风险评分

100/100 (Very Low)

OpenClaw: benign
VirusTotal: benign
StaticScan: clean

Ml Ops

作者: clawkk
Slug:ml-ops
版本:1.0.0
更新时间:2026-03-25 08:51:47
风险信息

OpenClaw: benign

查看 OpenClaw 分析摘要
This is a documentation-only MLOps workflow guide; it does not request credentials, install code, or perform actions, and its requirements align with its stated purpose.

VirusTotal: benign VT 报告

静态扫描: clean

No suspicious patterns detected.
README

README 未提供

文件列表

无文件信息

下载
下载官方 ZIP
原始 JSON 数据
{
    "latestVersion": {
        "_creationTime": 1774397569946,
        "_id": "k978yyj7jjvhyb0ptacg2wg7pn83j1zr",
        "changelog": "- Initial release of the \"ml-ops\" skill featuring a comprehensive MLOps workflow.\n- Covers reproducible training, experiment tracking, packaging, deployment, monitoring (drift, performance), governance, and rollback.\n- Introduces six workflow stages: problem & risk class, data & reproducibility, training & evaluation, packaging & deployment, monitoring & feedback, governance & rollback.\n- Provides practical triggers, stage exit conditions, and a final review checklist.\n- Includes tips for preventing common pitfalls and adapting practices for LLM products or small teams.",
        "changelogSource": "auto",
        "createdAt": 1774397569946,
        "version": "1.0.0"
    },
    "owner": {
        "_creationTime": 0,
        "_id": "s170g5yz1q3ksjnn4gz6v24af983h1mh",
        "displayName": "clawkk",
        "handle": "clawkk",
        "image": "https:\/\/avatars.githubusercontent.com\/u\/265748372?v=4",
        "kind": "user",
        "linkedUserId": "kn76s36x99btbdct5mxefxwmth8310m2"
    },
    "ownerHandle": "clawkk",
    "skill": {
        "_creationTime": 1774397569946,
        "_id": "kd71ecz9bszr0s73mm16nmh17n83k2hw",
        "badges": [],
        "createdAt": 1774397569946,
        "displayName": "Ml Ops",
        "latestVersionId": "k978yyj7jjvhyb0ptacg2wg7pn83j1zr",
        "ownerPublisherId": "s170g5yz1q3ksjnn4gz6v24af983h1mh",
        "ownerUserId": "kn76s36x99btbdct5mxefxwmth8310m2",
        "slug": "ml-ops",
        "stats": {
            "comments": 0,
            "downloads": 4,
            "installsAllTime": 0,
            "installsCurrent": 0,
            "stars": 0,
            "versions": 1
        },
        "summary": "Deep MLOps workflow—reproducible training, experiment tracking, packaging, deployment, monitoring (drift, performance), governance, and rollback for ML. Use...",
        "tags": {
            "latest": "k978yyj7jjvhyb0ptacg2wg7pn83j1zr"
        },
        "updatedAt": 1774399907053
    }
}