run-jira

SkillProductivity

Lets your agent fetch a Jira issue and draft an implementation plan based on your codebase.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the run-jira skill

About this capability

Fetch a Jira issue and propose an implementation plan based on codebase analysis

What this skill tells your AI

The instructions your AI receives, as published by datadog/datadog-agent in .agents/skills/run-jira/SKILL.md and read by ahel’s review.

Fetch the Jira issue $ARGUMENTS from the Datadog Atlassian instance and use it as the basis for a codebase analysis and implementation proposal.

Step 1: Gather Jira issue data

Use the Atlassian MCP tools to fetch the issue. Only request the fields you need to avoid huge responses:

  1. Call mcp__atlassian__getJiraIssue with:
    • cloudId: datadoghq.atlassian.net
    • issueIdOrKey: $ARGUMENTS
    • fields: ["summary", "description", "status", "assignee", "issuetype", "comment", "priority"]
  2. Extract the title (summary), description, status, assignee, and comments from the response.

If the issue cannot be found, stop and inform the user.

Step 2: Summarize the issue

Present a clear summary of the Jira issue:

Step 3: Fetch linked resources

Scan the issue description and comments for links to external resources and fetch them for additional context:

  • Datadog notebooks / postmortems (app.datadoghq.com/notebook/<id>): use mcp__datadog-mcp__get_datadog_notebook with the notebook ID
  • GitHub PRs (github.com/.../pull/<number>): use gh pr view <number> via Bash
  • GitLab commits/pipelines or other URLs: use WebFetch if accessible

This step is critical — linked resources often contain the root cause analysis, timelines, and technical details that the Jira description alone does not capture.

Step 4: Analyze the codebase

Based on the issue requirements and linked resources, explore the codebase to understand:

  • Which files and packages are relevant
  • Existing patterns and conventions that should be followed
  • Dependencies and potential impacts

Use Glob, Grep, and Read tools extensively. For broad exploration, use the Task tool with subagent_type=Explore.

Step 5: Propose an implementation

Enter plan mode with EnterPlanMode and write a detailed implementation plan that includes:

  • A breakdown of the changes needed, organized by file
  • Any new files that need to be created
  • Test strategy
  • Potential risks or open questions

Wait for user approval before implementing.

Signals

GitHub stars
4k
Forks
1k
Last commit
Sep 2026

ahel review

  • S4info
    community integration — published by datadog, not jira

Automated review, not a security audit. Ruleset v1.

Advanced
Catalog kind
skill
Gateway key
run-jira
Source
github.com/datadog/datadog-agent