How do I install the Package Private Ml Artifacts skill?

Run npx skillmds@latest add bastos/package-private-ml-artifacts in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Package Private Ml Artifacts safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Package Private Ml Artifacts?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Package Private Ml Artifacts free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Package Private Ml Artifacts?

bastos (@bastos) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Package Private Ml Artifacts by bastos

Name: Package Private Ml Artifacts
Rating: 4.666666666666667 (3 reviews)
Author: bastos

Package Private Ml Artifacts

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.

bastos a1d206c 3 files · 4.8 KB Updated Sep 10, 2026

File contents

Package Private ML Artifacts

Preserve reproducibility while keeping credentials, restricted sources, and large assets out of source control.

Confirm authorization and scope

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.

Build a manifest

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.

Upload and verify

Create or verify the private destination before transfer.
Upload the manifest with the artifacts.
Record destination repository, revision/commit, upload command, and timestamp.
Re-list or download enough data to prove remote sizes and hashes match.
Keep local evidence until remote verification succeeds.

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.

---name: package-private-ml-artifactsdescription: 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.---# Package Private ML ArtifactsPreserve reproducibility while keeping credentials, restricted sources, and large assets out of source control.## Confirm authorization and scopeObtain 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.## Build a manifestRun:```shpython 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.## Upload and verify1. Create or verify the private destination before transfer.2. Upload the manifest with the artifacts.3. Record destination repository, revision/commit, upload command, and timestamp.4. Re-list or download enough data to prove remote sizes and hashes match.5. Keep local evidence until remote verification succeeds.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.

Frequently asked questions