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 omer-metin/skills-for-antigravity --skill distributed-traininggit clone --depth 1 https://github.com/omer-metin/skills-for-antigravityWrote 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/omer-metin/skills-for-antigravity/distributed-training)<a href="https://agentmods.dev/skills/omer-metin/skills-for-antigravity/distributed-training"><img src="https://agentmods.dev/badge/skills/omer-metin/skills-for-antigravity/distributed-training/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/omer-metin/skills-for-antigravity/distributed-training"><img src="https://agentmods.dev/badge/skills/omer-metin/skills-for-antigravity/distributed-training.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00055 | $0.00234 |
| Opus 5 | $0.00028 | $0.00117 |
| Sonnet 5 | $0.00011 | $0.00047 |
| Haiku 4.5 | $0.00006 | $0.00023 |
Grade A, and why
distributed-training 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 9d 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.
This is a copy
86% identical to computer-use-agents — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Distributed Training
Identity
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.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 21 lines · 55 tokens per session scan A e62d05c79ae6
distributed-training is a skill published in the GitHub repository omer-metin/skills-for-antigravity (145 stars, last pushed 7mo ago), licensed Apache-2.0. It adds 55 tokens to every session and 234 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to computer-use-agents, differing in 6 lines, and is treated as a copy.
Other skills, from other repositories
ai-ops
Guides operational excellence for AI/ML systems in production. Use when deploying models, managing inference infrastructure, monitoring model drift, or maintaining AI-powered features. Use when you need reliable, observable, and governable machine learning systems.
data-engineering
Guides data pipeline design, ETL/ELT workflows, schema evolution, and data quality assurance. Use when building data pipelines, designing data warehouses, migrating schemas, or ensuring data integrity across systems. Use when you need reliable, testable, and observable data flows.
ai-llm-integration-expert
Expert guide for integrating Large Language Models (LLMs), Model Context Protocol (MCP v1.x), hybrid reasoning models, RAG architecture, vector databases, and AI agents / Panduan ahli untuk integrasi LLM, Model Context Protocol (MCP), model hybrid reasoning, arsitektur RAG, vector database, dan agen AI.
vector-db-rag-expert
Expert guide for high-performance Vector Databases, Deep RAG architectures, pgvector 0.8+ HNSW, Reciprocal Rank Fusion (RRF), Cross-Encoder Re-ranking, and Late Chunking / Panduan ahli Vector DB, arsitektur Deep RAG, pgvector HNSW, RRF, dan Re-ranking.
graph-rag-knowledge-expert
Expert guide for Knowledge Graphs, GraphRAG, Microsoft GraphRAG, Neo4j Text2Cypher, multi-hop relational retrieval, and hybrid vector-graph search / Panduan ahli Knowledge Graph, GraphRAG, dan pencarian relasional multi-hop.
pydantic-ai-expert
Expert guide for type-safe Python AI agent development with Pydantic AI — dependency injection, structured outputs, model-agnostic routing, and graph workflows / Panduan ahli pengembangan agen AI Python type-safe dengan Pydantic AI.