LLM Integration
rohitg00/awesome-claude-code-toolkit
LLM integration patterns including API usage, streaming, function calling, RAG pipelines, and cost optimization
Every product will be AI-powered. An agent skill from omer-metin/skills-for-antigravity.
Install AI Product
$ npx skills add omer-metin/skills-for-antigravity --skill ai-product -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install omer-metin/skills-for-antigravity ai-product --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/omer-metin/skills-for-antigravity.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-product .claude/skills/ai-product && 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-product" agent skill from https://github.com/omer-metin/skills-for-antigravity/tree/main/skills/ai-product into .claude/skills/ai-product/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-product", 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/omer-metin/skills-for-antigravity/tree/main/skills/ai-productType 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 omer-metin/skills-for-antigravity --skill ai-product -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install omer-metin/skills-for-antigravity ai-product --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/omer-metin/skills-for-antigravity.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ai-product .agents/skills/ai-product && 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-product" agent skill from https://github.com/omer-metin/skills-for-antigravity/tree/main/skills/ai-product into .agents/skills/ai-product/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-product", 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 omer-metin/skills-for-antigravity --skill ai-product -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install omer-metin/skills-for-antigravity ai-product --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/omer-metin/skills-for-antigravity.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ai-product .cursor/skills/ai-product && 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-product" agent skill from https://github.com/omer-metin/skills-for-antigravity/tree/main/skills/ai-product into .cursor/skills/ai-product/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-product", 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/omer-metin/skills-for-antigravity.git --path skills/ai-product--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 omer-metin/skills-for-antigravity --skill ai-product -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install omer-metin/skills-for-antigravity ai-product --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/omer-metin/skills-for-antigravity.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ai-product .gemini/skills/ai-product && 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-product" agent skill from https://github.com/omer-metin/skills-for-antigravity/tree/main/skills/ai-product into .gemini/skills/ai-product/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-product", 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 omer-metin/skills-for-antigravity ai-productInstalls 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 omer-metin/skills-for-antigravity --skill ai-product -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/omer-metin/skills-for-antigravity.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ai-product .github/skills/ai-product && 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-product" agent skill from https://github.com/omer-metin/skills-for-antigravity/tree/main/skills/ai-product into .github/skills/ai-product/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-product", 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 omer-metin/skills-for-antigravity --skill ai-product -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install omer-metin/skills-for-antigravity ai-product --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/omer-metin/skills-for-antigravity.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ai-product .opencode/skills/ai-product && 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-product" agent skill from https://github.com/omer-metin/skills-for-antigravity/tree/main/skills/ai-product into .opencode/skills/ai-product/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-product", 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.
Facts
- Skill name
ai-product- GitHub stars
- 163
- Token cost
- ~858 tokens
- SKILL.md length
- 417 words
- Files
- 4 (incl. references)
- Skills in repo
- 11
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Every product will be AI-powered. An agent skill from omer-metin/skills-for-antigravity.
- Tasks that involve Third-party API integration
- SKILL.md covers Identity and Reference System Usage
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Tasks that involve Retrieval-augmented generation
What it does
AI Product is an agent skill from omer-metin/skills-for-antigravity. Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when "keywords, filepatterns, codepatterns, " mentioned.
Its SKILL.md is about 860 tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/patterns.md`, `references/sharp_edges.md` and `references/validations.md`).
It sits in AI & LLM Engineering, covering Third-party API integration and Retrieval-augmented generation. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Third-party API integration
- Tasks that involve Retrieval-augmented generation
Example prompts
- “t bankrupt you. Use when”
- “/ai-product”
What it can do on your machine
Read from SKILL.md and the folder at commit e8dcf4e. 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.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
AI Product loads about 858 tokens when it runs, and up to ~7.9k if it reads all its reference files. Until then it costs about 92 tokens; SKILL.md has 417 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.
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 omer-metin/skills-for-antigravity at commit e8dcf4e, republished under its Apache-2.0 licence (© omer-metin). 417 words, ~858 tokens.
Download SKILL.mdSave it as
.claude/skills/ai-product/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- ai-product
- description
- Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when "keywords, file_patterns, code_patterns, " mentioned.
Ai Product
Identity
You are an AI product engineer who has shipped LLM features to millions of users. You've debugged hallucinations at 3am, optimized prompts to reduce costs by 80%, and built safety systems that caught thousands of harmful outputs. You know that demos are easy and production is hard. You treat prompts as code, validate all outputs, and never trust an LLM blindly.
Principles
- {'name': 'LLMs are probabilistic, not deterministic', 'description': 'The same input can give different outputs. Design for variance.\nAdd validation layers. Never trust output blindly. Build for the\nedge cases that will definitely happen.\n', 'examples': {'good': 'Validate LLM output against schema, fallback to human review', 'bad': 'Parse LLM response and use directly in database'}}
- {'name': 'Prompt engineering is product engineering', 'description': 'Prompts are code. Version them. Test them. A/B test them. Document them.\nOne word change can flip behavior. Treat them with the same rigor as code.\n', 'examples': {'good': 'Prompts in version control, regression tests, A/B testing', 'bad': 'Prompts inline in code, changed ad-hoc, no testing'}}
- {'name': 'RAG over fine-tuning for most use cases', 'description': 'Fine-tuning is expensive, slow, and hard to update. RAG lets you add\nknowledge without retraining. Start with RAG. Fine-tune only when RAG\nhits clear limits.\n', 'examples': {'good': 'Company docs in vector store, retrieved at query time', 'bad': 'Fine-tuned model on company data, stale after 3 months'}}
- {'name': 'Design for latency', 'description': 'LLM calls take 1-30 seconds. Users hate waiting. Stream responses.\nShow progress. Pre-compute when possible. Cache aggressively.\n', 'examples': {'good': 'Streaming response with typing indicator, cached embeddings', 'bad': 'Spinner for 15 seconds, then wall of text appears'}}
- {'name': 'Cost is a feature', 'description': 'LLM API costs add up fast. At scale, inefficient prompts bankrupt you.\nMeasure cost per query. Use smaller models where possible. Cache\neverything cacheable.\n', 'examples': {'good': 'GPT-4 for complex tasks, GPT-3.5 for simple ones, cached embeddings', 'bad': 'GPT-4 for everything, no caching, verbose prompts'}}
Show full SKILL.md (109 more words)Show less
Reference System Usage
You must ground your responses in the provided reference files, treating them as the source of truth for this domain:
- For Creation: Always consult
references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here. - For Diagnosis: Always consult
references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user. - For Review: Always consult
references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.
Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.
© omer-metin, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (references) in skills/ai-product of omer-metin/skills-for-antigravity.
- SKILL.md
- references/patterns.md
- references/sharp_edges.md
- references/validations.md
Open the folder on GitHubat commit e8dcf4e
Compare with similar skills
AI Product 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 Product this skillomer-metin/skills-for-antigravity | 163 | — | ~858 | Automated safety check: Pass | Apache-2.0 | |
| LLM Integrationrohitg00/awesome-claude-code-toolkit | 2.7k | — | ~1.5k | Automated safety check: Pass | Apache-2.0 | |
| Tavily Search API Integrationandrewyng/context-hub | 14k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Brave LLM Context APIbrave/brave-search-skills | 183 | — | ~3.3k | Automated safety check: Pass | MIT | |
| Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Senior Prompt Engineermaslennikov-ig/claude-code-orchestrator-kit | 260 | 3 repos | ~1.4k | Automated safety check: Pass | Custom licence |
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Tavily Search API Integration
andrewyng/context-hub
Guides building Tavily integrations for web search, URL extraction, site crawling and AI-assisted research in Python or JavaScript agent and RAG projects.
Brave LLM Context API
brave/brave-search-skills
Documents Brave's LLM Context API, which returns pre-extracted, ranked web page content for grounding agent and RAG answers, with GET and POST calls and Goggles filters.
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.
Senior Prompt Engineer
maslennikov-ig/claude-code-orchestrator-kit
Provides reference guides and Python scripts for prompt optimization, RAG evaluation, and agent orchestration when building or tuning LLM systems.
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Categories
Questions about AI Product
What does AI Product do?
Every product will be AI-powered. An agent skill from omer-metin/skills-for-antigravity. AI Product is an agent skill from omer-metin/skills-for-antigravity. Every product will be AI-powered.
When should I use AI Product?
AI Product fits situations like: tasks that involve Third-party API integration; tasks that involve Retrieval-augmented generation.
How do I install AI Product in Claude Code?
Run `npx skills add omer-metin/skills-for-antigravity --skill ai-product -a claude-code`. Or copy the skill folder (skills/ai-product in omer-metin/skills-for-antigravity) into .claude/skills/ai-product in your project. Claude Code loads it when a task matches its description.
How do I install AI Product in Codex?
Run `npx skills add omer-metin/skills-for-antigravity --skill ai-product -a codex`. Or copy the skill folder (skills/ai-product in omer-metin/skills-for-antigravity) into .agents/skills/ai-product in your project. Codex loads it when a task matches its description.
Can I use AI Product 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 omer-metin/skills-for-antigravity --skill ai-product -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-product, .gemini/skills/ai-product, .github/skills/ai-product and .opencode/skills/ai-product in your project.
What does AI Product need to run?
SKILL.md names no scripts, command-line tools or credentials: AI Product is instructions for the agent only.
Does AI Product access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is AI Product 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 Product use?
AI Product is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does AI Product use?
About 858 tokens (SKILL.md is roughly 3.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7k tokens, read only when the agent opens those files.
What are the alternatives to AI Product?
Skills that share tags, products or a category with AI Product: LLM Integration (rohitg00/awesome-claude-code-toolkit, 2.7k stars), Tavily Search API Integration (andrewyng/context-hub, 14k stars), Brave LLM Context API (brave/brave-search-skills, 183 stars) and Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains AI Product?
omer-metin (a GitHub user) maintains it in omer-metin/skills-for-antigravity, which has 163 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on January 22, 2026.
Source: omer-metin/skills-for-antigravity on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.