deposit system."

The agent will output a structured spec following the skill’s process: facts, assumptions, flows, states, and acceptance criteria.

Compare before adopting

Before making this a permanent part of your agent’s toolkit, compare it with generic product-spec templates or other agent skills. Look at how well it enforces evidence-based decisions—does it refuse to invent logic when data is missing? Check maintenance: the repo has 22 stars and a single author, so review commit recency and issues. Also assess workflow fit: this skill is mini-program specific. If your agents mostly work on web apps, a general spec skill might be more adaptable.

Why it is worth tracking

The skill stands out because it explicitly separates facts, user decisions, assumptions, unknowns, and future ideas—a discipline that most product-spec generators lack. It also produces testable acceptance criteria, which directly improves agent-generated specs for downstream QA. While the community momentum is modest (22 stars), the methodology is sound and the author is actively maintaining the suite. It’s worth evaluating whenever you need to standardize how your agents turn vague product requests into rigorous, handoff-ready specifications. For a deeper look, check the skill page at OpenAgentSkill.