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Agile Sprint Planning

Community
aj-geddes
agile-sprint-planning

Plan and execute effective sprints using Agile methodologies. Define sprint goals, estimate user stories, manage sprint backlog, and facilitate daily standups to maximize team productivity and deliver value incrementally.

Overview

Publisheraj-geddes
Repositoryuseful-ai-prompts
Skill nameagile-sprint-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 Agile Sprint 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/agile-sprint-planning .claude/skills/agile-sprint-planning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agile Sprint 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 Agile Sprint 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 Agile Sprint 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.

Agile Sprint Planning

Table of Contents

Overview

Agile sprint planning provides a structured approach to organize work into time-boxed iterations, enabling teams to deliver value incrementally while maintaining flexibility and responding to change.

When to Use

  • Starting a new sprint cycle
  • Defining sprint goals and objectives
  • Estimating user stories and tasks
  • Managing sprint backlog prioritization
  • Handling mid-sprint changes or scope adjustments
  • Preparing sprint reviews and retrospectives
  • Training team members on Agile practices

Quick Start

Minimal working example:

markdown
# Sprint Planning Checklist

## 1-2 Days Before Planning Meeting

- [ ] Groom product backlog (ensure top items are detailed)
- [ ] Update user story acceptance criteria
- [ ] Identify dependencies and blockers
- [ ] Prepare estimates from previous sprints
- [ ] Review team velocity (average story points per sprint)
- [ ] Identify team availability/absences
- [ ] Prepare sprint goals draft

## Information to Gather

- Product Owner priorities
- Team capacity (working hours available)
- Previous sprint metrics
- Upcoming holidays or interruptions
- Technical debt items to address

Reference Guides

Detailed implementations in the references/ directory:

GuideContents
Sprint Planning Meeting StructureSprint Planning Meeting Structure
Story Point EstimationStory Point Estimation
Sprint Goal DefinitionSprint Goal Definition
Daily Standup ManagementDaily Standup Management

Best Practices

✅ DO

  • Base capacity on actual team velocity from past sprints
  • Include buffer time for interruptions and support work
  • Focus sprint goal on business value, not technical tasks
  • Timeboxe planning meeting (2 hours max for 2-week sprint)
  • Include entire team in planning discussion
  • Break down large stories into smaller, manageable pieces
  • Track story points for velocity trending
  • Review and adjust estimates based on actual completion
  • Maintain consistent sprint length
  • Include retrospective improvements in planning

❌ DON'T

  • Plan for 100% capacity utilization
  • Skip story grooming before planning meeting
  • Add stories after sprint starts (unless emergency)
  • Let one person estimate for entire team
  • Use story points as employee performance metrics
  • Ignore team velocity trends
  • Plan without clear sprint goal
  • Force stories into sprints to match capacity numbers
  • Skip sprint planning to save time
  • Use planning poker results as final estimate without discussion

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 Agile Sprint Planning AI skill do?

Plan and execute effective sprints using Agile methodologies. Define sprint goals, estimate user stories, manage sprint backlog, and facilitate daily standups to maximize team productivity and deliver value incrementally.

Why use Agile Sprint Planning on TypingMind?

Because you install it once and use it with any model. Agile Sprint 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 Agile Sprint Planning in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aj-geddes/useful-ai-prompts/tree/main/skills/agile-sprint-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 Agile Sprint 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 Agile Sprint Planning?

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

Is the Agile Sprint 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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