Can I filter the backlog by specific epics or components?

Yes, you can provide project keys, epic links, components, or labels to refine the JQL query used for the analysis.

How many tickets can Claude analyze at one time?

The skill is optimized to analyze up to 25 tickets at once to ensure the AI provides deep, actionable insights without losing context.

Does this skill require the Atlassian CLI installed?

No, the skill prefers the Atlassian MCP Server for direct API integration, though it can use the Atlassian CLI (acli) as a fallback.

Can this skill create or edit Jira tickets?

No, this skill is focused on read-only analysis and summarization. For ticket modification, you should use specialized skills like jira-create or jira-update.

Does this skill support story point analysis?

Yes, it extracts story points where available to calculate effort distribution and suggest balanced sprint scopes.

Jira Backlog & Sprint Planner

Name: Jira Backlog & Sprint Planner
Author: bcowdery

bybcowdery

Collaboration Tools

Generates AI-powered backlog summaries and sprint planning recommendations using Jira ticket data.

About

The Jira Backlog Summary skill empowers Claude to act as a technical product manager by fetching and analyzing issues from your Jira project. By integrating with the Atlassian MCP Server or Atlassian CLI, it identifies work themes, assesses complexity distribution, and flags potential risks or blockers. It is specifically designed to streamline sprint planning by providing structured recommendations on ticket groupings and scope, helping teams transition from a raw backlog to an actionable plan in seconds.

Key Features

AI-powered identification of ticket themes and functional patterns
Detailed complexity analysis and story point distribution
Support for Atlassian MCP Server and Jira CLI fallback
0 GitHub stars
Automated sprint scope recommendations based on project priorities
Customizable JQL filtering for epics, components, and labels

Use Cases

Analyzing high-priority backlog items to identify technical dependencies
Preparing structured summaries for upcoming sprint planning sessions
Generating executive summaries of project progress and upcoming work focus

About

Key Features

AI-powered identification of ticket themes and functional patterns
Detailed complexity analysis and story point distribution
Support for Atlassian MCP Server and Jira CLI fallback
0 GitHub stars
Automated sprint scope recommendations based on project priorities
Customizable JQL filtering for epics, components, and labels

Use Cases

Analyzing high-priority backlog items to identify technical dependencies
Preparing structured summaries for upcoming sprint planning sessions
Generating executive summaries of project progress and upcoming work focus