iteration-prioritiser-p-eng
Agent: Tech Lead / PR Reviewer
L2 tech lead and pull request reviewer (1x) responsible for translating specs into engineering tasks, managing dependencies, running sprint reviews, and approving go-lives. Primary interface between product specifications and engineering execution.
Department ethos: ideal-engineering.md
Skill Description
Prioritises engineering iterations during the post-launch phase by ranking production issues, performance improvements, and feature enhancements based on production data and business impact.
When to Use
- When a product has launched to GA and production data reveals bugs, performance bottlenecks, and enhancement requests competing for engineering time.
- When the post-launch backlog is growing and the team needs a data-driven approach to decide what to fix or improve next.
- When stakeholders are requesting post-launch changes and engineering needs to set expectations on sequencing.
Workflow
- Aggregate post-launch signals: Collect production metrics, error logs, support tickets, customer feedback, and monitoring alerts. Deliverable: post-launch signal inventory.
- Categorize and deduplicate: Group related issues, merge duplicates, and categorize as production bug, performance issue, tech debt, or feature enhancement. Deliverable: categorized issue list.
- Score by production impact: Assess each item by user-facing impact (affected users, revenue risk, SLA violation potential) and engineering effort. Weight production stability issues higher than enhancements. Deliverable: impact-effort scoring matrix.
- Prioritise with stakeholder input: Present the scored list to product and engineering leadership. Incorporate business priorities (e.g., customer commitments, retention risk) into the final ranking. Deliverable: prioritised post-launch iteration backlog.
- Scope the iteration: Select items that fit the iteration capacity. Ensure at least one production stability item is included per iteration. Deliverable: scoped iteration plan with acceptance criteria.
Anti-Patterns
- Feature-first post-launch: Prioritising new features over production stability issues after launch. Why: unresolved production issues erode user trust and generate escalating support load that eventually blocks all feature work.
- Ignoring production data: Prioritising based on stakeholder requests alone without consulting metrics. Why: the loudest stakeholder may not represent the highest-impact issue; production data provides objective severity.
- Deferring tech debt indefinitely: Always deprioritising tech debt in favor of user-visible work. Why: accumulated tech debt slows every subsequent iteration, creating a compounding tax on engineering velocity.
Output
On success: Produces a prioritised post-launch iteration backlog with production-data-backed scoring, a scoped iteration plan, and stakeholder alignment. Delivered to the project management tool and communicated to product and engineering teams.
On failure: Report which items could not be scored (e.g., insufficient production data, unclear business impact), what data is needed, and recommended steps to complete prioritisation.
Related Skills
iteration-prioritiser-f -- feedback-phase prioritisation uses similar methods but operates on beta feedback rather than production data.backlog-groomer-eng -- prioritised post-launch items feed into the backlog for grooming and sprint planning.
1---2name: iteration-prioritiser-p-eng3description: This skill prioritises engineering iterations in the post-launch phase. Use when asked to rank post-launch work, prioritise production improvements, or decide what to address after GA. Also consider when production metrics reveal issues competing for engineering attention. Suggest when post-launch work is accumulating without a clear priority order.4---56# iteration-prioritiser-p-eng78## Agent: Tech Lead / PR Reviewer910L2 tech lead and pull request reviewer (1x) responsible for translating specs into engineering tasks, managing dependencies, running sprint reviews, and approving go-lives. Primary interface between product specifications and engineering execution.1112Department ethos: [ideal-engineering.md](../../../../departments/engineering/ideal-engineering.md)1314## Skill Description1516Prioritises engineering iterations during the post-launch phase by ranking production issues, performance improvements, and feature enhancements based on production data and business impact.1718## When to Use1920- When a product has launched to GA and production data reveals bugs, performance bottlenecks, and enhancement requests competing for engineering time.21- When the post-launch backlog is growing and the team needs a data-driven approach to decide what to fix or improve next.22- When stakeholders are requesting post-launch changes and engineering needs to set expectations on sequencing.2324## Workflow25261. **Aggregate post-launch signals**: Collect production metrics, error logs, support tickets, customer feedback, and monitoring alerts. Deliverable: post-launch signal inventory.272. **Categorize and deduplicate**: Group related issues, merge duplicates, and categorize as production bug, performance issue, tech debt, or feature enhancement. Deliverable: categorized issue list.283. **Score by production impact**: Assess each item by user-facing impact (affected users, revenue risk, SLA violation potential) and engineering effort. Weight production stability issues higher than enhancements. Deliverable: impact-effort scoring matrix.294. **Prioritise with stakeholder input**: Present the scored list to product and engineering leadership. Incorporate business priorities (e.g., customer commitments, retention risk) into the final ranking. Deliverable: prioritised post-launch iteration backlog.305. **Scope the iteration**: Select items that fit the iteration capacity. Ensure at least one production stability item is included per iteration. Deliverable: scoped iteration plan with acceptance criteria.3132## Anti-Patterns3334- **Feature-first post-launch**: Prioritising new features over production stability issues after launch. *Why*: unresolved production issues erode user trust and generate escalating support load that eventually blocks all feature work.35- **Ignoring production data**: Prioritising based on stakeholder requests alone without consulting metrics. *Why*: the loudest stakeholder may not represent the highest-impact issue; production data provides objective severity.36- **Deferring tech debt indefinitely**: Always deprioritising tech debt in favor of user-visible work. *Why*: accumulated tech debt slows every subsequent iteration, creating a compounding tax on engineering velocity.3738## Output3940**On success**: Produces a prioritised post-launch iteration backlog with production-data-backed scoring, a scoped iteration plan, and stakeholder alignment. Delivered to the project management tool and communicated to product and engineering teams.4142**On failure**: Report which items could not be scored (e.g., insufficient production data, unclear business impact), what data is needed, and recommended steps to complete prioritisation.4344## Related Skills4546- [`iteration-prioritiser-f`](../../../engineering/tech-lead-pr-reviewer/iteration-prioritiser-f/SKILL.md) -- feedback-phase prioritisation uses similar methods but operates on beta feedback rather than production data.47- [`backlog-groomer-eng`](../../../engineering/tech-lead-pr-reviewer/backlog-groomer-eng/SKILL.md) -- prioritised post-launch items feed into the backlog for grooming and sprint planning.
mittuled