Engineering enablement · AI/LLM · Human review
Reusable AI workflows for software delivery.
I built reusable Claude Code skills and project plugins that connected engineering work to Jira. Tests, pull-request builds and code review remained required.
- Role
- AI workflow design, plugin development, adoption and review standards
- Technology
- Claude Code · Atlassian MCP · Jira · Project plugins · CI
01 / Background
Context
Engineers repeatedly spent time supplying project architecture, domain knowledge, coding conventions and delivery workflows before an AI assistant could produce useful work. Creating tickets also meant manually translating implementation details into a consistent Jira structure.
02 / Constraints
Constraint
The tooling needed explicit project knowledge, installable team workflows and the same engineering checks used for manually produced changes. Speed could not come at the expense of accuracy or review.
03 / Change
Before / after
Project context and ticket structure had to be rebuilt repeatedly, slowing first passes and increasing the chance of context-related implementation mistakes.
Reusable skills and project plugins supplied project-specific context, created structured tickets directly in Jira and produced work for engineers to review.
04 / Architecture
System design
- 01
Encoded project context
Architecture, domain knowledge, conventions and delivery workflows
- 02
Reusable AI tooling
Claude Code ticket skill, repository plugins and Atlassian MCP
- 03
Engineering verification
Unit tests, pull-request builds and human code review
05 / Decisions
Technical decisions
- 01
Include project context
Package project architecture, domain language, coding conventions and development workflows so useful constraints travelled with the tool.
- 02
Connect tools to the delivery workflow
Generate structured development tickets and create them directly in Jira through Atlassian MCP instead of stopping at disconnected draft text.
- 03
Keep the existing review process
Validate AI-assisted changes through unit tests, pull-request builds and human code review before treating generated work as complete.
06 / Leadership
My contribution
I developed Claude Code plugins for two production codebases, documented how other developers could install them and kept engineers responsible for reviewing the resulting work.
07 / Results
Outcome
The reusable tooling improved AI-assisted development speed, reduced context-related implementation mistakes and returned more engineering time to architecture, edge cases and review across two production codebases.