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da-retro

Analyze retrospective notes to identify themes, action items, and sentiment.

Updated Feb 21, 2026
Search Tags:#retrospective#data analytics#sentiment analysis#action items#team improvement#sprint review
One-click install
npx skills add https://github.com/hilaryosborne/skills --skill da-retro
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: da-retro
Source: https://github.com/hilaryosborne/skills/tree/main/data-analytics/da-retro
Command: npx skills add https://github.com/hilaryosborne/skills --skill da-retro

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps in analyzing retrospective notes from data analytics teams to identify recurring themes, action items, and areas for improvement.

Core Features & Use Cases

  • Theme Identification: Automatically categorizes retrospective comments into predefined themes (e.g., technical debt, communication, tooling).
  • Action Item Extraction: Identifies and lists concrete action items suggested during the retrospective.
  • Sentiment Analysis: Provides a general sentiment score for the retrospective discussion.
  • Use Case: A data lead can use this skill after a sprint retrospective to quickly summarize key discussion points and track action items for the next sprint.

Quick Start

Analyze the provided retrospective notes for common themes and action items.

Frequently Asked Questions about da-retro

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

FAQPage Schema
How do I analyze retrospective notes to identify recurring themes and action items?▼

Analyzing retrospective notes involves using natural language processing to categorize unstructured comments into predefined themes like technical debt or communication, while extracting concrete action items and gauging overall sentiment. This provides a summarized review for team improvement.

What is the best way to extract action items from sprint review notes?▼

Extracting action items from sprint review notes requires natural language processing to identify actionable insights from unstructured text. This process highlights concrete tasks suggested during the retrospective, allowing data leads to track improvements for the next sprint.

Can I use sentiment analysis on post-project review notes for a data science team?▼

Sentiment analysis can be applied to post-project review notes to gauge the general sentiment score of the retrospective discussion. This helps identify areas for improvement and team morale within data science and analytics workflows.

How does natural language processing categorize retrospective comments into themes?▼

Natural language processing categorizes retrospective comments by analyzing unstructured text and grouping feedback into predefined themes such as tooling, communication, or technical debt. This automatically identifies recurring discussion points for data analytics teams.

Does this retrospective analysis work for both sprint retrospectives and post-project reviews?▼

Retrospective analysis applies to both sprint retrospectives and post-project reviews within data science and analytics workflows. It processes unstructured text from either format to extract actionable insights and summarize key discussion points.

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