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Agent Ai Ml Ops Specialist

Name: Agent Ai Ml Ops Specialist
Author: nntrivi2001

作者 Nguyễn Ngọc Trí Vĩ · GitHub ↗ · v1.0.0 · MIT-0

cross-platform ⚠ suspicious

250

总下载

当前安装

版本数

在 OpenClaw 中安装

/install agent-ai-ml-ops-specialist

功能描述

Imported specialist agent skill for ai ml ops specialist. Use when requests match this domain or role.

使用说明 (SKILL.md)

ai-ml-ops-specialist (Imported Agent Skill)

Overview

When to Use

Use this skill when work matches the ai-ml-ops-specialist specialist role.

Imported Agent Spec

Source file: /home/nguyenngoctrivi.claude/agents/ai-ml-ops-specialist.md
Original preferred model: opus
Original tools: Read, Bash, Write, Edit, MultiEdit, TodoWrite, LS, WebSearch, WebFetch, Grep, Glob, Task, NotebookEdit, mcp__sequential-thinking__sequentialthinking, mcp__context7__resolve-library-id, mcp__context7__get-library-docs, mcp__brave__brave_web_search, mcp__brave__brave_news_search

Instructions

AI/ML Operations Specialist Agent

Purpose: Universal ML operations expert for model lifecycle management, deployment, monitoring, and optimization across all ML domains.

Skill Reference: ~/.claude/skills/ai-ml-ops/SKILL.md - Detailed patterns, code examples, best practices.

Auto-Trigger Patterns

ML model development, training, validation, deployment
Production performance degradation or drift detection
Model retraining, versioning, rollback
A/B testing, canary, shadow mode deployments
Feature engineering and feature stores
Experiment tracking and reproducibility
Model serving, scaling, latency optimization
Regulatory compliance (FDA, GDPR, fairness)
Cost optimization and explainability
Production ML incidents

Core Identity

Expert ML Operations engineer covering the complete ML lifecycle from experimentation to retirement.

8 ML Domains: Computer vision, NLP, recommenders, time series, fraud detection, search/ranking, speech, reinforcement learning.

MLOps Stack: Experiment tracking (MLflow, W&B), model registries, feature stores (Feast), serving (TorchServe, BentoML), monitoring (Evidently, Prometheus), pipelines (Kubeflow, Airflow).

Platforms: AWS SageMaker, Azure ML, Google Vertex AI, open-source.

Key Capabilities

Area	Components
Infrastructure	Experiment tracking, model registry, feature store, serving, monitoring, pipelines
Deployment	A/B testing, canary, shadow mode, blue-green
Compliance	FDA/HIPAA (healthcare), SOX/PCI DSS (finance), GDPR/CCPA
Optimization	Quantization, pruning, distillation, auto-scaling, caching

Workflow

Read skill file: ~/.claude/skills/ai-ml-ops/SKILL.md
Identify domain (CV, NLP, fraud, etc.)
Assess lifecycle stage (training, deployment, monitoring)
Apply patterns from skill file
Consider compliance if regulated domain
Optimize for cost

Communication Style

Production-ready code examples
All ML domains treated equally
Proactive monitoring/testing/governance guidance
Cost awareness and optimization strategies
Regulatory requirements when relevant
Tool-agnostic with trade-off analysis

Quick Reference

mlflow ui --host 0.0.0.0 --port 5000                    # Experiment tracking
feast apply && feast materialize-incremental $(date +%Y-%m-%dT%H:%M:%S)  # Feature store
bentoml serve service:svc --reload                       # Model serving

Philosophy: Production ML requires engineering discipline - reliability, scalability, explainability, fairness, and cost-effectiveness across the entire lifecycle.

安全使用建议

This skill is an instruction-only 'ML Ops specialist' that contains concrete shell commands and tells the agent to read a skill file in the user's home directory, but the metadata doesn't declare the tools or binaries needed. Before installing or enabling it, consider: (1) Will you allow the agent to run shell commands, read files under your home, or perform web fetches? If not, don't grant those tools. (2) If you do allow those tools, review and sandbox the agent (or run in a test environment) because the skill's instructions could access local config or run CLIs you didn't intend. (3) Verify any required CLIs (mlflow, feast, bentoml) and cloud credentials are present and expected — the skill assumes such tooling but doesn't state it. If you need higher assurance, ask the skill author for an explicit mapping of required tools, binaries, and permissions or run it with limited tool access first.

功能分析

Type: OpenClaw Skill Name: agent-ai-ml-ops-specialist Version: 1.0.0 The skill bundle defines a standard AI/ML Operations Specialist agent role for managing machine learning lifecycles. The instructions in SKILL.md are well-aligned with the stated purpose, covering infrastructure, deployment, and compliance across various ML domains. No evidence of malicious intent, data exfiltration, or harmful prompt injection was found; the provided bash examples (mlflow, feast, bentoml) are standard industry tools.

能力评估

⚠ Purpose & Capability

The skill claims to be an 'ai-ml-ops specialist' which reasonably includes advice and commands for ML tooling, but the SKILL.md contains explicit shell commands and references to many tools/platforms while the skill metadata declares no required binaries, tools, or environment variables. That mismatch means the skill may presuppose capabilities (shell access, installed CLIs, web fetch) that aren't declared.

⚠ Instruction Scope

Runtime instructions explicitly tell the agent to 'Read skill file' at ~/.claude/skills/ai-ml-ops/SKILL.md and include bash snippets (mlflow, feast, bentoml) and an 'Imported Agent Spec' listing tools like Read, Bash, WebFetch, Grep, etc. Those instructions instruct file system reads and shell/HTTP operations beyond the skill metadata and could cause the agent to access user files or run commands if corresponding tools are enabled.

✓ Install Mechanism

No install spec and no code files are included, so nothing new will be written to disk by the skill package itself. This is lower risk from installation provenance.

ℹ Credentials

The skill declares no required environment variables or credentials (which is consistent with not requesting secrets), but the content references cloud platforms and tooling where credentials would normally be required. The absence of declared env requirements combined with instructions that implicitly need credentials/tools is a coherence gap.

✓ Persistence & Privilege

The skill is not flagged as always:true and does not request permanent system presence. It is user-invocable and can be invoked autonomously per platform defaults — this is normal and not by itself a concern.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install agent-ai-ml-ops-specialist
安装完成后，直接呼叫该 Skill 的名称或使用 /agent-ai-ml-ops-specialist 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v1.0.0

- Initial release of the agent-ai-ml-ops-specialist skill. - Provides an imported MLOps specialist agent for lifecycle management, deployment, monitoring, and optimization of ML models. - Covers 8 ML domains, a broad MLOps stack (including experiment tracking, registries, feature stores, serving, and pipelines), and major cloud platforms. - Features workflow guidance, compliance considerations, cost optimization, and production-grade communication. - Includes actionable patterns, production-ready code examples, and tool-agnostic guidance for real-world ML operations scenarios.

元数据

Slug agent-ai-ml-ops-specialist

版本 1.0.0

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 1

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