backlog-grooming
SkillProductivityUse when a task needs a product backlog groomed, refined, or cleaned up — sprint readiness, estimation, and zombie-story cleanup.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the backlog-grooming skill
What this skill tells your AI
The instructions your AI receives, as published by jshsakura/awesome-opencode-skills in skills/backlog-grooming/SKILL.md and read by ahel’s review.
Instructions
Own backlog grooming as throughput hygiene for the next two to three sprints, not a checkbox ceremony.
Prioritize the smallest set of changes that make the top of the backlog sprint-ready and surface zombie stories that should be archived or decided on.
Working mode:
- Read the current backlog and classify items as Now, Next, Later, Icebox, or Won't Do.
- Flag stories older than 90 days without action and stories sitting in "ready" for 3+ sprints.
- Refine the top 5-8 candidate stories against the Definition of Ready.
- Recheck priority order against current strategy and surface items to promote, demote, or archive.
Focus on:
- Definition of Ready: testable acceptance criteria, resolved dependencies, available design, team estimate, fits in one sprint
- estimation discipline: Fibonacci story points or t-shirt sizing, no calendar-time stand-ins
- planning poker hygiene: simultaneous vote, outlier explanation, consensus over averaging
- zombie story detection: stale stories, duplicates, abandoned-priority items, ambiguous owners
- priority drift: do top items still reflect current strategy, not last quarter's
- dependency mapping across the top of the backlog
- backlog category boundaries: avoid Later items leaking into Now without refinement
Quality checks:
- verify every Now item has owner, priority, acceptance criteria, and Definition of Done
- confirm story sizes are realistic (1-8 points, with 13+ broken down)
- check that stale and duplicate items have an explicit archive or decision recommendation
- ensure priority changes are tied to a strategy or signal, not personal preference
- call out items that need product, design, or engineering input before further refinement
Return:
- groomed backlog assessment with counts per category
- top stories that are sprint-ready vs need more work (with what is missing)
- archive recommendations with one-line rationale per item
- priority promotion/demotion recommendations with rationale
- agenda for the next grooming session focused on remaining unresolved items
Do not rewrite acceptance criteria without preserving original intent, archive stories silently without a recommendation, or estimate in calendar days unless explicitly requested by the parent agent.
Signals
- GitHub stars
- 26
- Forks
- 2
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
backlog-grooming-jshsakura- Source
- github.com/jshsakura/awesome-opencode-skills