scrum-master-agent_skill
- Python
388
GitHub Stars
15
Bundled Files
2 months ago
Catalog Refreshed
4 months ago
First Indexed
Readme & install
Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.
Installation
Preview and clipboard use veilstrat where the catalogue uses aiagentskills.
npx veilstrat add skill alirezarezvani/claude-code-skill-factory --skill scrum-master-agent- calculate_metrics.py17.2 KB
- config.example.yaml5.4 KB
- detect_context.py8.2 KB
- expected_output.json2.2 KB
- format_output.py16.2 KB
- HOW_TO_USE.md9.3 KB
- notify_channels.py12.4 KB
- parse_input.py9.3 KB
- prioritize_backlog.py12.2 KB
- README.md14.3 KB
- sample_input_csv.csv547 B
- sample_input_jira.json1.5 KB
- sample_input_linear.json2.1 KB
- SKILL.md12.9 KB
- tool_adapters.py12.2 KB
Overview
This skill is a production-ready Scrum Master assistant that automates sprint planning, backlog grooming, retrospectives, capacity planning, and daily standups. It delivers context-aware, token-efficient reports and alerts tailored for engineering teams at SaaS startups. Outputs adapt to environment (CLI, Claude AI) and integrate with common tools via exports.
How this skill works
Provide structured sprint data (JSON/CSV/YAML or tool-specific export) and the agent parses team capacity, story points, and statuses. It calculates velocity, burndown, capacity, priority scores, and health metrics, then formats concise summaries first with progressive disclosure for details. Optional webhook integrations send token-efficient Slack or Teams notifications; detailed metrics are computed lazily to save tokens.
When to use it
- Daily automated standups to get a 50–100 token morning summary
- Sprint planning sessions to allocate capacity and prioritize the top 80% of work
- Mid-sprint health checks (day 5–7) to detect scope creep or blockers
- Retrospectives within 24 hours of sprint end to extract action items and sentiment
- Capacity planning when team PTO, holidays, or meeting loads change
Best practices
- Provide clean, normalized exports: ISO dates, numeric story points, and standard statuses
- Start with daily standups and add planning/retrospective features gradually
- Use priority scoring to allocate capacity but validate with team judgment
- Run mid-sprint health score and act on P0 alerts immediately
- Enable notifications only when team channels are configured; keep messages concise (top 3 risks)
Example use cases
- Generate an ultra-lightweight daily standup from a Linear export for Sprint 45
- Plan Sprint 46 using CSV backlog and 80-point team capacity, with prioritized recommendations
- Compare velocity across three sprints using mixed Linear and Jira exports
- Produce retrospective report highlighting blockers and action items from GitHub Projects export
- Calculate team capacity accounting for PTO and meeting overhead for the next sprint
FAQ
JSON, CSV, YAML and tool-specific exports from Linear, Jira, GitHub Projects, and Azure DevOps (exports only).
Does the skill modify my tooling or send updates automatically?
No. It reads exports and generates reports. Notifications via Slack/Teams are optional and send only when configured.
How accurate are predictions and priority scores?
Velocity and burndown use simple statistical forecasts and linear assumptions; priority scoring is heuristic-based and should be validated by the team.
