MLOps
v1.0.0Deploy ML models to production with pipelines, monitoring, serving, and reproducibility best practices.
⭐ 4· 680·2 current·2 all-time
byIván@ivangdavila
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
The name/description (CI/CD, serving, monitoring, reproducibility, GPU patterns) matches the SKILL.md and the companion markdown files. All referenced tools (MLflow, W&B, DVC, Triton, Airflow, etc.) are reasonable given the topic.
Instruction Scope
The SKILL.md and supporting files are guidance and examples (YAML, bash snippets) focused on pipeline/serving/monitoring best practices. They do not instruct the agent to read arbitrary files, access unexpected environment variables, contact unknown endpoints, or exfiltrate data. Mentions of Slack/on-call pages and hosted tools are contextual and not tied to any required credentials in the skill.
Install Mechanism
No install spec and no code files — instruction-only — so nothing is downloaded or written to disk by the skill itself.
Credentials
The skill declares no required environment variables, credentials, or config paths. References to external services (MLflow, W&B, Slack) are expected for MLOps guidance but would require separate credentials only if you choose to integrate those tools.
Persistence & Privilege
Skill is not always-enabled and uses platform defaults for invocation. It does not request persistent installation, modify other skills, or claim system-wide privileges.
Assessment
This skill is high-level, instruction-only guidance for MLOps and appears coherent with its description. Because it has no install steps and requests no secrets, it doesn't itself introduce credential or exfil risks. Before using it in an automated agent: (1) verify the skill's provenance since the source is unknown, (2) be cautious if you provide the agent with real credentials for MLflow/W&B/Slack — those are not required by the skill but would be needed for real integrations, and (3) treat the advice as best-practice guidance rather than executable automation; if you let the agent perform actions (deploy, run pipelines), review the exact commands it will execute and any credentials you supply. If you want stronger assurance, ask the publisher for a homepage or repo so you can audit changes over time.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.
Runtime requirements
🤖 Clawdis
OSLinux · macOS · Windows