技能创建器
skill-creator
创建、改进和评估自定义技能,把专业流程转化为可重复使用的 AI 能力。
package-private-ml-artifacts【数据与研究】Inventories, hashes, scans, and verifies datasets, adapters, checkpoints, logs, and evaluation evidence before an explicitly approved private upload. Use when ephemeral jobs or handoffs require durable ML artifacts without exposing credentials or unapproved data.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
Preserve reproducibility while keeping credentials, restricted sources, and large assets out of source control.
Obtain explicit approval before uploading datasets, weights, adapters, checkpoints, or other large artifacts. Confirm the destination is private and list exactly what will be uploaded. Never infer that permission to train also grants permission to publish artifacts.
Classify each artifact by source, license, sensitivity, retention need, and whether it belongs in Git, private object storage, or a private model/dataset repository.
Run:
python scripts/build_artifact_manifest.py experiment-output \
--output experiment-output/artifact-manifest.json \
--max-file-mb 5000
The script records relative paths, byte sizes, and SHA-256 hashes and fails on suspicious credential filenames/content or oversized files. Review flagged paths; do not weaken the scan merely to complete an upload.
Include reproduction inputs: commands, configuration, revisions, seed, packages, hardware, source/split manifests, logs, raw outputs, normalized results, validators, and reports. Do not include environment files, tokens, caches, or unrelated workspace data.
Report omissions and why they were omitted. Do not call an upload complete when only a mutable branch name, partial logs, or unverified files remain.