Personal Productivity

Energy Based Workload and Capacity Planner

Plan work around energy, cognitive load, deadlines, recovery, commitments, buffers, and realistic weekly capacity.

Last updated Jul 12, 2026
FreeClaudeChatGPT
TL;DR

Energy Based Workload and Capacity Planner is a free AI skill for personal productivity. Plan work around energy, cognitive load, deadlines, recovery, commitments, buffers, and realistic weekly capacity. It works with Claude, ChatGPT and is ready to use out of the box.

Download Skill.md Package

About this skill

Plan work around energy, cognitive load, deadlines, recovery, commitments, buffers, and realistic weekly capacity. It applies a structured professional workflow, makes assumptions visible, and produces implementation-ready guidance with ownership, edge cases, controls, and validation.

What it does

The skill analyzes objectives, users, workflows, evidence, dependencies, constraints, and risks; converts them into a practical operating model; and produces explicit rules, responsibilities, exceptions, controls, tests, and rollout guidance.

What is included

  • Capacity context
  • Commitment inventory
  • Energy profile
  • Task-load model
  • Weekly allocation
  • Deep and shallow work
  • Buffers and limits
  • Recovery plan

How to use it

1. Download the energy-based-workload-and-capacity-planner-SKILL.md file
2. Upload it to your AI, operational, or project workspace
3. Provide the current process, users, goals, evidence, and constraints
4. Add ownership, approval, risk, and implementation requirements
5. Use the output for design, review, testing, and rollout

Examples

Example input
Create a weekly capacity model for a student-founder balancing university, product work, client tasks, language learning, and recovery.
Example output
A complete professional deliverable with context, decisions, ownership, workflows, edge cases, risks, controls, validation criteria, and an implementation roadmap.

FAQ

What is this skill for?
It creates a professional energy based workload and capacity planner deliverable.
Will it invent facts or results?
No. Missing evidence, assumptions, and unknowns are labeled clearly.
Can it improve an existing process?
Yes. It can audit the current process before redesigning it.
Does it include edge cases?
Yes. Exceptions, failures, escalation, and fallback behavior are included.
Can it assign ownership?
Yes. Roles, decision rights, and handoffs are made explicit.
How is this different from generic advice?
It produces a structured, testable, implementation-ready system.

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