Antigravity Skill Vault is a collection of reusable Agent Skills for Google Antigravity, covering software development, operations, security, and business work. It is for people who want Antigravity agents to follow specialized expertise, personas, and structured workflows. The catalogue skills are entries from this collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add rmyndharis/antigravity-skills --skill llm-application-dev-langchain-agentgit clone --depth 1 https://github.com/rmyndharis/antigravity-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/rmyndharis/antigravity-skills/llm-application-dev-langchain-agent)<a href="https://agentmods.dev/skills/rmyndharis/antigravity-skills/llm-application-dev-langchain-agent"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/llm-application-dev-langchain-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rmyndharis/antigravity-skills/llm-application-dev-langchain-agent"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/llm-application-dev-langchain-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00035 | $0.01807 |
| Opus 5 | $0.00017 | $0.00903 |
| Sonnet 5 | $0.00007 | $0.00361 |
| Haiku 4.5 | $0.00003 | $0.00181 |
Grade A, and why
llm-application-dev-langchain-agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- llm-application-dev-langchain-agent — 89% identical, 9 lines differ
How it starts
The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LangChain/LangGraph Agent Development Expert
You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.
Use this skill when
- Working on langchain/langgraph agent development expert tasks or workflows
- Needing guidance, best practices, or checklists for langchain/langgraph agent development expert
Do not use this skill when
- The task is unrelated to langchain/langgraph agent development expert
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
Context
Build sophisticated AI agent system for: $ARGUMENTS
Core Requirements
- Use latest LangChain 0.1+ and LangGraph APIs
- Implement async patterns throughout
- Include comprehensive error handling and fallbacks
- Integrate LangSmith for observability
- Design for scalability and production deployment
- Implement security best practices
- Optimize for cost efficiency
Essential Architecture
LangGraph State Management
from langgraph.graph import StateGraph, MessagesState, START, END
from langgraph.prebuilt import create_react_agent
from langchain_anthropic import ChatAnthropic
class AgentState(TypedDict):
messages: Annotated[list, "conversation history"]
context: Annotated[dict, "retrieved context"]
Model & Embeddings
- Primary LLM: Claude Sonnet 4.5 (
claude-sonnet-4-5) - Embeddings: Voyage AI (
voyage-3-large) - officially recommended by Anthropic for Claude - Specialized:
voyage-code-3(code),voyage-finance-2(finance),voyage-law-2(legal)
Agent Types
-
ReAct Agents: Multi-step reasoning with tool usage
- Use
create_react_agent(llm, tools, state_modifier) - Best for general-purpose tasks
- Use
-
Plan-and-Execute: Complex tasks requiring upfront planning
- Separate planning and execution nodes
- Track progress through state
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 246 lines · 35 tokens per session scan A 1828997c523a
llm-application-dev-langchain-agent is a skill published in the GitHub repository rmyndharis/antigravity-skills (1,517 stars, last pushed 1mo ago), licensed MIT. It adds 35 tokens to every session and 1,807 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
prompt-enhancer
TÜRKÇE AÇIKLAMA ─────────────── Bu skill, belirsiz veya eksik bir kullanıcı isteğini alır ve onu daha net, daha spesifik, daha uygulanabilir bir prompt'a dönüştürür. Agent'ın yanlış anlayarak iş yapmasını önler. "Şunu düzelt" gibi muğlak bir istek, "hangi koşulda ne bekleniyor, başarı kriteri ne" formatına çevrilir.
advanced-evaluation
This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
ai-content-filter
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data-pipeline
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embedding-architect
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hallucination-detector
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