review-reference-maintenance

Name: review-reference-maintenance
Availability: InStock
Author: koseki2580

Audit and reclassify team-lead review references by reusability and context clarity.

Updated Apr 30, 2026

Search Tags:#quality assurance #code review #knowledge management #team workflow #reference maintenance #applicability criteria #status triage

Ready to run

npx skills add https://github.com/koseki2580/skills --skill review-reference-maintenance

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: review-reference-maintenance
Source: https://github.com/koseki2580/skills/tree/main/skills/review-reference-maintenance
Command: npx skills add https://github.com/koseki2580/skills --skill review-reference-maintenance

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Review references given to AI reviewers can become stale or overly contextual, causing incorrect assessments and inconsistent feedback. This Skill helps teams periodically audit those references so the review system keeps producing reliable guidance.

Core Features & Use Cases

Audit and triage reference freshness: Evaluate entries by status (active first), review age, and applicability to current codebase/design direction.
Classify by reusability strength: Move items among active, candidate, and deprecated based on concrete criteria like context clarity and evidence quality.
Harden references for reuse: Distill lessons to remove personal names, emotion, and overly context-dependent phrasing so references stay broadly applicable.

Quick Start

Use review-reference-maintenance to inspect review-references/team-lead/references.yaml and update reference statuses (active/candidate/deprecated) with clear reasons and a maintenance result summary.

Frequently Asked Questions about review-reference-maintenance

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

FAQPage Schema

How do I prevent stale code review references from giving incorrect AI feedback?▼

To prevent stale code review references from causing incorrect AI feedback, you need periodic reference maintenance to audit and reclassify entries by status, review age, and applicability to the current codebase. This ensures active references remain accurate and reusable.

What is the best way to triage outdated review guidance for AI code reviews?▼

The best way to triage outdated review guidance is to evaluate active entries first based on review age and codebase applicability, then assign candidate or deprecated status based on concrete evidence of context clarity and reusability strength.

How do I audit a references.yaml file to keep team-lead review guidance reusable?▼

To audit a references.yaml file for team-lead review guidance, inspect each entry to evaluate reusability and context clarity, update statuses to active, candidate, or deprecated, and generate a structured maintenance result with counts and human-review flags.

How do I remove personal names and overly contextual phrasing from team workflow references?▼

To remove personal names and overly contextual phrasing from team workflow references, harden the references by distilling lessons to eliminate emotion and context-dependent details, ensuring the guidance stays broadly applicable for future AI code reviews.

When should I deprecate a review reference instead of keeping it active?▼

You should deprecate a review reference when it lacks applicability evidence, context clarity, or reusability strength for the current design direction, moving it from active to deprecated status while flagging entries that require human review.

Do I need any specific dependencies to perform review-reference maintenance?▼

No specific dependencies are required to perform review-reference maintenance. The process operates directly on your team-lead references configuration to evaluate reusability criteria and output a structured maintenance summary with status counts and human-review flags.