How AI Product Managers Write Skill Requirement Documents: Templates and Best Practices

When a business use case is well-suited for implementation as a Skill, the core responsibility of an AI product manager is to translate vague business requirements into a clearly structured Skill requirement document that developers can act on directly. Official platforms provide standardized documentation formats for developers to follow during implementation. The requirement document that a product manager produces is the critical bridge connecting business needs and technical execution.

This article, based on a sharing by a Bilibili content creator, outlines the standard template for a Skill requirement document along with three efficient writing approaches.

Why Skill Requirement Documents Matter

The typical Skill development workflow goes like this: the product manager translates business logic into a concrete Skill requirement document and hands it off to developers, who then complete the implementation following the platform's official standards. Whether the document is clear and whether it covers all the key details directly determines how efficiently developers can execute — and whether the Skill, once published to the marketplace, can be correctly understood and invoked.

In other words, the requirement document is the key vehicle for translating business language into technically executable specifications. Write it vaguely, and developers will need constant back-and-forth clarification. Write it rigorously, and development can be completed in one clean pass.

The "Skill" referred to here is the skill module found in AI Agent platforms (such as Coze, Dify, etc.) — a reusable functional unit that encapsulates specific business logic. A single Skill typically does just one thing, such as "query order status" or "generate a weekly report summary." Multiple Skills combined together form a complete AI Agent workflow. The Skill marketplace is a shared ecosystem provided by the platform where developers can publish their Skills for other teams or users to invoke directly — similar to the logic of an app store. Because a Skill may be called by different users across different scenarios, the accuracy of its name and description in the requirement document directly affects whether it can be correctly discovered and used.

The Five Core Elements of a Skill Requirement Document

A complete Skill requirement document typically includes the following core sections.

Naming

When naming a Skill, first check whether your organization already has an internal naming convention — if so, follow it. If not, define a name that clearly communicates the Skill's purpose. The core principle is: when this Skill is listed on the Skill marketplace, anyone who sees the name should immediately understand what it does.

Overview

The overview section should briefly describe what specific task this Skill is designed to accomplish, what outcome it aims to achieve, and a general sense of how it works. It serves as an executive summary of the entire document, giving readers a quick grasp of the Skill's positioning.

Implementation Logic

Implementation logic is the most critical part of the entire document. It should break down the execution steps clearly around the business requirement — what happens in step one, step two, step three, and so on.

If the process involves data retrieval or requires invoking MCP (Model Context Protocol), the specific MCPs being used should be explicitly listed at the very first step. Any data definitions involved along the way should also be clearly specified. This section requires the product manager to conduct thorough research after gathering requirements — it's the part that demands the most expertise.

MCP (Model Context Protocol) is an open protocol proposed by Anthropic in late 2024, designed to standardize how AI models interact with external tools and data sources. Think of it as the "USB port" of the AI world — whether it's a database query, an API call, or a file read/write operation, as long as a tool implements the MCP specification, an AI can invoke it in a unified way without needing to write custom adapter logic for each tool. Explicitly listing the required MCPs in the implementation logic is equivalent to declaring upfront which "external capability plugins" this Skill depends on, so developers can correctly configure dependencies during the development phase and ensure the Skill can successfully access the data or perform the external operations it needs at runtime.

Output

The final step is specifying the expected output — what the Skill is ultimately supposed to produce. It's best to include concrete examples here, giving developers an intuitive reference for what the end result should look like.

Error Handling

The closing section of the template covers error handling. Any core risks, data anomaly scenarios, or edge cases should be clearly described here — what to do when these exceptions occur and how to handle them appropriately. This section is easy to overlook, yet it directly affects the stability of the Skill once it goes live.

Comparing Three Efficient Writing Approaches

Now that we know what the template contains, the next question is: how do you write the document efficiently? The content creator summarized three approaches and shared their own preference.

Approach 1: Let AI Generate Everything

The first approach is to hand your requirement description directly to an AI and have it produce content in the standard format. The content creator personally doesn't recommend this. The reason is that general-purpose AI models simply don't know your company's internal resources, business logic, or data specifics. The Skill requirement document it produces may look logically coherent and plausible on the surface, but when you dig into the details, much of it tends to be inaccurate.

Approach 2: Write a Draft by Hand, Then Polish with AI (Recommended)

The second approach is to first write out the Skill requirement document yourself following the template structure, then hand it to AI for polishing or correction. This approach consistently produces the best results. Since the core business logic and data are controlled by someone who understands the business, AI only handles expression and detail refinement — the two strengths complement each other.

Approach 3: Write Everything by Hand

The third approach is to write the entire document manually. Quality-wise this is fine, but purely manual writing occasionally leads to overlooked details. The remedy is to pass the handwritten draft to AI and ask for revision suggestions, then manually incorporate any reasonable feedback.

The fundamental difference between these three approaches lies in the ratio of business knowledge to AI-generated content. Business logic must be driven by humans; AI is better suited to play a supporting role in catching gaps and refining expression.

Summary

The value of a Skill requirement document lies in precisely translating business requirements into a standardized, developer-executable specification. The template consists of five core elements: naming, overview, implementation logic, output, and error handling. Among these, implementation logic — particularly the breakdown of execution steps and MCP declarations — is the most critical and research-intensive part. When it comes to writing method, "write a draft by hand, then polish with AI" is considered the optimal balance of quality and efficiency.

This article focuses on templates and methodology. For how to apply these in practice with real-world examples, stay tuned for follow-up case breakdowns.