Developer Tools & AI AgentsCodegeist 2025 Hackathon Product

AI Pit Strategist: F1-Inspired Developer Sprint Command Center

Created for Codegeist 2025: An experimental sprint control dashboard integrating Jira REST API, GitHub GraphQL API, and Gemini 2.5 Flash LLM.

Hackathon Project & Trademark Disclaimer

AI Pit Strategist is an experimental sprint control platform engineered for Atlassian's Codegeist 2025 hackathon. References to Formula 1, Williams Racing, Atlassian, Jira, GitHub, and Gemini belong to their respective owners. No official partnership or endorsement is implied.

Executive Summary

AI Pit Strategist is an AI-powered developer tool engineered for Atlassian's Codegeist 2025 hackathon. Inspired by Formula 1 race engineering telemetry, it unifies Jira sprint metrics, GitHub pull request status, and Gemini LLM intelligence into a single real-time command console.

The Problem & Friction

Engineering managers and team leads spend hours sifting across Jira backlog boards, GitHub PR queues, and Slack updates to assess sprint velocity, uncover technical blockers, and prepare daily standup summaries.

Product & Engineering Strategy

Softora Systems designed a low-latency command center interface with an F1 pit-wall metaphor. The platform fetches live issues from Jira REST APIs and open PRs from GitHub GraphQL endpoints, feeding structured context into Gemini 2.5 Flash to generate instant sprint diagnostics, automated pull request reviews, and intelligent backlog triage.

Core Solution & Implementation

Engineered a high-performance single-page web app built with React, Vite, TypeScript, and Tailwind CSS. Built OAuth 2.0 integration pipelines for Jira and GitHub, backed by LLM prompt guardrails ensuring human oversight.

Architecture Highlights

F1 Race Control UI Metaphor: High-contrast dark telemetry theme designed for rapid monitoring of velocity, PR bottleneck alerts, and team burnout indicators.
Atlassian Jira REST API Integration: Direct REST pipeline fetching active sprint scopes, issue states, story points, and assignee workload distribution.
GitHub GraphQL API Integration: GraphQL query engine pulling open PR statuses, review requests, build status checks, and commit timelines.
Gemini 2.5 Flash LLM Diagnostics: Context-stuffed prompt pipeline generating automated standup summaries, sprint retrospectives, and task risk scoring under 1.5 seconds.
Human-in-the-Loop Safeguards: Explicit AI boundary rules ensuring all ticket assignments, prioritization updates, and PR recommendations require explicit human lead approval before execution.

Technical Challenges & Solutions

Challenge: Handling complex GraphQL schema structures from GitHub alongside REST payloads from Jira without UI lag.

Engineering Solution: Built normalized TypeScript data transformer layers that unify disparate API payloads into a unified sprint telemetry state.

Challenge: Preventing hallucinated or unsafe automated actions on development backlogs.

Engineering Solution: Strictly enforced a read-only telemetry model for AI analysis, requiring explicit human approval for any state-modifying actions.

Verified Outcomes

Shipped production-ready experimental command center for Codegeist 2025 Atlassian hackathon.

Achieved sub-1.5s AI analysis latency for sprint velocity summaries and PR bottleneck detection.

Demonstrated multi-platform integration combining Jira, GitHub, and Gemini APIs into a cohesive developer tool.

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Architecture Highlights

F1 Race Control UI Metaphor: High-contrast dark telemetry theme designed for rapid monitoring of velocity, PR bottleneck alerts, and team burnout indicators.

Atlassian Jira REST API Integration: Direct REST pipeline fetching active sprint scopes, issue states, story points, and assignee workload distribution.

GitHub GraphQL API Integration: GraphQL query engine pulling open PR statuses, review requests, build status checks, and commit timelines.

Gemini 2.5 Flash LLM Diagnostics: Context-stuffed prompt pipeline generating automated standup summaries, sprint retrospectives, and task risk scoring under 1.5 seconds.

Human-in-the-Loop Safeguards: Explicit AI boundary rules ensuring all ticket assignments, prioritization updates, and PR recommendations require explicit human lead approval before execution.

Technical Challenges & Solutions

Challenge: Handling complex GraphQL schema structures from GitHub alongside REST payloads from Jira without UI lag.

Engineering Solution: Built normalized TypeScript data transformer layers that unify disparate API payloads into a unified sprint telemetry state.

Challenge: Preventing hallucinated or unsafe automated actions on development backlogs.

Engineering Solution: Strictly enforced a read-only telemetry model for AI analysis, requiring explicit human approval for any state-modifying actions.