Agent skill

AI Models

by alinaqi in alinaqi/maggy

Latest AI models reference - Claude, OpenAI, Gemini, Eleven Labs, Replicate

MITAuto-check passedAI & LLM Engineering

Install AI Models

skills CLI
$ npx skills add alinaqi/maggy --skill ai-models -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install alinaqi/maggy ai-models --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/alinaqi/maggy.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-models .claude/skills/ai-models && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ai-models
GitHub stars
707
Token cost
~4.1k tokens
SKILL.md length
306 words
Files
1
Skills in repo
71
Repo updated
First seen
Licence
MIT

At a glance

Latest AI models reference - Claude, OpenAI, Gemini, Eleven Labs, Replicate

  • AI & LLM Engineering work in your project
  • SKILL.md covers Philosophy, Model Selection Matrix, Anthropic (Claude) and OpenAI, plus 4 more sections
  • Reaches api.stability.ai and api.voyageai.com; needs ANTHROPIC_API_KEY and OPENAI_API_KEY

What it does

AI Models is an agent skill from alinaqi/maggy. Latest AI models reference - Claude, OpenAI, Gemini, Eleven Labs, Replicate

Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering. It works with OpenAI. The repository describes itself as: What started as an opinionated Claude Code setup kit is now an autonomous AI engineering command center. The licence is MIT.

When your agent uses it

  • AI & LLM Engineering work in your project

Example prompts

  • “/ai-models”

Requirements

  • A credential in ANTHROPIC_API_KEY
  • A credential in OPENAI_API_KEY

What it can do on your machine

Read from SKILL.md and the folder at commit 72a456e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript and bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • api.stability.ai
    • api.voyageai.com

    Also links to:

    • ai.google.dev
    • elevenlabs.io
    • replicate.com
    • docs.anthropic.com
    • platform.openai.com
    • openai.com
    • docs.mistral.ai
    • docs.voyageai.com
    • platform.stability.ai
    • anthropic.com
    • stability.ai
    • mistral.ai
    • voyageai.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • ANTHROPIC_API_KEY
    • OPENAI_API_KEY
    • GOOGLE_API_KEY
    • ELEVENLABS_API_KEY
    • REPLICATE_API_TOKEN
    • STABILITY_API_KEY
    • MISTRAL_API_KEY
    • VOYAGE_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

AI Models loads about 4.1k tokens when it runs. Until then it costs about 21 tokens; SKILL.md has 306 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~21
When it runs · the whole SKILL.md, loaded when a task matches
~4.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from alinaqi/maggy at commit 72a456e, republished under its MIT licence (© alinaqi). 306 words, ~4,092 tokens.

Download SKILL.mdSave it as .claude/skills/ai-models/SKILL.md (or your agent's skills folder).
name
ai-models
description
Latest AI models reference - Claude, OpenAI, Gemini, Eleven Labs, Replicate
when-to-use
When choosing models, comparing capabilities, or referencing model specs
user-invocable
true
effort
low

AI Models Reference Skill

Last Updated: December 2025

Philosophy

Use the right model for the job. Bigger isn't always better - match model capabilities to task requirements. Consider cost, latency, and accuracy tradeoffs.

Model Selection Matrix

TaskRecommendedWhy
Complex reasoningClaude Opus 4.5, o3, Gemini 3 ProHighest accuracy
Fast chat/completionClaude Haiku, GPT-4.1 mini, Gemini FlashLow latency, cheap
Code generationClaude Sonnet 4.5, Codestral, GPT-4.1Strong coding
Vision/imagesClaude Sonnet, GPT-4o, Gemini 3 ProMultimodal
Embeddingstext-embedding-3-small, VoyageCost-effective
Voice synthesisEleven Labs v3, OpenAI TTSNatural sounding
Image generationFLUX.2, DALL-E 3, SD 3.5Different styles

Anthropic (Claude)

Documentation
Latest Models (December 2025)
typescript
const CLAUDE_MODELS = {
  // Flagship - highest capability
  opus: 'claude-opus-4-5-20251101',

  // Balanced - best for most tasks
  sonnet: 'claude-sonnet-4-5-20250929',

  // Previous generation (still excellent)
  opus4: 'claude-opus-4-6',
  sonnet4: 'claude-sonnet-5',

  // Fast & cheap - high volume tasks
  haiku: 'claude-haiku-4-5-20251001',
} as const;
Usage
typescript
import Anthropic from '@anthropic-ai/sdk';

const anthropic = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY,
});

const response = await anthropic.messages.create({
  model: 'claude-sonnet-4-5-20250929',
  max_tokens: 1024,
  messages: [
    { role: 'user', content: 'Hello, Claude!' }
  ],
});
Model Selection
claude-opus-4-5-20251101 (Opus 4.5)
├── Best for: Complex analysis, research, nuanced writing
├── Context: 200K tokens
├── Cost: $5/$25 per 1M tokens (input/output)
└── Use when: Accuracy matters most

claude-sonnet-4-5-20250929 (Sonnet 4.5)
├── Best for: Code, general tasks, balanced performance
├── Context: 200K tokens
├── Cost: $3/$15 per 1M tokens
└── Use when: Default choice for most applications

claude-haiku-4-5-20251001 (Haiku 4.5)
├── Best for: Classification, extraction, high-volume
├── Context: 200K tokens
├── Cost: $1/$5 per 1M tokens
└── Use when: Speed and cost matter most

OpenAI

Documentation
Latest Models (December 2025)
typescript
const OPENAI_MODELS = {
  // GPT-5 series (latest)
  gpt5: 'gpt-5.2',
  gpt5Mini: 'gpt-5-mini',

  // GPT-4.1 series (recommended for most)
  gpt41: 'gpt-4.1',
  gpt41Mini: 'gpt-4.1-mini',
  gpt41Nano: 'gpt-4.1-nano',

  // Reasoning models (o-series)
  o3: 'o3',
  o3Pro: 'o3-pro',
  o4Mini: 'o4-mini',

  // Legacy but still useful
  gpt4o: 'gpt-4o',           // Still has audio support
  gpt4oMini: 'gpt-4o-mini',

  // Embeddings
  embeddingSmall: 'text-embedding-3-small',
  embeddingLarge: 'text-embedding-3-large',

  // Image generation
  dalle3: 'dall-e-3',
  gptImage: 'gpt-image-1',

  // Audio
  tts: 'tts-1',
  ttsHd: 'tts-1-hd',
  whisper: 'whisper-1',
} as const;
Usage
typescript
import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

// Chat completion
const response = await openai.chat.completions.create({
  model: 'gpt-4.1',
  messages: [
    { role: 'user', content: 'Hello!' }
  ],
});

// With vision
const visionResponse = await openai.chat.completions.create({
  model: 'gpt-4.1',
  messages: [
    {
      role: 'user',
      content: [
        { type: 'text', text: 'What is in this image?' },
        { type: 'image_url', image_url: { url: 'https://...' } },
      ],
    },
  ],
});

// Embeddings
const embedding = await openai.embeddings.create({
  model: 'text-embedding-3-small',
  input: 'Your text here',
});
Model Selection
o3 / o3-pro
├── Best for: Math, coding, complex multi-step reasoning
├── Context: 200K tokens
├── Cost: Premium pricing
└── Use when: Hardest problems, need chain-of-thought

gpt-4.1
├── Best for: General tasks, coding, instruction following
├── Context: 1M tokens (!)
├── Cost: Lower than GPT-4o
└── Use when: Default choice, replaces GPT-4o

gpt-4.1-mini / gpt-4.1-nano
├── Best for: High-volume, cost-sensitive
├── Context: 1M tokens
├── Cost: Very low
└── Use when: Simple tasks at scale

o4-mini
├── Best for: Fast reasoning at low cost
├── Context: 200K tokens
├── Cost: Budget reasoning
└── Use when: Need reasoning but cost-conscious

Google (Gemini)

Documentation
Latest Models (December 2025)
typescript
const GEMINI_MODELS = {
  // Gemini 3 (Latest)
  gemini3Pro: 'gemini-3-pro-preview',
  gemini3ProImage: 'gemini-3-pro-image-preview',
  gemini3Flash: 'gemini-3-flash-preview',

  // Gemini 2.5 (Stable)
  gemini25Pro: 'gemini-2.5-pro',
  gemini25Flash: 'gemini-2.5-flash',
  gemini25FlashLite: 'gemini-2.5-flash-lite',

  // Specialized
  gemini25FlashTTS: 'gemini-2.5-flash-preview-tts',
  gemini25FlashAudio: 'gemini-2.5-flash-native-audio-preview-12-2025',

  // Previous generation
  gemini2Flash: 'gemini-2.0-flash',
} as const;
Usage
typescript
import { GoogleGenerativeAI } from '@google/generative-ai';

const genAI = new GoogleGenerativeAI(process.env.GOOGLE_API_KEY);
const model = genAI.getGenerativeModel({ model: 'gemini-2.5-flash' });

const result = await model.generateContent('Hello!');
const response = result.response.text();

// With vision
const visionModel = genAI.getGenerativeModel({ model: 'gemini-2.5-pro' });
const imagePart = {
  inlineData: {
    data: base64Image,
    mimeType: 'image/jpeg',
  },
};
const result = await visionModel.generateContent(['Describe this:', imagePart]);
Model Selection
gemini-3-pro-preview
├── Best for: "Best model in the world for multimodal"
├── Context: 2M tokens
├── Cost: Premium
└── Use when: Need absolute best quality

gemini-2.5-pro
├── Best for: State-of-the-art thinking, complex tasks
├── Context: 2M tokens
├── Cost: $1.25/$5 per 1M tokens
└── Use when: Long context, complex reasoning

gemini-2.5-flash
├── Best for: Fast, balanced performance
├── Context: 1M tokens
├── Cost: $0.075/$0.30 per 1M tokens
└── Use when: Speed and cost matter

gemini-2.5-flash-lite
├── Best for: Ultra-fast, lowest cost
├── Context: 1M tokens
├── Cost: $0.04/$0.15 per 1M tokens
└── Use when: High volume, simple tasks

Eleven Labs (Voice)

Documentation
Latest Models (December 2025)
typescript
const ELEVENLABS_MODELS = {
  // Latest - highest quality (alpha)
  v3: 'eleven_v3',

  // Production ready
  multilingualV2: 'eleven_multilingual_v2',
  turboV2_5: 'eleven_turbo_v2_5',

  // Ultra-low latency
  flashV2_5: 'eleven_flash_v2_5',
  flashV2: 'eleven_flash_v2', // English only
} as const;
Usage
typescript
import { ElevenLabsClient } from 'elevenlabs';

const elevenlabs = new ElevenLabsClient({
  apiKey: process.env.ELEVENLABS_API_KEY,
});

// Text to speech
const audio = await elevenlabs.textToSpeech.convert('voice-id', {
  text: 'Hello, world!',
  model_id: 'eleven_turbo_v2_5',
  voice_settings: {
    stability: 0.5,
    similarity_boost: 0.75,
  },
});

// Stream audio (for real-time)
const audioStream = await elevenlabs.textToSpeech.convertAsStream('voice-id', {
  text: 'Streaming audio...',
  model_id: 'eleven_flash_v2_5',
});
Model Selection
eleven_v3 (Alpha)
├── Best for: Highest quality, emotional range
├── Latency: ~1s+ (not for real-time)
├── Languages: 74
└── Use when: Quality over speed, pre-rendered

eleven_turbo_v2_5
├── Best for: Balanced quality and speed
├── Latency: ~250-300ms
├── Languages: 32
└── Use when: Good quality with reasonable latency

eleven_flash_v2_5
├── Best for: Real-time, conversational AI
├── Latency: <75ms
├── Languages: 32
└── Use when: Live voice agents, chatbots

Replicate

Documentation
typescript
const REPLICATE_MODELS = {
  // FLUX.2 (Latest - November 2025)
  flux2Pro: 'black-forest-labs/flux-2-pro',
  flux2Flex: 'black-forest-labs/flux-2-flex',
  flux2Dev: 'black-forest-labs/flux-2-dev',

  // FLUX.1 (Still excellent)
  flux11Pro: 'black-forest-labs/flux-1.1-pro',
  fluxKontext: 'black-forest-labs/flux-kontext', // Image editing
  fluxSchnell: 'black-forest-labs/flux-schnell',

  // Video
  stableVideo4D: 'stability-ai/sv4d-2.0',

  // Audio
  musicgen: 'meta/musicgen',

  // LLMs (if needed outside main providers)
  llama: 'meta/llama-3.2-90b-vision',
} as const;
Usage
typescript
import Replicate from 'replicate';

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

// Image generation with FLUX.2
const output = await replicate.run('black-forest-labs/flux-2-pro', {
  input: {
    prompt: 'A serene mountain landscape at sunset',
    aspect_ratio: '16:9',
    output_format: 'webp',
  },
});

// Image editing with Kontext
const edited = await replicate.run('black-forest-labs/flux-kontext', {
  input: {
    image: 'https://...',
    prompt: 'Change the sky to sunset colors',
  },
});
Model Selection
flux-2-pro
├── Best for: Highest quality, up to 4MP
├── Speed: ~6s
├── Cost: $0.015 + per megapixel
└── Use when: Professional quality needed

flux-2-flex
├── Best for: Fine details, typography
├── Speed: ~22s
├── Cost: $0.06 per megapixel
└── Use when: Need precise control

flux-2-dev (Open source)
├── Best for: Fast generation
├── Speed: ~2.5s
├── Cost: $0.012 per megapixel
└── Use when: Speed over quality

flux-kontext
├── Best for: Image editing with text
├── Speed: Variable
├── Cost: Per run
└── Use when: Edit existing images

Stability AI

Documentation
Latest Models (December 2025)
typescript
const STABILITY_MODELS = {
  // Image generation
  sd35Large: 'sd3.5-large',
  sd35LargeTurbo: 'sd3.5-large-turbo',
  sd3Medium: 'sd3-medium',

  // Video
  sv4d: 'sv4d-2.0', // Stable Video 4D 2.0

  // Upscaling
  upscale: 'esrgan-v1-x2plus',
} as const;
Usage
typescript
const response = await fetch(
  'https://api.stability.ai/v2beta/stable-image/generate/sd3',
  {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      Authorization: `Bearer ${process.env.STABILITY_API_KEY}`,
    },
    body: JSON.stringify({
      prompt: 'A futuristic city at night',
      output_format: 'webp',
      aspect_ratio: '16:9',
      model: 'sd3.5-large',
    }),
  }
);

Mistral AI

Documentation
Latest Models (December 2025)
typescript
const MISTRAL_MODELS = {
  // Flagship
  large: 'mistral-large-latest',  // Points to 2411

  // Medium tier
  medium: 'mistral-medium-2505',  // Medium 3

  // Small/Fast
  small: 'mistral-small-2506',    // Small 3.2

  // Code specialized
  codestral: 'codestral-2508',
  devstral: 'devstral-medium-2507',

  // Reasoning (Magistral)
  magistralMedium: 'magistral-medium-2507',
  magistralSmall: 'magistral-small-2507',

  // Audio
  voxtral: 'voxtral-small-2507',

  // OCR
  ocr: 'mistral-ocr-2505',
} as const;
Usage
typescript
import MistralClient from '@mistralai/mistralai';

const client = new MistralClient(process.env.MISTRAL_API_KEY);

const response = await client.chat({
  model: 'mistral-large-latest',
  messages: [{ role: 'user', content: 'Hello!' }],
});

// Code completion with Codestral
const codeResponse = await client.chat({
  model: 'codestral-2508',
  messages: [{ role: 'user', content: 'Write a Python function to...' }],
});
Model Selection
mistral-large-latest (123B params)
├── Best for: Complex reasoning, knowledge tasks
├── Context: 128K tokens
└── Use when: Need high capability

codestral-2508
├── Best for: Code generation, 80+ languages
├── Speed: 2.5x faster than predecessor
└── Use when: Code-focused tasks

magistral-medium-2507
├── Best for: Multi-step reasoning
├── Specialty: Transparent chain-of-thought
└── Use when: Need reasoning traces

Voyage AI (Embeddings)

Documentation
Latest Models (December 2025)
typescript
const VOYAGE_MODELS = {
  // General purpose
  large2: 'voyage-large-2',
  large2Instruct: 'voyage-large-2-instruct',

  // Code specialized
  code2: 'voyage-code-2',
  code3: 'voyage-code-3',

  // Multilingual
  multilingual2: 'voyage-multilingual-2',

  // Domain specific
  law2: 'voyage-law-2',
  finance2: 'voyage-finance-2',
} as const;
Usage
typescript
const response = await fetch('https://api.voyageai.com/v1/embeddings', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    Authorization: `Bearer ${process.env.VOYAGE_API_KEY}`,
  },
  body: JSON.stringify({
    model: 'voyage-code-3',
    input: ['Your code to embed'],
  }),
});

const { data } = await response.json();
const embedding = data[0].embedding;

Quick Reference

Cost Comparison (per 1M tokens, approx.)
ProviderCheapMidPremium
Anthropic$0.25 (Haiku)$3 (Sonnet 4.5)$5 (Opus 4.5)
OpenAI$0.15 (4.1-nano)$2 (4.1)$15+ (o3)
Google$0.04 (Flash-lite)$0.08 (Flash)$1.25 (Pro)
Mistral$0.25 (Small)$2.70 (Medium)$8 (Large)
Best For Each Task
Reasoning/Analysis    → Claude Opus 4.5, o3, Gemini 3 Pro
Code Generation       → Claude Sonnet 4.5, Codestral 2508, GPT-4.1
Fast Responses        → Claude Haiku, GPT-4.1-mini, Gemini Flash
Long Context          → Gemini 2.5 Pro (2M), GPT-4.1 (1M), Claude (200K)
Vision                → GPT-4.1, Claude Sonnet, Gemini 3 Pro
Embeddings            → Voyage code-3, text-embedding-3-small
Voice Synthesis       → Eleven Labs v3/flash, OpenAI TTS
Image Generation      → FLUX.2 Pro, DALL-E 3, SD 3.5
Video Generation      → Stable Video 4D 2.0, Runway
Image Editing         → FLUX Kontext, gpt-image-1
Environment Variables Template
bash
# .env.example (NEVER commit actual keys)

# LLMs
ANTHROPIC_API_KEY=sk-ant-...
OPENAI_API_KEY=sk-...
GOOGLE_API_KEY=AI...
MISTRAL_API_KEY=...

# Media
ELEVENLABS_API_KEY=...
REPLICATE_API_TOKEN=r8_...
STABILITY_API_KEY=sk-...

# Embeddings
VOYAGE_API_KEY=pa-...
Model Update Checklist
When models update:
□ Check official changelog/blog
□ Update model ID strings
□ Test with existing prompts
□ Compare output quality
□ Check pricing changes
□ Update context limits if changed

Sources

© alinaqi, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/ai-models of alinaqi/maggy.

Open the folder on GitHubat commit 72a456e

Compare with similar skills

AI Models next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

AI Models compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
AI Models this skillalinaqi/maggy707—~4.1kAutomated safety check: PassMIT
Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs13k7 repos~2.3kAutomated safety check: PassMIT
Codebase Managementgiancarloerra/SocratiCode3.3k1 repos~1.8kAutomated safety check: PassAGPL-3.0
CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs13k7 repos~1.7kAutomated safety check: PassMIT
Docs Plannerstrands-agents/harness-sdk8.8k—~821Automated safety check: PassApache-2.0
Codex Fable5baskduf/FableCodex437—~1.6kAutomated safety check: PassAGPL-3.0

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Works with

Questions about AI Models

What does AI Models do?

Latest AI models reference - Claude, OpenAI, Gemini, Eleven Labs, Replicate. AI Models is an agent skill from alinaqi/maggy.

When should I use AI Models?

AI Models fits situations like: AI & LLM Engineering work in your project.

How do I install AI Models in Claude Code?

Run `npx skills add alinaqi/maggy --skill ai-models -a claude-code`. Or copy the skill folder (skills/ai-models in alinaqi/maggy) into .claude/skills/ai-models in your project. Claude Code loads it when a task matches its description.

How do I install AI Models in Codex?

Run `npx skills add alinaqi/maggy --skill ai-models -a codex`. Or copy the skill folder (skills/ai-models in alinaqi/maggy) into .agents/skills/ai-models in your project. Codex loads it when a task matches its description.

Can I use AI Models in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add alinaqi/maggy --skill ai-models -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-models, .gemini/skills/ai-models, .github/skills/ai-models and .opencode/skills/ai-models in your project.

What does AI Models need to run?

Going by SKILL.md and its folder, AI Models needs credentials named ANTHROPIC_API_KEY, OPENAI_API_KEY, GOOGLE_API_KEY and ELEVENLABS_API_KEY. Our summary lists: A credential in ANTHROPIC_API_KEY; A credential in OPENAI_API_KEY.

Does AI Models access the network?

SKILL.md names 15 domains. In commands or code: api.stability.ai and api.voyageai.com; the agent is likely to contact these when it follows the instructions. As links in the text: ai.google.dev, elevenlabs.io, replicate.com, docs.anthropic.com, platform.openai.com, openai.com, docs.mistral.ai, docs.voyageai.com, platform.stability.ai, anthropic.com, stability.ai, mistral.ai and voyageai.com. This is read from the text; nothing was executed.

Is AI Models safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does AI Models use?

AI Models is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does AI Models use?

About 4.1k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to AI Models?

Skills that share tags, products or a category with AI Models: Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), Codebase Management (giancarloerra/SocratiCode, 3.3k stars), CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Docs Planner (strands-agents/harness-sdk, 8.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AI Models?

alinaqi (a GitHub user) maintains it in alinaqi/maggy, which has 707 GitHub stars. The repository holds 71 skills in this directory. The repository was last updated on September 24, 2026.

Source: alinaqi/maggy on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.