backlog-grooming

SkillProductivity

Use 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.

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:

  1. Read the current backlog and classify items as Now, Next, Later, Icebox, or Won't Do.
  2. Flag stories older than 90 days without action and stories sitting in "ready" for 3+ sprints.
  3. Refine the top 5-8 candidate stories against the Definition of Ready.
  4. 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