Agent skill · Data & Analytics

planning

Discovers user intent and generates a structured, step-by-step plan for model customization workflows. This skill must always be activated alongside any other skill when the user's request relates to model customization — including fine-tuning, training, building, customizing, reviewing data, or getting advice on approach, regardless of domain. Do not skip this skill even if the immediate ask is narrow (e.g., reviewing data format or a single workflow step), because planning discovers the full scope of work needed. Also activate when the user wants to resume, continue, or modify an existing pl

Amazon Web Services - Labs12,649★ · +18/wk · 2 repos on radarProfile →
claude-codeApache-2.0
Install
npx skills add awslabs/agent-plugins --skill planning --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 5
SKILL.md size: 7 KB
Bundled scripts: none
Version: 2.0.0
Path: plugins/sagemaker-ai/skills/planning/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 850
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

## Principles - **One question at a time.** Each question should resolve a branching decision in the plan. Avoid generic or out-of-domain questions. - **Surface constraints early.** If a user decision would constrain downstream options, flag it before the plan is finalized. - **Keep plans short.** Only include tasks that are necessary for the user's stated goal. - **Don't ask what you already know.** Check conversation history and project files before asking the user. --- ## Phase 1: Brainstorming **Goal:** Understand what the user wants to accomplish and identify which skills belong in the plan. Read `references/input-output-contracts.md`, `references/model-customization-plan.md`, and `references/evaluate-first-plan.md` to: - Identify which skills could be relevant to the user's stated goal. - Check whether the user has the necessary input artifacts for each skill. If not, find the skills that generate those inputs and add them first. - Order skills to allow a smooth transition from one to the next and avoid dead ends. - Check if a recommended workflow matches the user's needs. If not, assess what modifications are needed and verify they are possible against the contracts table. -

What's inside
Steps it walks through
  1. Principles
  2. Phase 1: Brainstorming
  3. Phase 2: Plan Generation
  4. Phase 3: Plan Iteration
  5. Execution
  6. Plan Completion
  7. References
Ships with 4 files
  • references/evaluate-first-plan.md
  • references/input-output-contracts.md
  • references/model-customization-plan.md
  • references/skill-routing-constraints.md
More from agent-plugins
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About this skill
What does the planning skill do?

Discovers user intent and generates a structured, step-by-step plan for model customization workflows. This skill must always be activated alongside any other skill when the user's request relates to model customization — including fine-tuning, training, building, customizing, reviewing data, or getting advice on approach, regardless of domain. Do not skip this skill even if the immediate ask is narrow (e.g., reviewing data format or a single workflow step), because planning discovers the full scope of work needed. Also activate when the user wants to resume, continue, or modify an existing pl

How do I install it?

Run `npx skills add awslabs/agent-plugins --skill planning --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From awslabs/agent-plugins, a repository with 850 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going