Fetch a Jira issue and propose an implementation plan based on codebase analysis
npx skills add https://github.com/DataDog/datadog-agent --skill run-jira
Fetch the Jira issue $ARGUMENTS from the Datadog Atlassian instance and use it as the basis for a codebase analysis and implementation proposal.
Use the Atlassian MCP tools to fetch the issue. Only request the fields you need to avoid huge responses:
mcp__atlassian__getJiraIssue with:cloudId: datadoghq.atlassian.netissueIdOrKey: $ARGUMENTSfields: ["summary", "description", "status", "assignee", "issuetype", "comment", "priority"]If the issue cannot be found, stop and inform the user.
Present a clear summary of the Jira issue:
Scan the issue description and comments for links to external resources and fetch them for additional context:
app.datadoghq.com/notebook/<id>): use mcp__datadog-mcp__get_datadog_notebook with the notebook IDgithub.com/.../pull/<number>): use gh pr view <number> via BashWebFetch if accessibleThis step is critical — linked resources often contain the root cause analysis, timelines, and technical details that the Jira description alone does not capture.
Based on the issue requirements and linked resources, explore the codebase to understand:
Use Glob, Grep, and Read tools extensively. For broad exploration, use the Task tool with subagent_type=Explore.
Enter plan mode with EnterPlanMode and write a detailed implementation plan that includes:
Wait for user approval before implementing.
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.
Generates creative domain name ideas for your project and checks availability across multiple TLDs (.com, .io, .dev, .ai, etc.). Saves hours of brainstorming and manual checking.
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls.
Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".
Take datadog/run-jira from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.