Chroma Vector Database
Orchestra-Research/AI-Research-SKILLs
Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.
Latest AI models reference - Claude, OpenAI, Gemini, Eleven Labs, Replicate
$ npx skills add alinaqi/maggy --skill ai-models -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install alinaqi/maggy ai-models --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "ai-models" agent skill from https://github.com/alinaqi/maggy/tree/main/skills/ai-models into .claude/skills/ai-models/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-models", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/alinaqi/maggy/tree/main/skills/ai-modelsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add alinaqi/maggy --skill ai-models -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install alinaqi/maggy ai-models --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alinaqi/maggy.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ai-models .agents/skills/ai-models && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ai-models" agent skill from https://github.com/alinaqi/maggy/tree/main/skills/ai-models into .agents/skills/ai-models/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-models", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add alinaqi/maggy --skill ai-models -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install alinaqi/maggy ai-models --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alinaqi/maggy.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ai-models .cursor/skills/ai-models && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ai-models" agent skill from https://github.com/alinaqi/maggy/tree/main/skills/ai-models into .cursor/skills/ai-models/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-models", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/alinaqi/maggy.git --path skills/ai-models--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add alinaqi/maggy --skill ai-models -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install alinaqi/maggy ai-models --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alinaqi/maggy.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ai-models .gemini/skills/ai-models && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ai-models" agent skill from https://github.com/alinaqi/maggy/tree/main/skills/ai-models into .gemini/skills/ai-models/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-models", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install alinaqi/maggy ai-modelsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add alinaqi/maggy --skill ai-models -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/alinaqi/maggy.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ai-models .github/skills/ai-models && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ai-models" agent skill from https://github.com/alinaqi/maggy/tree/main/skills/ai-models into .github/skills/ai-models/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-models", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add alinaqi/maggy --skill ai-models -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install alinaqi/maggy ai-models --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alinaqi/maggy.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ai-models .opencode/skills/ai-models && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ai-models" agent skill from https://github.com/alinaqi/maggy/tree/main/skills/ai-models into .opencode/skills/ai-models/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-models", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
ai-modelsLatest AI models reference - Claude, OpenAI, Gemini, Eleven Labs, Replicate
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.
Read from SKILL.md and the folder at commit 72a456e. It shows what the files ask for, not the result of running them.
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.
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.
Hosts in commands or code, which the agent is likely to contact:
api.stability.aiapi.voyageai.comAlso links to:
ai.google.develevenlabs.ioreplicate.comdocs.anthropic.complatform.openai.comopenai.comdocs.mistral.aidocs.voyageai.complatform.stability.aianthropic.comstability.aimistral.aivoyageai.comFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
ANTHROPIC_API_KEYOPENAI_API_KEYGOOGLE_API_KEYELEVENLABS_API_KEYREPLICATE_API_TOKENSTABILITY_API_KEYMISTRAL_API_KEYVOYAGE_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from alinaqi/maggy at commit 72a456e, republished under its MIT licence (© alinaqi). 306 words, ~4,092 tokens.
.claude/skills/ai-models/SKILL.md (or your agent's skills folder).Last Updated: December 2025
Use the right model for the job. Bigger isn't always better - match model capabilities to task requirements. Consider cost, latency, and accuracy tradeoffs.
| Task | Recommended | Why |
|---|---|---|
| Complex reasoning | Claude Opus 4.5, o3, Gemini 3 Pro | Highest accuracy |
| Fast chat/completion | Claude Haiku, GPT-4.1 mini, Gemini Flash | Low latency, cheap |
| Code generation | Claude Sonnet 4.5, Codestral, GPT-4.1 | Strong coding |
| Vision/images | Claude Sonnet, GPT-4o, Gemini 3 Pro | Multimodal |
| Embeddings | text-embedding-3-small, Voyage | Cost-effective |
| Voice synthesis | Eleven Labs v3, OpenAI TTS | Natural sounding |
| Image generation | FLUX.2, DALL-E 3, SD 3.5 | Different styles |
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;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!' }
],
});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 mostconst 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;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',
});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-consciousconst 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;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]);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 tasksconst 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;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',
});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, chatbotsconst 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;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',
},
});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 imagesconst 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;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',
}),
}
);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;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...' }],
});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 tracesconst 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;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;| Provider | Cheap | Mid | Premium |
|---|---|---|---|
| Anthropic | $0.25 (Haiku) | $3 (Sonnet 4.5) | $5 (Opus 4.5) |
| OpenAI | $0.15 (4.1-nano) | $2 (4.1) | $15+ (o3) |
| $0.04 (Flash-lite) | $0.08 (Flash) | $1.25 (Pro) | |
| Mistral | $0.25 (Small) | $2.70 (Medium) | $8 (Large) |
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# .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-...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© alinaqi, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/ai-models of alinaqi/maggy.
Open the folder on GitHubat commit 72a456e
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| AI Models this skillalinaqi/maggy | 707 | — | ~4.1k | Automated safety check: Pass | MIT | |
| Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Codebase Managementgiancarloerra/SocratiCode | 3.3k | 1 repos | ~1.8k | Automated safety check: Pass | AGPL-3.0 | |
| CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Docs Plannerstrands-agents/harness-sdk | 8.8k | — | ~821 | Automated safety check: Pass | Apache-2.0 | |
| Codex Fable5baskduf/FableCodex | 437 | — | ~1.6k | Automated safety check: Pass | AGPL-3.0 |
Orchestra-Research/AI-Research-SKILLs
Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.
giancarloerra/SocratiCode
Set up, index, and manage SocratiCode codebase indexing. An agent skill from giancarloerra/SocratiCode.
Orchestra-Research/AI-Research-SKILLs
Explains OpenAI's CLIP model for zero-shot image classification, image-text similarity, semantic image search and content moderation, with install steps and code patterns.
strands-agents/harness-sdk
Identify documentation gaps and prioritize the docs backlog.
baskduf/FableCodex
Apply a Claude Fable 5 inspired operating style inside Codex.
MigoXLab/dingo
A skill your agent uses when the user wants to fact-check an article or verify factual claims in a document.
alinaqi/maggy
AI Engine Optimization - semantic triples, page templates, content clusters for AI citations
alinaqi/maggy
Claude Code Agent Teams - default team-based development with strict TDD pipeline enforcement
alinaqi/maggy
Android Java development with MVVM, ViewBinding, and Espresso testing
alinaqi/maggy
Android Kotlin development with Coroutines, Jetpack Compose, Hilt, and MockK testing
alinaqi/maggy
AI-driven testing agent that auto-discovers, generates, executes, evaluates, and fixes tests for any project type
alinaqi/maggy
AWS Aurora Serverless v2, RDS Proxy, Data API, connection pooling
Works with
Categories
Latest AI models reference - Claude, OpenAI, Gemini, Eleven Labs, Replicate. AI Models is an agent skill from alinaqi/maggy.
AI Models fits situations like: AI & LLM Engineering work in your project.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.