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/performance-regression/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/performance-regression)<a href="https://agentmods.dev/skills/cloudposse/atmos/performance-regression"><img src="https://agentmods.dev/badge/skills/cloudposse/atmos/performance-regression/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/performance-regression"><img src="https://agentmods.dev/badge/skills/cloudposse/atmos/performance-regression.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 | $0.00045 | $0.01134 |
| Opus 5.5 | $0.00018 | $0.00454 |
| Sonnet 5.5 | $0.00009 | $0.00227 |
| Haiku 4.5 | $0.00005 | $0.00113 |
Grade A, and why
performance-regression 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 5d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Regression
Establish whether the reported workload regressed, which operation multiplied, and whether the benchmark can distinguish the known-good and known-bad builds. Preserve the user's requested scope: an investigation does not authorize production fixes, publishing reports, or cloud operations.
Evidence and workload selection
Search docs/fixes for the affected command, subsystem, and symptoms. Use the relevant fix logs as the source for incident history, reproduction conditions, and known-good/bad revisions; verify those details against the implementation and current behavior.
Record the command, flags, scale, configuration shape, and external operations that trigger the slowdown. Reproduce the reported command itself: a faster neighboring command does not establish that the affected path improved.
Use the suite coverage and field-test results in magefiles/README.md to select or extend a workload. Preserve the conditions that reach the expensive path, verify expected outputs outside timing, and count repeated operations where useful. State which parts of the original workload a local fixture cannot reproduce.
Measure through Mage
Keep automation in magefiles, benchmark workloads in Go, and scratch results
under .context/ or build/benchmarks/. The maintained command contract is in
the Mage README; do not add parallel shell orchestration to the skill.
Collect a new candidate with go tool mage bench:run <candidateDir>.
Builds and setup stay outside measurements. Do not run compilation, tests, or
other heavy work concurrently with timed samples. CLI samples use fresh processes
and isolated caches; harness allocations are not CLI memory measurements.
Retrieve historical benchmarks
Stored measurements are GitHub Actions artifacts from the
Benchmarks workflow.
For an existing PR comparison, follow the sticky comment's measurements link
and download that run's benchmarks-<runID>-<attempt> artifact into
build/benchmarks/candidate/, preserving bench.txt and manifest.json together.
Use its recorded BaseSHA as <main-base-sha> below.
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.
- 5d ago First seen · 107 lines · 45 tokens per session scan A cab5626c8e2e
performance-regression is a skill published in the GitHub repository cloudposse/atmos (1,403 stars, last pushed today), licensed Apache-2.0. It adds 45 tokens to every session and 1,134 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-12.
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