The problem

Engineering managers often waste hours manually correlating git commits, Jira tickets, and incident logs to prepare for weekly planning. Without a strict evidence-based approach, these retrospectives often devolve into subjective performance assessments or biased causality claims.

What it does

• Synthesizes git, ticket, and incident exports into a manager-ready weekly decision memo.
• Flags planning drift by identifying completed unplanned work and unaddressed planned items.
• Surfaces explicit friction signals like blocked tickets and open incident records.
• Generates evidence-linked decision prompts for human review rather than automated mandates.
• Proposes a single, reversible one-week experiment based on observed friction patterns.

Frameworks & tools

Works with structured exports (JSON, CSV, or text) from GitHub, GitLab, Jira, Linear, PagerDuty, and similar engineering ops tools.

Why this beats prompting it yourself

This skill enforces a strict read-only evidence contract that prevents the LLM from hallucinating causality or making inappropriate HR assessments. It handles the tedious work of record linkage and status normalization while maintaining a fail-closed gate for incomplete data.

Use cases

• Prepare a factual foundation for weekly engineering syncs or sprint reviews.
• Identify systemic blockers and friction without resorting to individual blame.
• Analyze plan-vs-actual drift using strictly bounded export windows.
• Design low-risk process experiments based on concrete incident and ticket data.

Known limitations

Does not connect to live APIs or repositories. Author names are excluded from analysis. Cannot infer causality or individual performance from incomplete exports.