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Dev Planning

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codeaholicguy
dev-planning

AI DevKit · Planning phase guidance for creating and reconciling feature task plans. Use when the user wants to create an implementation plan, update planning docs, mark task progress, capture blockers or new tasks, or run dev-lifecycle planning work.

Overview

Publishercodeaholicguy
Repositoryai-devkit
Skill namedev-planning
Stars
1.6K
Forks
252
Bundled files
1
LicenseApache-2.0
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.

  • 1 bundled files

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

  • Open source

    Published by codeaholicguy on GitHub. Read the source before you install it.

Installation

Install the Dev 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/codeaholicguy/ai-devkit.git /tmp/ai-devkit
mkdir -p .claude/skills
cp -r /tmp/ai-devkit/skills/dev-planning .claude/skills/dev-planning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dev 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 Dev 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 Dev 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.

Dev Planning

Run planning creation and reconciliation for configured AI docs features. Before changing docs, propose the concrete plan for this phase and wait for user approval unless the user already approved the exact phase plan.

Phase Contract

  1. Run npx ai-devkit@latest lint before phase work.
  2. If working on a named feature, run npx ai-devkit@latest lint --feature <name>.
  3. Read existing configured planning, implementation, and testing docs before changes. Resolve paths through lint --feature instead of assuming docs/ai.
  4. Keep task creation and updates traceable to requirements, design, testing scenarios, completed work, blockers, or newly discovered scope.
  5. If parent dev-lifecycle established usable task tracing, emit planning phase, progress, blocker/scope, and next-step events per task.

Create Initial Plan

Use for Phase 4 after requirements, design, and initial testing docs exist.

  1. Run npx ai-devkit@latest lint --feature <name> and identify the planning doc path it validates. If docs init-feature just ran, use the returned planning path as authoritative.
  2. Read requirements, design, and testing docs for the feature.
  3. Convert goals, user stories, design components, API/data changes, migration needs, and testing scenarios into implementation tasks.
  4. Group tasks by milestone or logical sequence.
  5. For each task, include outcome, dependencies, validation evidence, and related testing scenarios.
  6. Verify every test-plan scenario has at least one implementation task.
  7. Add risks, blockers, sequencing notes, and likely follow-up checks.
  8. Update the planning doc with the initial ordered task list.
  9. If task tracing is available, record plan progress and next implementation step per task.

Next: dev-implementation.

Update Planning

Use for Phase 6. Auto-trigger this phase after completing any task in dev-implementation.

  1. Run npx ai-devkit@latest lint --feature <name> and reconcile the planning doc path it validates. If manual path resolution is unavoidable, first resolve .ai-devkit.json paths.docs, falling back to docs/ai.
  2. If continuing from implementation, carry forward existing context. Otherwise ask for feature name, completed tasks, new tasks, blockers, and planning doc path.
  3. Review existing milestones, sequencing, dependencies, and outstanding tasks.
  4. Reconcile each task: mark status as done, in-progress, blocked, or not started; note scope changes; record blockers; capture skipped or added tasks.
  5. Update the planning doc with the current status checklist.
  6. Suggest the next 2-3 actionable tasks, risky areas, and coordination needed.
  7. If task tracing is available, record completed/blocked/new tasks, blockers, and next action per task.
  8. Write a summary paragraph for the planning doc covering progress, risks, upcoming focus, and scope changes.

Next: if tasks remain, return to dev-implementation. If all done, run implementation verification before testing and review.

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 Dev Planning AI skill do?

AI DevKit · Planning phase guidance for creating and reconciling feature task plans. Use when the user wants to create an implementation plan, update planning docs, mark task progress, capture blockers or new tasks, or run dev-lifecycle planning work.

Why use Dev Planning on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/codeaholicguy/ai-devkit/tree/main/skills/dev-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 Dev 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 Dev Planning?

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

Is the Dev Planning AI skill free?

Yes. It is published on GitHub by codeaholicguy under the Apache-2.0 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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