sr-why

Search explanation records from specrails agents by keyword or tag.

9|2|Updated Mar 12, 2026
One-click install
npx skills add https://github.com/fjpulidop/specrails-core --skill sr-why
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: sr-why
Source: https://github.com/fjpulidop/specrails-core/tree/main/templates/skills/sr-why
Command: npx skills add https://github.com/fjpulidop/specrails-core --skill sr-why

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Quickly locate and review explanation records produced by specrails agents during the OpenSpec implementation pipeline, reducing time spent hunting for rationales and decisions.

Core Features & Use Cases

  • Search and filter explanation records by date, agent, feature, and tags to surface relevant decisions.
  • Index and recall past decisions to help new team members understand design rationale and avoid rework.
  • Use Case: when validating a feature, quickly review why a particular approach was chosen and how it aligns with previous decisions.

Quick Start

Run /specrails:why to list the 20 most recent explanation records, or /specrails:why <query> to search by keyword or tag.

Frequently Asked Questions about sr-why

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

FAQPage Schema
How do I search AI agent decision history records in OpenSpec?

You can search OpenSpec decision history by running a keyword or tag query to instantly filter explanation records produced by sr-architect, sr-developer, and sr-reviewer agents.

What are OpenSpec explanation records and when do I need to review them?

OpenSpec explanation records are markdown files documenting design rationale and decisions made by AI agents. You need to review them when validating features or understanding why a specific approach was chosen.

How do I retrieve past AI agent design rationale from the implementation pipeline?

Retrieve past design rationale by listing the 20 most recent records or filtering by date, agent, feature, and tags using frontmatter metadata stored in the agent-memory explanations directory.

Can I filter explanation records by specific AI agents and feature tags?

Yes, you can filter explanation records by agent name, feature, date, and tags. The skill reads frontmatter fields from markdown files to score and present matching decision records.

What is the best way to index decision history for new team members?

Indexing decision history is best handled by querying the stored explanation records, allowing new team members to search past decisions by keyword or tag to understand design rationale.

Do I need specrails agents running to generate explanation records for searching?

Yes, you need sr-architect, sr-developer, and sr-reviewer agents to generate explanation records during the OpenSpec pipeline before this skill can search and retrieve them.