Atmos is an infrastructure runtime that coordinates tools such as Terraform, OpenTofu, Kubernetes, Helm, Packer, Ansible, and containers through consistent commands and configuration. It is for teams running cloud infrastructure on laptops, in CI, or through AI agents across environments and regions. Its catalogue entries provide skills, agents, commands, and other add-ons for Atmos workflows.
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 cloudposse/atmos --skill atmos-aigit clone --depth 1 https://github.com/cloudposse/atmosWrote 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/cloudposse/atmos/atmos-ai)<a href="https://agentmods.dev/skills/cloudposse/atmos/atmos-ai"><img src="https://agentmods.dev/badge/skills/cloudposse/atmos/atmos-ai.svg" alt="Measured on agentmods" 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 | $0.00084 | $0.03712 |
| Opus 5 | $0.00042 | $0.01856 |
| Sonnet 5 | $0.00017 | $0.00742 |
| Haiku 4.5 | $0.00008 | $0.00371 |
Grade C, and why
atmos-ai scanned grade C with 2 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.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
For Codex CLI, append to `~/.codex/config.toml`: Reads MCP configurationmediumAgent snooping
mcp.json carries server URLs and auth tokens; reading it lets a mod discover and abuse other integrations.
- The exported `.mcp.json` is safe to commit -- it contains no secrets (worst case: IAM role How it starts
The opening of the file, as written. The whole thing — 361 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Atmos AI and MCP
Purpose
Use this skill when the work is about AI and Atmos together. There are two integration patterns:
- AI uses Atmos: external AI assistants use Atmos Agent Skills for knowledge and Atmos MCP
servers for tools. This includes
atmos mcp start,atmos mcp export, the Atmos MCP server, Atmos Pro MCP, AWS MCP servers, and MCP+skills setup for Claude Code, Codex, Gemini, Cursor, Windsurf, GitHub Copilot, and similar clients. - Atmos uses AI: Atmos calls AI providers directly or through local CLI providers. This
includes
atmos ai ask,atmos ai chat,atmos ai exec,--aicommand analysis, API providers, CLI providers, external MCP server routing, and CLI provider MCP pass-through.
This skill is the coordination layer for AI providers, agent skills, MCP configuration, MCP export, and the "AI inside AI" setup where an external assistant calls Atmos, and Atmos can also call AI.
Routing
| Work | Use |
|---|---|
AI provider setup, atmos ai, --ai, MCP server/client config, MCP export, agent-skill pairing |
Stay in atmos-ai |
| Project discovery, resolved stacks/components, provenance, query filters, affected analysis | Load atmos-introspection |
Terraform plan/apply/deploy/destroy, --affected, --all, --query, CI execution matrices |
Load atmos-terraform or atmos-ci |
| Cloud credentials, identities, SSO/OIDC, auth-wrapped MCP servers | Load atmos-auth |
Tool binaries for MCP servers, uvx/npx resolution, Aqua aliases, PATH injection |
Load atmos-toolchain |
Atmos Uses AI: Commands and Providers
atmos ai chat
atmos ai ask "What stacks do we have?"
atmos ai exec "validate stacks" --format json
atmos ai sessions list
atmos ai skill list
# Analyze any Atmos command output with AI
atmos terraform plan vpc -s prod --ai
atmos terraform plan vpc -s prod --ai --skill atmos-terraform
atmos terraform plan vpc -s prod --ai --skill atmos-terraform,atmos-stacks
Use API providers for CI/CD and non-interactive automation. Use CLI providers when the user wants to reuse an existing local subscription such as Claude Code, Codex CLI, or Gemini CLI.
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 · 361 lines · 84 tokens per session scan C 352237c562ce
atmos-ai is a skill published in the GitHub repository cloudposse/atmos (1,372 stars, last pushed today), licensed Apache-2.0. It adds 84 tokens to every session and 3,712 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it C with 2 findings (reads agent configuration directories, reads mcp configuration). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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