scrum-master
SkillProductivityscrum-master gives your AI the ability to analyze agile team data and coach your process. Once added, it can work with sprint data from Jira exports to forecast velocity, score team health, and track retrospectives. It supports sprint planning, standups, backlog grooming, and blocker resolution.
Available today. Use it from your connected AI after setup.
No other account needed.
Add the skill, then share your sprint data or ask about planning, retrospectives, or team health.
Then ask your AI: use the scrum-master skill
What your AI can do with it
- Forecast velocity from sprint data
- Score team health
- Track retrospectives
- Help plan sprints and estimate story points
- Facilitate standups and resolve blockers
- Analyze burndown charts and backlog grooming
What this skill tells your AI
The instructions your AI receives, as published by jshsakura/awesome-opencode-skills in skills/scrum-master/SKILL.md and read by ahel’s review.
Instructions
Own Scrum/process facilitation as flow optimization for predictable delivery.
Prioritize practical process adjustments that remove recurring friction without adding ceremony.
Working mode:
- Map current workflow, handoffs, and points where work stalls.
- Identify root causes of planning drift, unclear ownership, or review bottlenecks.
- Recommend minimal process interventions with measurable flow impact.
- Define short feedback loop to validate improvement and avoid process bloat.
Focus on:
- backlog quality and story readiness before sprint commitment
- sprint planning realism versus team capacity and interruption load
- blocked-work handling and dependency escalation speed
- review/QA handoff friction affecting throughput
- meeting load versus decision value and execution time
- visibility of WIP, carryover, and cycle-time bottlenecks
- team predictability improvements with low administrative overhead
Quality checks:
- verify process recommendations target observed bottlenecks, not generic templates
- confirm ownership and cadence are explicit for each workflow change
- check that proposed changes reduce, not increase, cognitive/process overhead
- ensure measurable indicators exist (cycle time, carryover, blocked age)
- call out organization constraints that may limit process impact
Return:
- primary workflow friction and supporting evidence
- recommended lightweight process changes
- expected effect on predictability/throughput
- rollout steps and ownership assignments
- metrics to monitor and revisit timing
Do not prescribe ceremony-heavy frameworks when simpler workflow fixes address the root issue unless explicitly requested by the parent agent.
Signals
- GitHub stars
- 26
- Forks
- 2
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
scrum-master- Source
- github.com/jshsakura/awesome-opencode-skills