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Plan With Team

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DanielKerridge
plan-with-team

Spawns an Agent Team to collaboratively plan Power Platform / Dataverse applications. Three specialists (Data Architect, UX Designer, The Skeptic) debate and refine the plan before any code is written. Falls back to structured single-agent planning if agent teams are not enabled. Triggers on: "plan my app", "plan with team", "design my app", "architect this app", "plan the schema", "team planning", "agent team plan", "plan power app", "plan dataverse app", "design the data model".

Overview

PublisherDanielKerridge
Repositoryclaude-code-power-platform-skills
Skill nameplan-with-team
Stars
63
Forks
16
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 DanielKerridge on GitHub. Read the source before you install it.

Installation

Install the Plan With Team 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/DanielKerridge/claude-code-power-platform-skills.git /tmp/claude-code-power-platform-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-power-platform-skills/plan-with-team .claude/skills/plan-with-team
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Plan With Team 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 Plan With Team 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 Plan With Team 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.

Plan With Team — Agent Team Planning Skill

You orchestrate a 3-agent planning team that debates and refines an application plan before any code is written. The goal is to produce a battle-tested plan where architectural flaws, edge cases, and UX gaps have been challenged and resolved.

CRITICAL RULES

  1. No code is written during planning. The output is a plan document only.
  2. The Skeptic writes NO artifacts. Their only job is to challenge and find flaws.
  3. Every agent must reference the relevant skills. Data Architect uses dataverse-web-api, UX Designer uses power-apps-code-apps. Load those skills for domain knowledge.
  4. The plan must be consolidated by the Lead into a single structured document before presenting to the user for approval.
  5. If agent teams are not enabled, fall back to single-agent structured planning using the same framework. Read resources/fallback-mode.md.
  6. Schema creation can be parallelized. When implementation starts, reference resources/parallelization.md in the dataverse-web-api skill for the dependency graph. Multiple tables can be built simultaneously by separate agents.

The Team

RoleAgent NameMandate
Data Architectdata-architectDesign tables, columns, relationships, option sets, security model
UX/Flow Designerux-designerDesign app module, forms, views, sitemap, user journeys
The Skepticthe-skepticChallenge everything. Find security holes, edge cases, ALM issues, performance traps

Workflow

Phase 1 — Gather Requirements (Lead)

Before spawning the team, the Lead must understand:

  • Who uses this app? (personas/roles)
  • What data does it manage? (entities/relationships)
  • What are the key workflows? (create, approve, report, etc.)
  • What existing Dataverse tables/solutions already exist?

Ask the user these questions if the prompt is vague. Do NOT spawn the team until you have enough context to write meaningful spawn prompts.

Phase 2 — Spawn the Team

Spawn all three agents with detailed context. Each agent gets:

  • The user's requirements (from Phase 1)
  • Their role-specific instructions (from resources/roles/)
  • Instructions to load the relevant domain skill
Spawn a teammate called "data-architect" with this prompt:
[Read resources/roles/data-architect.md and include its full content]

Spawn a teammate called "ux-designer" with this prompt:
[Read resources/roles/ux-designer.md and include its full content]

Spawn a teammate called "the-skeptic" with this prompt:
[Read resources/roles/the-skeptic.md and include its full content]

Phase 3 — Parallel Design (Agents Work)

  • Data Architect designs the schema and broadcasts their proposal
  • UX Designer designs the app structure and broadcasts their proposal
  • The Skeptic reads both proposals and challenges them with specific questions

Agents message each other directly to resolve issues. The Lead monitors but does NOT intervene unless agents are stuck or going in circles.

Phase 4 — Skeptic Review Round

The Skeptic performs a structured review using the checklist in resources/roles/the-skeptic.md. They broadcast findings to both agents.

Data Architect and UX Designer must respond to every finding with either:

  • ACCEPTED — change incorporated into their design
  • REJECTED (reason) — justified pushback

Phase 5 — Consolidation (Lead)

The Lead collects all three agents' final outputs and consolidates into the plan template defined in resources/plan-template.md.

Present the consolidated plan to the user for approval.

Phase 6 — Handoff

Once approved, the plan document serves as the implementation spec. Each section maps directly to Dataverse Web API operations documented in the dataverse-web-api skill.

Parallelization: Consider which implementation steps can run in parallel vs must be sequential. Multiple table agents can create their table + columns + views + forms simultaneously. Cross-cutting concerns (relationships, sitemap, app module) must be handled by the main agent after table agents complete. See the dataverse-web-api skill's parallelization.md resource.

Enabling Agent Teams

If the user hasn't enabled agent teams:

json
// Add to .claude/settings.json
{
  "env": {
    "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1"
  }
}

Then restart Claude Code.

If agent teams are not available, use the fallback in resources/fallback-mode.md.

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 Plan With Team AI skill do?

Spawns an Agent Team to collaboratively plan Power Platform / Dataverse applications. Three specialists (Data Architect, UX Designer, The Skeptic) debate and refine the plan before any code is written. Falls back to structured single-agent planning if agent teams are not enabled. Triggers on: "plan my app", "plan with team", "design my app", "architect this app", "plan the schema", "team planning", "agent team plan", "plan power app", "plan dataverse app", "design the data model".

Why use Plan With Team on TypingMind?

Because you install it once and use it with any model. Plan With Team 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 Plan With Team in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/DanielKerridge/claude-code-power-platform-skills/tree/master/plan-with-team. 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 Plan With Team?

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 Plan With Team?

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

Is the Plan With Team AI skill free?

Yes. It is published on GitHub by DanielKerridge 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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