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onboard-marin

Check a new internal Marin developer’s local environment and access to development services. Distinguish setup problems from missing permissions, repair authorized local issues, and provide a checklist of verified capabilities and remaining access requests.

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Verify or complete a new internal Marin developer's local setup and access to GitHub, GCP, Iris, Weights & Biases, Hugging Face, and optional CoreWeave storage. Use when a team member asks to onboard, validate onboarding, or diagnose missing development access.

实测评分 未实测

查看安装方法

适用场景

  • Setting up a new Marin developer
  • Validating a completed development setup
  • Diagnosing missing service access
  • Checking access before operating a Pulumi stack

包含的文件(1)

  • SKILL.md4.2 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Onboard a Marin developer

Establish which parts of the developer environment are ready, fix local setup when safe, and identify each missing external grant. Keep secrets out of command output and the final report.

Sources

Use these as the current sources of truth:

  • Local installation: docs/tutorials/installation.md.
  • Development workflow: docs/dev-guide/contributing.md.
  • Iris authentication and access checks: lib/iris/OPS.md and the use-iris skill.
  • CoreWeave credentials and routing: docs/tutorials/cloud-gpu.md.
  • Pulumi operator grants and state access: infra/pulumi/README.md, the add-grant skill, and the review-grant skill.

Do not copy procedures from infra/README.md; it is an infrastructure index.

Verify

Start with read-only checks. Report each area as ready, missing access, missing local setup, or not checked.

  1. Confirm the command is running from a Marin checkout and inspect the working tree without changing user work.
  2. Check the required local tools and Python version. Check dependency and pre-commit setup against the installation and contributing guides. Install or repair local dependencies when the user's onboarding request authorizes it.
  3. Check GitHub authentication and repository push permission without pushing a branch.
  4. Check the active GCP account, the hai-gcp-models project, Application Default Credentials, and read access to gs://marin-us-central2. Confirm the current principal has projects/hai-gcp-models/roles/marindev with a filtered IAM query that prints only the matching role name. Do not print credential contents or the complete project policy.
  5. When the user will operate the marin GCP Pulumi stack:
    • In addition to the marindev check above, confirm the current principal has projects/hai-gcp-models/roles/marinPulumiAdmin with the same filtered IAM query.
    • Check that the Pulumi CLI and repository deploy dependencies are present. Run pulumi -C infra/pulumi stack export --stack marin >/dev/null to verify state access without printing state contents.
    • Report CoreWeave kubeconfig access separately when the user will operate a CoreWeave stack. Do not run pulumi up as an onboarding check. Do not grant roles or mutate live IAM unless the user asks for the grant workflow.
  6. Check Iris authentication and read-only cluster status. Use iris login only for an interactive human session; let the browser or headless login flow request the human's input.
  7. Check whether WANDB_API_KEY and HF_TOKEN are present without printing their values. When useful, perform a read-only identity check with the service's CLI and confirm access to the marin-community W&B entity. Do not persist a token outside the user's chosen credential store.
  8. Check CoreWeave object-storage access only when the user needs to inspect GPU job outputs. Do not treat storage credentials as proof of GPU scheduling access; Iris controls compute access separately.

Distinguish local configuration failures from permissions that a maintainer must grant. GCP project access, Iris IAP access, GitHub access, Weights & Biases, Hugging Face, Pulumi operator access, and CoreWeave storage are independent.

Smoke tests

Run local import or CPU checks when they are cheap and do not download a large dataset. A remote Iris job changes shared state: submit one only when the user explicitly authorizes the smoke job. Keep it CPU-only, small, and bounded. Never request a GPU or TPU during onboarding.

Do not start, stop, restart, deploy, or otherwise mutate a shared cluster.

Report

Give the user a compact checklist of verified capabilities and remaining actions. Include the failing command category and error summary without secret values. Distinguish grants the user can request from local fixes. Do not edit IAM data or file a grant request unless the user asks.

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同一仓库的技能

Compare Zephyr changes using the same prepared dataset and matching execution settings. Analyze Finelog metrics for each processing stage to distinguish CPU efficiency, elapsed time, and peak memory changes, then report improvements, regressions, or tradeoffs.

中文简介正在准备中。当前显示英语简介。

  • Compare Zephyr performance before and after a PR
  • Assess speed gains against additional CPU usage
  • Check peak memory and out-of-memory regressions
marin-community/marin40112026年10月24日 更新

Register Hugging Face datasets for Marin by inspecting their structure without downloading the full dataset. Identify text fields, configurations, and data splits, then add a dataset module and validate the mapping with a sample.

中文简介正在准备中。当前显示英语简介。

  • Registering a Hugging Face dataset for Marin
  • Inspecting text fields without a full dataset download
  • Checking configurations and data splits
marin-community/marin40112026年10月24日 更新

add-grant

免费

Turn a specified GCP access request into a reviewable Pulumi change and pull request. Select the narrowest suitable role and encrypt personal email identities, covering resource permissions and Cloud Run viewer access through IAP, an access gateway.

中文简介正在准备中。当前显示英语简介。

  • Prepare a request for GCS bucket read access
  • Add a viewer to a Cloud Run service protected by IAP
  • Prepare service account impersonation permissions
marin-community/marin40112026年10月24日 更新

Add or update Pallas/Mosaic kernels, specialized computation routines for JAX. The skill covers reference implementations, output and gradient checks, public wrappers, fallback behavior, performance benchmarks, and hardware-specific tuning when requested.

中文简介正在准备中。当前显示英语简介。

  • Adding a Pallas kernel for a JAX computation
  • Checking outputs and gradients against a reference
  • Maintaining backend selection and fallback behavior
marin-community/marin40112026年10月24日 更新

Try model or training changes in a copied Grug or Grugformer experiment, document the differences, and bring successful changes back into the base template while removing superseded experiment code and checking behavior.

中文简介正在准备中。当前显示英语简介。

  • Try changes to attention or masking
  • Experiment with optimizers or training loops
  • Bring successful changes into the base template
marin-community/marin40112026年10月24日 更新

Organize Marin experiment reports into a maintained index, or archive explicitly named older experiments using lasting tags and issue references. Existing conclusions and canonical links are preserved so experiment records remain traceable.

中文简介正在准备中。当前显示英语简介。

  • Organizing an index of experiment reports
  • Consolidating canonical report entries
  • Archiving named legacy experiments
marin-community/marin40112026年10月24日 更新

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