Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
MLOps
Deploy ML models to production with pipelines, monitoring, serving, and reproducibility best practices.
skill openclawclawhub Free
Deploy ML models to production with pipelines, monitoring, serving, and reproducibility best practices.
β¬ 0 downloads β
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, gpu.md, monitoring.md, pipelines.md, reproducibility.md, serving.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- ivangdavila
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Quick Reference
TopicFileKey TrapCI/CD and DAGspipelines.mdCoupling training/inference depsModel servingserving.mdCold start with large modelsDrift and alertsmonitoring.mdOnly technical metricsVersioningreproducibility.mdNot versioning preprocessingGPU infrastructuregpu.mdGPU request = full device
Critical Traps
Training-Serving Skew: Preprocessing in notebook β preprocessing in service β silent bugs Pandas in notebook β memory leaks in production (use native types) Feature store values at training time β serving time without proper joins GPU Memory: requests.nvidia.com/gpu: 1 reserves ENTIRE GPU, not partial memory MIG/MPS sharing has real limitations (not plug-and-play) OOM on GPU kills pod with no useful logs Model Versioning β Code Versioning: Model artifacts need separate versioning (MLflow, W&B, DVC) Training data version + preprocessing version + code version = reproducibility Rollback requires keeping old model versions deployable Drift Detection Timing: Retraining trigger isn't just "drift > threshold" β cost/benefit matters Delayed ground truth makes concept drift detection lag weeks Upstream data pipeline changes cause drift without model issues
Scope
This skill ONLY covers: CI/CD pipelines for models Model serving and scaling Monitoring and drift detection Reproducibility practices GPU infrastructure patterns Does NOT cover: ML algorithms, feature engineering, hyperparameter tuning.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
6 Docs
- SKILL.md
- gpu.md
- monitoring.md
- pipelines.md
- reproducibility.md
- serving.md