Does this skill perform the verification itself?

This skill provides the decision logic and criteria. It is designed to be used alongside other tools like WebFetch or shell commands to perform the actual lookups.

Why do I need a knowledge freshness skill?

AI models have a training knowledge cutoff. This skill ensures you don't use outdated code patterns or deprecated versions by identifying exactly when verification is necessary.

Can I use this for any programming language?

Yes, the triage logic applies to all development domains, including library management, API usage, and CLI interactions across all programming languages and tech stacks.

What are common anti-patterns this skill helps avoid?

It prevents relying on memory for versions, assuming session-wide consistency, and trusting generated patterns without independent verification against current documentation.

How does the triage framework work?

It categorizes technical domains into High (always verify), Medium (verify when uncertain), and Low (trust training data) risk tiers based on how frequently the technology changes.

Knowledge Freshness Triage

Name: Knowledge Freshness Triage
Author: Kiwi-Home

byKiwi-Home

0•

Productivity & Workflow

Evaluates the reliability of AI training data and provides a decision framework for verifying library versions and API patterns.

The Knowledge Freshness skill addresses the inherent 'staleness' of AI training data by implementing a robust triage framework. It classifies technical domains by risk level—High, Medium, or Low—guiding developers on exactly when to trust internal knowledge and when to trigger external verification. By standardizing the 'verify before trusting' workflow, this skill prevents common AI-generated errors related to deprecated APIs, breaking changes in newer library versions, and incorrect CLI flags, ensuring production code remains modern and maintainable.

Key Features

010 GitHub stars

02Decision-making criteria for adding dependencies and writing framework patterns

03Mapping of verification methods to specific tools like Bash, WebFetch, and MCP

04Identification of common AI anti-patterns like 'session-internal staleness drift'

05Three-tier staleness risk classification for library, API, and framework knowledge

06Cross-referencing with project-specific stack detection and codebase analysis

Use Cases

01Validating CLI flags and tool capabilities against current documentation before execution

02Verifying if an API signature has changed in a framework after a recent major release

03Determining if a suggested library version is safe to use or requires a registry lookup

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add kiwi-home/ai-coding-resources knowledge-freshness

For use in Claude.ai and ChatGPT

Download Skill