scrum-conductor_skill

Overview

This skill is a Senior Agile Facilitator and Delivery Architect focused on AI-enhanced Scrum orchestration, automated ticket management, and high-velocity sprint coordination. It uses LLMs and telemetry from Git, PRs, and chat to synthesize daily updates, surface blockers early, and maintain a clean, machine-readable backlog across GitHub Issues, Jira, and Linear. The skill enforces 2026 standards like Fact-First standups, machine-readable Definitions of Done, and predictive capacity forecasting. It is designed to reduce meeting noise, remove manual triage, and increase delivery predictability.

How this skill works

The skill inspects recent commits, pull requests, CI status, and team communications to build a factual baseline before any ceremony. It transforms informal notes and chat snippets into structured tickets with Acceptance Criteria and a machine-readable DoD, then syncs or flags items across issue trackers. Predictive models use historical cycle time, holidays, and context debt to forecast sprint capacity and risk. Automation rules flag stale backlog items, cluster duplicates, and route blockers to owners for fast resolution.

When to use it

• Before daily standups to generate fact-checked summaries and focus discussions on blockers.
• During sprint planning to produce realistic capacity forecasts and machine-readable tickets.
• For backlog grooming to cluster duplicates, flag stale items, and keep the backlog under control.
• When onboarding a new team or project to enforce ticket engineering standards and Definition of Done.
• To automate routine delivery reporting and stakeholder summaries from Git and PR telemetry.

Best practices

• Never ask for status that can be inferred from commit/PR activity—use fact-first summaries.
• Require a machine-readable DoD on every ticket before it enters the sprint backlog.
• Limit standups to 15 minutes; move deep dives to parking-lot sessions with explicit owners.
• Automatically flag tickets older than two sprints for archive or refactor decisions.
• Use predictive forecasts based on historical cycle time and context debt, not gut feel.

Example use cases

• Generate a Daily Conductor Update summarizing merged PRs, in-progress work, and blockers before the standup.
• Convert a Slack thread into a structured FEAT ticket with Acceptance Criteria and implementation pointers.
• Run a pre-planning capacity forecast that accounts for holidays and recent cycle-time trends.
• Auto-flag and cluster duplicate issues across GitHub, Jira, and Linear to reduce backlog noise.
• Produce an executive sprint report aggregating Git activity and risk indicators for stakeholders.