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 agentmods add skills/duqueom/ml-mlops-portfolio/rollbacknpx skills add DuqueOM/ML-MLOps-Portfolio --skill rollbackgit clone --depth 1 https://github.com/DuqueOM/ML-MLOps-PortfolioWrote 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/duqueom/ml-mlops-portfolio/rollback)<a href="https://agentmods.dev/skills/duqueom/ml-mlops-portfolio/rollback"><img src="https://agentmods.dev/badge/skills/duqueom/ml-mlops-portfolio/rollback.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 | $0.00027 | $0.02154 |
| Opus 5 | $0.00014 | $0.01077 |
| Sonnet 5 | $0.00005 | $0.00431 |
| Haiku 4.5 | $0.00003 | $0.00215 |
Grade B, and why
rollback scanned grade B 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 4d 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.
Unrestricted tool accessmediumExcessive agency
A wildcard tool grant or "run any command" leaves no least-privilege boundary at all.
- Bash(amtool:*) Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Bash(curl:*) How it starts
The opening of the file, as written. The whole thing — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rollback — Emergency Procedure
This is the MOST CRITICAL skill in on-call. It must be predictable, idempotent, and auditable. Execution is STOP-class: the agent produces the plan; a human approves each destructive command.
When NOT to use this skill
- Flaky alert without user impact → investigate with
concept-drift-analysisfirst. Rollback has its own blast radius. - Degraded single slice, global healthy → targeted retrain, not rollback.
- Canary already auto-aborted by Argo Rollouts → no action needed; verify and document.
- Pre-canary issues (CI failures) → use the deploy skill's abort path, not this one.
Prerequisites
Before running the plan, verify:
kubectl config current-contextpoints at the affected clusterkubectl argo rollouts versionis available OR kubectl with therolloutsubcommand for plain Deployments- GitHub token has
repo+actionsscopes for issue creation - MLflow Tracking URI is reachable (check via
mlflow experiments list)
Decision tree
Incident declared
│
┌────────────┴────────────┐
Argo Rollouts? Plain Deployment?
│ │
kubectl argo rollouts kubectl rollout undo
abort + undo deployment/{service}
│ │
└────────────┬────────────┘
│
Wait for Ready (kubectl rollout status)
│
┌────────────┴────────────┐
Model changed? Image-only change?
(MLflow revert) (done)
│
MLflow transition_model_version_stage
Previous → Production
│
POST /model/reload to each pod (or restart)
│
Silence incident-related alerts
│
Open audit issue (labels: rollback, incident)
Step 1 — Confirm the incident (AUTO, 1 min)
# Who is firing?
kubectl get prometheusrules -n monitoring -o json | \
jq '.items[].spec.groups[].rules[] | select(.alert != null) | .alert'
# Current rollout state
kubectl argo rollouts get rollout {service}-predictor -n {namespace}
# Recent deploys
kubectl rollout history deployment/{service}-predictor -n {namespace}
kubectl argo rollouts history {service}-predictor -n {namespace}
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.
- 4d ago First seen · 255 lines · 27 tokens per session scan B c145d578b0d9
rollback is a skill published in the GitHub repository DuqueOM/ML-MLOps-Portfolio (5 stars, last pushed 3d ago), licensed MIT. It adds 27 tokens to every session and 2,154 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 2 findings (unrestricted tool access, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
Cloud Security & Container Hardening
AWS/Azure/GCP security auditing, container and Kubernetes hardening, Infrastructure as Code scanning, and cloud compliance assessment.
ecs-genai
Use whenever someone runs a GPU / ML / GenAI / LLM workload on Amazon ECS: GPU on ECS, ECS GPU-optimized AMI, g4dn/g5/g6/p4/p5 on ECS, which ECS launch type for GPU, Inferentia/Trainium/Neuron on ECS, distributed training, model inference or vLLM/Triton/TGI/Ray on ECS, Capacity Blocks, GPU sharing, ASG per GPU type.…
eks-genai
Use whenever someone is building, training, fine-tuning, or serving a generative AI / LLM workload on Amazon EKS — phrased as "GPU vs Trainium/Inferentia", "vLLM on EKS", "Ray Serve / KubeRay", "distributed training on EKS", "FSx for Lustre for ML", "Karpenter for GPU", "EFA / NCCL multi-node", "DCGM / Neuron…
mlflow
Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycle platform.
setup
Configure MLflow tracing for Claude Code.
dstack-prototyping
Use with the dstack skill for model-serving work when the image, serving command, resources, backend/fleet choice, or service behavior is not proven. Guides task-first prototyping on real hardware, choosing fleets/backends that can reuse idle instances and caches, checking vLLM/SGLang sources, and verifying the final…