What does the /discover command actually do?

It automates the creation of a feature request document and a state tracking file, then immediately starts a discovery agent to help you refine the feature's details.

What information does the discovery agent provide?

The agent identifies target users, clarifies the core problem, assesses scope boundaries, and produces a comprehensive discovery brief for the feature.

Does this skill work with existing pipeline structures?

Yes, it is designed to integrate seamlessly with the Datum Cloud pipeline structure, reading and writing to specific directories to maintain project state.

How are feature IDs generated?

The skill scans your project's .claude/pipeline/requests/ directory and automatically generates the next sequential ID in the 'feat-NNN' format.

Datum Cloud Feature Discovery

Name: Datum Cloud Feature Discovery
Author: datum-cloud

bydatum-cloud

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Productivity & Workflow

Streamlines product discovery by automating the creation of feature request artifacts and triggering specialized discovery agents.

Datum Cloud Feature Discovery is designed to bridge the gap between initial ideation and technical planning within Claude Code. By using the /discover command, developers can bypass manual boilerplate and instantly generate standardized markdown requests and pipeline state files. The skill automatically assigns sequential feature IDs, manages directory structure, and hands off the context to a product-discovery agent that helps define target users, assess scope, and evaluate platform requirements, ensuring a structured and efficient start to the development lifecycle.

Key Features

01Standardized markdown artifact creation with metadata handoffs

02Automated pipeline state initialization via JSON

03Built-in templates for clarifying requirements and assumptions

04Automated sequential feature ID generation (feat-NNN)

05Instant invocation of specialized product-discovery agents

062 GitHub stars

Use Cases

01Rapidly capturing and formalizing new feature ideas during development

02Standardizing the requirement gathering process for engineering teams

03Automating the transition from raw feature requests to structured discovery briefs

Key Features

01Standardized markdown artifact creation with metadata handoffs

02Automated pipeline state initialization via JSON

03Built-in templates for clarifying requirements and assumptions

04Automated sequential feature ID generation (feat-NNN)

05Instant invocation of specialized product-discovery agents

062 GitHub stars

Use Cases

01Rapidly capturing and formalizing new feature ideas during development

02Standardizing the requirement gathering process for engineering teams

03Automating the transition from raw feature requests to structured discovery briefs