aws-ai-ml

Selects, deploys, and customizes AI models on Amazon SageMaker. Training or Processing jobs, fine-tuning (SFT/DPO/RLVR/RLAIF), model selection, dataset preparation, evaluation, SageMaker or Bedrock deployment, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, choosing which base model to customize, fine-tune, or deploy from SageMaker JumpStart or Hub, SageMakerPublicHub, or the SageMaker public model catalog, transforming or validating training data, evaluating model quality, deploying or optimizing endpoints, configuring IAM/S3 for training, or managing SageMaker Managed MLflow. Use for endpoint health, failures, latency, logs, metrics, errors. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.

Agent Skill from aws/agent-toolkit-for-aws

Copilot Studio

Passes documented load rules

No documented load rule failed.

Microsoft 365 Copilot Preview, Frontier tenants only

Needs changes

Fails: Directory depth is at most 3 (M365-DEPTH)
deepest file is 4 folders down
rule · Microsoft 365 Copilot: Support matrix

Warnings (don't change the verdict):

Warning: No sign of network access, package installs or authenticated calls (M365-SANDBOX)
scripts: references/finetuning/code_templates/dpo.py (pip/uv install); references/finetuning/code_templates/rlaif_builtin.py (pip/uv install); references/finetuning/code_templates/rlaif_custom_prompt.py (pip/uv install); references/finetuning/code_templates/rlvr.py (pip/uv install); references/finetuning/code_templates/sft.py (pip/uv install); …
rule · Microsoft 365 Copilot: Script execution sandbox

An agent can have at most 8 skills. Custom skills aren't available in tenants that use Microsoft Purview Information Barriers.

Compatible isn't the same as safe. These verdicts only say whether a skill's files follow Microsoft's documented load rules. The checker reads files and never runs them, and it doesn't review what a skill or its scripts actually do. Read a skill's instructions and scripts before you add it to an agent.

Standards signals

Meets the agentskills.io required fields.

Frontmatter	Present and parsed Spec: frontmatter
name	Meets the spec (1-64 chars, a-z 0-9 hyphens) Spec: name
description	Present, 1-1,024 characters Spec: description
compatibility	Absent or under 500 characters Spec: compatibility
Name matches folder	Yes (`aws-ai-ml`) Spec: name

Observed from the public SKILL.md at the commit above on 2026-10-23. Not a certification: AAIF and the Agent Skills project don't certify skills. Validate your own skill with skills-ref validate.

Source repo	aws/agent-toolkit-for-aws
Path	skills/core-skills/aws-ai-ml/SKILL.md
Commit checked	`9ab50636d125`
Repo license	Apache-2.0
Files	118
Scripts	37 (37 scanned for the sandbox heuristic)
Instructions	7,273 characters
Deepest folder	4 below the skill folder
Installs (skills.sh)	3,394
Rules version	2026-10-22

Re-check the latest version How skill checks work

Is this wrong or out of date? Email us. Skills data built 2026-10-23; rules as of 2026-10-22.

Rule-based checks of public files against Microsoft's documented load rules, not tested in Copilot Studio or Microsoft 365 Copilot. Independent project, not affiliated with Microsoft.