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Fetch a Jira issue and propose an implementation plan based on codebase analysis
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | run-jira |
| description | Fetch a Jira issue and propose an implementation plan based on codebase analysis |
| argument-hint | <JIRA-KEY e.g. ACTP-1234> |
| model | sonnet |
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.
Generate the Agent Supply Chain newsletter by researching team activity on GitHub and Confluence, then creating a Confluence draft and Gmail draft
Give your AI agents something more useful than a prompt. Velocity through clarity.
Extract an Allium specification from an existing codebase. Use when the user has existing code and wants to distil behaviour into a spec, reverse engineer a specification from implementation, generate a spec from code, turn implementation into a behavioural specification, or document what a codebase does in Allium terms.
Run a structured discovery session to build an Allium specification through conversation. Use when the user wants to create a new spec from scratch, elicit or gather requirements, capture domain behaviour, specify a feature or system, define what a system should do, or is describing functionality and needs help shaping it into a specification.
Generate tests from Allium specifications. Use when the user wants to propagate tests, generate test files from a spec, write tests for a specification, create property-based tests, produce state machine tests, check test coverage against spec obligations, or understand what tests a specification requires.
Autonomously work on Jira backlog tickets, creating PRs and shepherding them to merge