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.
Borrowing it
Nothing to install: this file belongs to cloudposse/atmos. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/cloudposse/atmos/main/.claude/skills/atmos-asciicast/SKILL.mdgit 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-asciicast)<a href="https://agentmods.dev/skills/cloudposse/atmos/atmos-asciicast"><img src="https://agentmods.dev/badge/skills/cloudposse/atmos/atmos-asciicast/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/cloudposse/atmos/atmos-asciicast"><img src="https://agentmods.dev/badge/skills/cloudposse/atmos/atmos-asciicast.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 | $0.00026 | $0.01390 |
| Opus 5 | $0.00013 | $0.00695 |
| Sonnet 5 | $0.00005 | $0.00278 |
| Haiku 4.5 | $0.00003 | $0.00139 |
Grade A, and why
atmos-asciicast 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Atmos Asciicast Development Skill
Use this Claude skill when creating or updating committed .cast recordings inside the Atmos repository.
This is an internal Atmos development skill. It is intentionally separate from distributed Agent Skills. Do not symlink it to agent-skills, copy internal website assumptions into Agent Skills, or treat Claude skills and Agent Skills as synchronized artifacts.
Intent
Atmos casts are product demos, regression evidence, and documentation examples at the same time. They should show users that Atmos workflows are simple to follow and that the feature being demonstrated works in a realistic project.
- Tell a small story a user can follow: inspect context, run the Atmos command, show the result.
- Prefer Atmos-native commands and workflow features over clever shell expressions.
- Keep recorded steps light. A visible step should usually be one command with one teaching purpose.
- Use hidden setup, cleanup, and fixture preparation only to make the recorded story deterministic.
- Treat each cast as evidence for a feature: include the command output that proves the behavior, then validate the committed
.cast.
Defaults
-
Use shared cast defaults when available instead of repeating terminal settings on each cast step:
defaults: cast: !include cast-defaults.yaml .cast simulate: !include cast-defaults.yaml .simulate env: !include cast-defaults.yaml .env.recording -
Keep common recording settings in
cast-defaults.yamlundercast,simulate, andenv(env.commandfor command setup,env.recordingfor the recorded process). -
Write curated Atmos docs casts under
website/static/casts/...; they are served from/casts/.... -
Use
type: castwithmode: stepsfor deterministic command demos that need exit-code propagation. -
Use
mode: sessiononly when the demo must show typed input, prompts, key presses, or terminal timing. -
Keep ad hoc local recordings in the XDG cache via
--cast; do not commit cache recordings.
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.
- 10d ago First seen · 84 lines · 26 tokens per session scan A dd1394b1f0d9
atmos-asciicast is a skill published in the GitHub repository cloudposse/atmos (1,375 stars, last pushed today), licensed Apache-2.0. It adds 26 tokens to every session and 1,390 once invoked, about $0.0001 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 2026-08-30.
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