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story-retrospective

Mine learning data from logs, git commits, and validation reports into a structured retrospective summary.

2|Updated Dec 21, 2025
Search Tags:#software development#learning#retrospective#story#agile#data mining
One-click install
npx skills add https://github.com/telum-ai/speck --skill story-retrospective
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: story-retrospective
Source: https://github.com/telum-ai/speck/tree/main/.cursor/skills/story-retrospective
Command: npx skills add https://github.com/telum-ai/speck --skill story-retrospective

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of gathering and structuring learnings from a completed software development story, making retrospectives more efficient and insightful.

Core Features & Use Cases

  • Automated Data Mining: Gathers data from .learning.log, git commits, and validation reports.
  • Structured Summary Generation: Creates a story-retro.md file with key learnings, patterns, and gotchas.
  • Immediate Learning Application: Proposes updates to future story specifications within the same epic based on discovered patterns and gotchas.
  • Use Case: After completing a user authentication story, this Skill mines commit messages for performance optimizations and log files for unexpected issues, then generates a summary that highlights a new security pattern to be applied to the next story in the epic.

Quick Start

Run the story-retrospective skill to mine data and generate a retrospective summary for the current story.

Frequently Asked Questions about story-retrospective

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

FAQPage Schema
How do I generate an agile retrospective summary from git commits and learning logs?▼

Agile retrospective summaries are generated by mining raw data from .learning.log files, git commits with learning tags, and validation reports to produce a structured document highlighting key learnings, patterns, and gotchas.

How do I extract software development learnings for epic retrospectives?▼

Software development learnings are extracted by analyzing story implementation data to create a structured story-retro.md file, which applies immediate learnings to current epic stories and flags patterns for retrospective validation.

Can I automatically apply discovered development patterns to future stories in the same epic?▼

Yes, discovered development patterns can be automatically applied to future stories by proposing updates to upcoming story specifications within the same epic based on gotchas and learnings mined from completed work.

What data sources do I need for automating story retrospective data mining?▼

Automating story retrospective data mining requires .learning.log files, git commits containing learning tags, and validation reports generated during software development story implementation.

What is the best way to structure story implementation learnings for a retrospective?▼

Structuring story implementation learnings is best achieved by aggregating data into a story-retro.md file, ensuring key patterns and gotchas are formatted for retrospective validation and future epic story updates.

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