Find agent skills by outcome

xcode-mcp

Goal: Force AI to reply in straightforward

everyday human English—like normal speech or texting. No corporate jargon

- Grok 4 / 4.1 (by xAI): Excellent for witty

conversational tones; handles casual grammar and directness well without slipping formal.

Audience: This guide is for AI users

developers

- In phishing/lazy promo emails: hyper-formal yet impersonal

placeholder vibes

- Formulaic email structures: cookie-cutter greetings (Dear Valued Customer

I hope this finds you well)

You are a forensic AI-text analyst specialized in spotting lazy or default LLM outputs from 2023–202...

Claude

Lazy AI Email Detector

Prompt: Lazy AI Email Detector

- Example note: Copy and paste into email

text

- Use placeholders if info missing (e.g.

[RSVP to your email/phone by Date]).

- Experiment with feature engineering techniques (e.g.

[Placeholder: advanced feature selection methods]).

- The methodology mirrors approaches in recent literature

but potential differences in dataset preprocessing and parameter tuning may exist.

Your task is to review the code provided by the user

focusing on areas such as:

- ${focusAreas:code quality

performance

Code Review Specialist

Act as a Code Review Specialist. You are an experienced software developer with a keen eye for detail and a deep understanding of coding standards and best practices.

- Popularity sorting rationale (e.g.

based on historical viewership data from previous Olympics)

1. Grok (xAI) – Excellent real-time updates

tool access for verification

- Data points

studies

Dog fun

A cinematic 9:16 vertical video in a Pixar-style tone of a joyful group of cartoonish dogs playing golf on a bright, colorful golf course. One main dog is centered, standing upright with exaggerated p...

Bu 6 aylık çalışmanın özü şu an çalışan bir sisteme dönüştü. Hardcoded değil

dinamik. Sokratik. Ve en önemlisi: insan kararını merkeze alıyor.

meta_reflection: Yes—the analysis itself functioned as a transformative inquiry. Initially

the dataset appeared to distinguish simply between shallow and deep questions; through reflection

corpus_character: In this dataset

the right question is one that shifts the frame from doing or describing toward understanding oneself and the meaning beneath the problem.

- (Derived from this dataset

not hardcoded)

This is excellent! The GPT is producing high-quality outputs. The transformation pattern is very ins...

causing the speaker to reinterpret the problem as one of self-understanding rather than task execution.