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Capacity Planning

Community
aj-geddes
capacity-planning

Analyze team capacity, plan resource allocation, and balance workload across projects. Forecast staffing needs and optimize team utilization while maintaining sustainable pace.

Overview

Publisheraj-geddes
Repositoryuseful-ai-prompts
Skill namecapacity-planning
Stars
340
Forks
55
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by aj-geddes on GitHub. Read the source before you install it.

Installation

Install the Capacity Planning AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aj-geddes/useful-ai-prompts.git /tmp/useful-ai-prompts
mkdir -p .claude/skills
cp -r /tmp/useful-ai-prompts/skills/capacity-planning .claude/skills/capacity-planning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Capacity Planning in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Capacity Planning on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Capacity Planning is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Capacity Planning

Table of Contents

Overview

Capacity planning ensures teams have sufficient resources to deliver work at sustainable pace, prevents burnout, and enables accurate commitment to stakeholders.

When to Use

  • Annual or quarterly planning cycles
  • Allocating people to projects
  • Adjusting team size
  • Planning for holidays and absences
  • Forecasting resource needs
  • Balancing multiple projects
  • Identifying bottlenecks

Quick Start

Minimal working example:

python
# Team capacity calculation and planning

class CapacityPlanner:
    # Standard work hours per week
    STANDARD_WEEK_HOURS = 40

    # Activities that reduce available capacity
    OVERHEAD_HOURS = {
        'meetings': 5,           # standups, 1-on-1s, planning
        'training': 2,           # learning new tech
        'administrative': 2,     # emails, approvals
        'support': 2,            # helping teammates
        'contingency': 2         # interruptions, emergencies
    }

    def __init__(self, team_size, sprint_duration_weeks=2):
        self.team_size = team_size
        self.sprint_duration_weeks = sprint_duration_weeks
        self.members = []

    def calculate_team_capacity(self):
        """Calculate available capacity hours"""
        # Base capacity
        base_hours = self.team_size * self.STANDARD_WEEK_HOURS * self.sprint_duration_weeks

// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

GuideContents
Capacity AssessmentCapacity Assessment
Capacity Planning TemplateCapacity Planning Template
Resource LevelingResource Leveling
Capacity ForecastingCapacity Forecasting

Best Practices

✅ DO

  • Plan capacity at 85% utilization (15% buffer)
  • Account for meetings, training, and overhead
  • Include known absences (vacation, holidays)
  • Identify skill bottlenecks early
  • Balance workload fairly across team
  • Review capacity monthly
  • Adjust plans based on actual velocity
  • Cross-train on critical skills
  • Communicate realistic commitments to stakeholders
  • Build contingency for emergencies

❌ DON'T

  • Plan at 100% utilization
  • Ignore meetings and overhead
  • Assign work without checking skills
  • Create overload with continuous surprises
  • Forget about learning/training time
  • Leave capacity planning to last minute
  • Overcommit team consistently
  • Burn out key people
  • Ignore team feedback on workload
  • Plan without considering absences

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Capacity Planning AI skill do?

Analyze team capacity, plan resource allocation, and balance workload across projects. Forecast staffing needs and optimize team utilization while maintaining sustainable pace.

Why use Capacity Planning on TypingMind?

Because you install it once and use it with any model. Capacity Planning is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Capacity Planning in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aj-geddes/useful-ai-prompts/tree/main/skills/capacity-planning. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Capacity Planning?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Capacity Planning?

As many as you like. As long as a model supports skills, you can use Capacity Planning with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Capacity Planning AI skill free?

Yes. It is published on GitHub by aj-geddes under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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