Super Ai Ml Ops
AI/ML operations: evaluation, monitoring, cost control, and reliability. Use for productionizing AI systems.
Install
$ agentstack add skill-arpitexplores-skills-super-super-ai-ml-ops ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-arpitexplores-skills-super-super-ai-ml-ops) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: arpitexplores
- Source: arpitexplores/skills-super
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.