From sagemaker-ai
Discovers user intent and generates structured plans for SageMaker AI model customization workflows: fine-tuning, data preparation, evaluation, deployment. Handles plan iteration and mid-execution alterations.
How this skill is triggered — by the user, by Claude, or both
Slash command
/sagemaker-ai:planningThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Implements dynamic planning for flexible user journeys. Instead of forcing a rigid workflow, this skill discovers what the user wants, proposes a plan, and adapts as needed.
Implements dynamic planning for flexible user journeys. Instead of forcing a rigid workflow, this skill discovers what the user wants, proposes a plan, and adapts as needed.
Goal: Understand what the user wants to accomplish.
First message rules:
During brainstorming:
Goal: Propose a structured plan for the user to review.
Generate a plan as a numbered list of tasks. Each task has:
Format:
Based on what you've described, here's what I propose:
1. ⬜ **[Task Name]** — [What happens]. *(Skill: [skill-name])*
2. ⬜ **[Task Name]** — [What happens]. *(Skill: [skill-name])*
3. ⬜ **[Task Name]** — [What happens]. *(Skill: [skill-name])*
Does this plan look right, or would you like to change anything?
Rules for plan generation:
references/skill-routing-constraints.md and validate the plan against it.When the user approves the plan, write it to PLAN.md using the following format. Save the file under the project directory structure defined by the directory-management skill, if available.
# Plan
1. ⬜ **[Task Name]** — [Description]. _(Skill: [skill-name])_
2. ⬜ **[Task Name]** — [Description]. _(Skill: [skill-name])_
3. ⬜ **[Task Name]** — [Description]. _(Skill: [skill-name])_
Status indicators:
Update PLAN.md whenever a task's status changes.
Goal: Refine the plan until the user approves it.
Once the plan is approved:
PLAN.md to 🔄 (In Progress).PLAN.md to ✅ (Completed), then briefly confirm completion and move to the next task.When all tasks in the plan are done:
"We've completed everything in the plan. What would you like to do next?"
This re-enters Phase 1 (Brainstorming) for a new goal. There is no terminal state — the conversation continues as long as the user wants.
Always load the corresponding reference plan based on the customer intent to learn about what a typical plan looks like, and then adjust based on customer's needs.
references/model-customization-plan.md — A typical end-to-end model customization/finetuning plan for reference when generating plans.references/skill-routing-constraints.md — Mandatory inclusion rules, ordering constraints, and skill boundary rules. Always consult when generating or modifying a plan.npx claudepluginhub awslabs/agent-plugins --plugin sagemaker-aiGenerates validated, runnable implementation plans for ML pipelines, architecture designs, and multi-step projects grounded in official framework documentation.
Guides selection of a base model and fine-tuning technique (SFT, DPO, RLVR) by querying SageMaker Hub. Use when choosing a model or technique for fine-tuning.
Plans architecture for new Claude Code skills via domain discovery, use case decomposition, complexity tier assessment, sub-skill breakdown, and file structure design.