AI Limitations and Best Practices

Understanding AI limitations and how to use AI-generated content responsibly.

3 minute read

AI Systems Can Make Mistakes

Large Language Models (LLMs) like Claude, GPT, and others are powerful tools, but they have inherent limitations that users should understand:

Common AI Limitations

Limitation	Description
Hallucinations	AI can generate plausible-sounding but factually incorrect information
Outdated information	Training data has a cutoff date; AI may not know recent developments
Lack of real-time access	Without tools, AI cannot verify current facts or access live data
Context window limits	Very long conversations or documents may exceed processing capacity
Reasoning errors	Complex multi-step reasoning can produce incorrect conclusions
Bias	Training data biases can affect outputs

Why This Matters for AI Config

The skills and agents in this repository were created with AI assistance. While we strive for accuracy, the content may contain:

Incorrect or outdated information about licenses, standards, or procedures
Oversimplified guidance that doesn’t account for edge cases
References to resources that have moved or changed
Recommendations that may not apply to your specific jurisdiction or context

Best Practices for Using AI-Generated Content

1. Always Verify Critical Information

Before making important decisions based on AI-generated content:

Cross-reference with authoritative sources
Consult professionals for legal, medical, financial, or academic matters
Test thoroughly before using in production environments

2. Authoritative Sources by Domain

Domain	Authoritative Sources
Open Source Licensing	OSI, FSF, SPDX
Creative Commons	Creative Commons Official
French Legal Context	Légifrance, CNIL
Academic Standards (France)	Ministère de l’Enseignement Supérieur
AI Standards	ISO/IEC JTC 1/SC 42, IEEE
API Documentation	Always prefer official documentation over AI summaries

3. Report Errors

If you find inaccuracies in this repository:

Open an issue on GitHub
Provide the correct information with authoritative sources
We will review and update the content

AI Transparency Metadata for Skills

The official Agent Skills specification only requires name and description fields. However, this repository uses extended metadata for AI transparency.

Note: We have proposed these extended metadata fields to the official specification in anthropics/skills#180.

---
# Required by specification
name: skill-name
description: "What the skill does"

# Extended metadata (optional, used in this repository)
version: "1.0.0"                 # Semantic Versioning (MAJOR.MINOR.PATCH)
status: tested | experimental    # Indicates testing level
author: "Name or handle"         # Human author/maintainer
generated_by: "Claude Opus 4.5"  # AI model used for generation
license: MIT                     # License for the skill itself
disclaimer: "This skill provides informational guidance only and does not constitute professional advice."
---

Field	Required	Purpose
`name`	Yes	Skill identifier
`description`	Yes	What the skill does
`version`	No	Semantic Versioning format (MAJOR.MINOR.PATCH)
`status`	No	`tested` or `experimental` - indicates reliability level
`author`	No	Human responsible for the skill
`generated_by`	No	AI model used to create/assist with the skill
`license`	No	License under which the skill is shared
`disclaimer`	No	Skill-specific disclaimer about limitations and appropriate use

This extended metadata helps users understand the provenance and reliability of each skill.

Understanding AI Transparency

This repository follows transparency principles:

Disclosure: All content created with AI assistance is clearly marked
Model identification: We specify which AI model was used (Claude Opus 4.5)
Limitations acknowledged: We don’t claim AI-generated content is error-free
Human oversight: Content is reviewed but may still contain errors