planning

A planning guide for requests that customize a machine-learning model, such as training or fine-tuning it. It identifies the needed work and arranges it into a step-by-step plan.

In plain words
What is it for?
It helps plan workflows for fine-tuning, training, reviewing data, evaluating results, or otherwise customizing a model.
Why use it?
Model customization can require several connected tasks and input files. This guide helps determine what is needed and which task should come first.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/awslabs/agent-plugins/planning
Any agent
npx skills add awslabs/agent-plugins --skill planning
Clone the repo
git clone --depth 1 https://github.com/awslabs/agent-plugins

Made for: Claude Code, Codex.

Per session 115 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,596 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00115 $0.01596
Opus 5 $0.00057 $0.00798
Sonnet 5 $0.00023 $0.00319
Haiku 4.5 $0.00012 $0.00160

Measured 2d ago against content hash b8bd4679c527, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

planning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/sagemaker-ai/skills/planning/SKILL.md · 143 lines

How it starts

The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.
  • Decide which skills in a matching workflow can be skipped.
  • Surface limitations early — if a user decision (model choice, region, evaluation method) would constrain downstream options, mention it proactively, get user feedback, and adapt the plan accordingly.

During brainstorming:

  • Workflow choice gate: Before generating any plan, determine whether the user wants the evaluate-first workflow or the direct fine-tuning workflow. If the user has explicitly chosen (e.g., "evaluate first", "skip evaluation", "already evaluated the base model"), proceed with their choice. Otherwise, present both options with brief pros/cons and ask the user to choose. Saying "fine-tune" or naming a technique alone is NOT an explicit choice to skip evaluation — the user may not know evaluate-first is an option. Do NOT present a plan until the user has chosen a path. After they choose, read ONLY the corresponding reference plan.
  • Use the Restrictions column of the contracts table to flag constraints as soon as the relevant decision is made. Examples (non-comprehensive list, check contracts table for the full picture):
    • User picks a Nova model → alert that deployment regions are limited.
    • User picks a region → alert if it conflicts with model availability.
  • If a restriction applies, check whether it requires changes to other steps in the plan.
  • Do NOT ask the user about base model selection or preferences. Model selection is handled exclusively by the model-selection skill.
  • Move to Phase 2 as soon as you can determine which skills and tools the plan needs.

Read the full file on GitHub · 143 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 143 lines · 115 tokens per session scan A b8bd4679c527

Subscribe to this mod's changes

planning is a skill published in the GitHub repository awslabs/agent-plugins (876 stars, last pushed 6d ago), licensed Apache-2.0. It adds 115 tokens to every session and 1,596 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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