What format is the output provided in?

The skill returns a curated Markdown table including the specific 'Gotcha', the resulting 'Impact', actionable 'Mitigation' steps, and a link to the 'Source' document.

Can this skill be used outside of creating PRDs?

Absolutely. It is highly effective for manual research when investigating the history of a specific code area or identifying risks associated with a particular library or tool.

Where does the skill look for historical information?

The skill specifically scans the memory/learnings/ directory within your repository for markdown files that follow the flowspec learning format.

How does it determine which learnings are most relevant?

It uses a tiered prioritization strategy: direct file path matches are highest, followed by similar failure modes, shared technology stacks, and finally the recency of the entry.

Historical Learnings & Gotchas

Name: Historical Learnings & Gotchas
Author: jpoley

byjpoley

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Learning & Documentation

Extracts and curates historical lessons learned from past implementations to prevent recurring mistakes in new requirements and specifications.

About

The gather-learnings skill acts as an intelligent memory layer for your development workflow by scanning previous implementation failures and 'gotchas' stored within your repository's memory. By performing pattern matching on file paths, technology keywords, and error categories, it automatically generates actionable insights for Product Requirements Documents (PRDs) and Platform Requirements Packages (PRPs). This ensures that critical knowledge gained from past bugs, CI failures, and architectural missteps is proactively applied to new features, significantly reducing technical debt and regression risks.

Key Features

Seamless integration with the flowspec documentation methodology
Pattern-based matching across file paths, keywords, and tags
5 GitHub stars
Automatic generation of formatted 'Gotchas / Prior Failures' tables
Contextual analysis of repository-specific learning files
Intelligent prioritization based on relevance, severity, and recency

Use Cases

Researching historical context before refactoring legacy code paths
Identifying technology-specific pitfalls during project design phases
Populating risk and prior failure sections in new feature PRDs

About

Key Features

Seamless integration with the flowspec documentation methodology
Pattern-based matching across file paths, keywords, and tags
5 GitHub stars
Automatic generation of formatted 'Gotchas / Prior Failures' tables
Contextual analysis of repository-specific learning files
Intelligent prioritization based on relevance, severity, and recency

Use Cases

Researching historical context before refactoring legacy code paths
Identifying technology-specific pitfalls during project design phases
Populating risk and prior failure sections in new feature PRDs