Super ai ml ops

Skill arpitexplores/super-ai-ml-ops

AI/ML operations: evaluation, monitoring, cost control, and reliability. Use for productionizing AI systems.From its SKILL.md

Install

npx -y skills add arpitexplores/super-ai-ml-ops

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

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SKILL.md

1.6 KB, 345 tokens by cl100k_base, as published. Nobody here has run it

Super AI/ML Ops

Overview

Make AI systems stable and measurable in production.

User Intent Examples

"Need help with LLM Evaluation for my product/site."
"Create a plan for LLM Ops."
"Not sure where to start, need a quick assessment."

Workflow

Define evaluation metrics, datasets, and acceptance thresholds.
Set up observability for quality, latency, and errors.
Implement caching and cost controls.
Create monitoring and alerting for regressions.
Establish release and rollback procedures for prompts/models.
Document runbooks and ongoing QA cadence.

Minimal Intake Questions

Primary goal or outcome
Scope (pages, systems, teams, or timeframe)
Constraints (tools, budget, timeline)

Output Format

Eval plan and scoring rubric
Monitoring and alerting checklist
Cost and caching strategy
Release and rollback plan
Runbook and QA cadence

Routing Map (Modules)

LLM Evaluation -> references/modules/llm-evaluation.md
LLM Ops -> references/modules/llm-ops.md

Bundled References

references/modules/
scripts/
assets/
agents/

Compatibility Notes

If any module references slash commands or tool-specific paths, translate them into plain-language steps.
Keep outputs platform-agnostic unless the user specifies a specific tool, stack, or agent.

Guardrails

Do not rely on single metrics; include qualitative checks.
Track cost per request and cap budgets.
Treat prompt/model updates as production changes.

What ships with it: 12 files

112.9 KB alongside SKILL.md

Gives 0 of the 12 instructions most ship operate skills give in 345 tokens

Counted across 779 of the 1,178 authors here whose files we hold, read 2026-08-07

Document a rollback plan before deploymentin 41 of 779, across 22 files
Update the changelogin 21 of 779, across 19 files
Run the test suitein 20 of 779
Create an annotated git tagin 20 of 779
Clean up feature flags after full rolloutin 18 of 779, across 10 files
Verify deployment health after launchin 18 of 779, across 10 files
Test both feature flag statesin 17 of 779, across 9 files
Verify the working tree is cleanin 17 of 779
Make database migrations backward-compatiblein 16 of 779, across 8 files
Set up error monitoring before launchin 15 of 779, across 7 files
Monitor metrics at each rollout stagein 14 of 779, across 5 files
Create a GitHub releasein 14 of 779

Said here and by no other author read

Translate tool-specific paths into plain-language steps
Keep outputs platform-agnostic unless specified otherwise
Define evaluation metrics, datasets, and acceptance thresholds
Set up observability for quality, latency, and errors
Implement caching and cost controls
Create monitoring and alerting for regressions

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.