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sprint-executor

Automate end-to-end sprint execution with ticket delegation, code review, and tracker updates.

Updated Aug 27, 2026
Search Tags:#code review#conventional commits#parallel execution#sprint execution#tech lead#tracker integration#ticket delegation
One-click install
npx skills add https://github.com/OntoLedgy/ol_ai_context_library --skill sprint-executor-ontoledgy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sprint-executor
Source: https://github.com/OntoLedgy/ol_ai_context_library/tree/main/skills/sprint-executor
Command: npx skills add https://github.com/OntoLedgy/ol_ai_context_library --skill sprint-executor-ontoledgy

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manually orchestrating sprint execution as a tech lead—delegating tickets, reviewing code, running quality checks, updating tracker states, and posting implementation logs—is repetitive, error-prone, and scales poorly for larger or parallel sprints.

Core Features & Use Cases

  • Dual Execution Modes: Supports both serial (one ticket at a time) and parallel (concurrent worktree-based execution per wave) sprint workflows, adapting to sprint size and team needs.
  • End-to-End Ticket Orchestration: Automates delegation to pre-assigned engineer skills or Codex, code review, dependency vulnerability gating, conventional commits, tracker state updates, and implementation logging for every ticket.
  • Tracker-Agnostic Integration: Works with Jira, Linear, Azure DevOps, or local filesystem trackers out of the box, no custom adapter code required.
  • Use Case: For a planned 2-week sprint with 12 tickets assigned to different engineers, this skill can execute the entire sprint, run parallel waves via isolated git worktrees, resolve merge conflicts, and update all tracker tickets automatically.

Quick Start

Use the sprint-executor skill to execute your approved sprint plan in serial mode, automatically delegating each ticket to its assigned engineer, running reviews and commits, and updating your Jira tracker with implementation logs.

Frequently Asked Questions about sprint-executor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate sprint execution and ticket delegation for my engineering team?▼

Automating sprint execution is achieved by orchestrating ticket delegation, code reviews, conventional commits, and tracker updates across your engineering team. This process eliminates manual coordination by automatically delegating tickets to assigned engineer skills or Codex.

Can I run parallel sprint execution using isolated git worktrees for concurrent ticket waves?▼

Parallel sprint execution is supported using isolated git worktrees to process dependency-ordered ticket waves concurrently. This dual execution mode allows you to run multiple tickets simultaneously while managing serial single-ticket execution workflows.

Does automated sprint execution work with Jira, Linear, and Azure DevOps trackers?▼

Automated sprint execution works directly with Jira, Linear, Azure DevOps, and local filesystem trackers out of the box. This tracker-agnostic integration automatically updates tracker states and posts implementation logs without requiring custom adapter code.

What is the best way to enforce conventional commits and code reviews during automated sprint execution?▼

Enforcing conventional commits and code reviews during sprint execution is handled natively by the orchestration workflow. The process automatically runs clean-code reviews, applies conventional commit standards, and executes dependency vulnerability gates for every delegated ticket.

How do I delegate planned sprint tickets to both Claude engineer skills and Codex via MCP?▼

Delegating planned sprint tickets to Claude-native engineer skills and Codex via MCP is fully supported by the execution workflow. The orchestrator manages end-to-end ticket delegation, automatically routing work to the assigned engineer skill or Codex environment.

When should I use serial versus parallel execution modes for time-boxed sprints?▼

Serial execution mode is used for processing one ticket at a time, while parallel execution mode is applied for concurrent worktree-based execution per wave. You should choose parallel mode for larger sprints with dependency-ordered ticket waves and serial mode for smaller, sequential tasks.

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