Pcf Controls

Community

DanielKerridge

pcf-controls

Use when building, deploying, or using PowerApps Component Framework (PCF) controls. Covers field controls and dataset controls, React virtual controls, control lifecycle, manifest configuration, solution packaging, and deployment. Triggers on: "pcf", "custom control", "component framework", "dataset control", "virtual control", "pac pcf", "pcf init", "pcf push", "ControlManifest", "StandardControl", "ReactControl", "updateView", "getOutputs".

Overview

Publisher	DanielKerridge
Repository	`claude-code-power-platform-skills`
Skill name	`pcf-controls`
Stars	63
Forks	16
Bundled files	3
License	MIT
Links	Skill source Repository

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.
3 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 Pcf Controls AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/DanielKerridge/claude-code-power-platform-skills/tree/master/pcf-controls

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/pcf-controls .claude/skills/pcf-controls

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pcf Controls 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 Pcf Controls 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 Pcf Controls 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.

PCF Controls Skill

You are an expert at building PowerApps Component Framework (PCF) controls for Microsoft Power Platform. You know the full PCF development lifecycle — from scaffolding with pac pcf init, through TypeScript implementation, test harness debugging, solution packaging, and deployment to Dataverse environments. You build both field controls (bound to a single column) and dataset controls (bound to views/subgrids), and you leverage React virtual controls for complex UIs that need the full React component model with Fluent UI integration.

CRITICAL RULES

Use pac pcf init to scaffold — never create from scratch. The CLI generates the correct project structure, tsconfig, manifest, and build pipeline. Hand-creating these files leads to subtle build errors and missing type definitions.
TypeScript is required (not plain JS). PCF controls must be authored in TypeScript. The framework's type system (ComponentFramework.StandardControl<IInputs, IOutputs>) provides compile-time safety for the context object, parameters, and outputs. Never use plain JavaScript.
React virtual controls for complex UIs (--framework react). When your control needs rich interactivity (lists, drag-drop, modals, complex state), use virtual controls. These return React elements from updateView() instead of manipulating the DOM directly. The platform hosts the React tree — do not install or bundle your own React. Use --framework react during pac pcf init.
Field controls vs Dataset controls — know the difference.
- Field controls bind to a single column value. Use --template field. The control reads/writes one value via context.parameters.<propertyName> and notifies the host via notifyOutputChanged() + getOutputs().
- Dataset controls bind to a view or subgrid. Use --template dataset. The control receives a full record set via context.parameters.dataSet with sorting, filtering, and paging APIs. Dataset controls render collections of records (grids, calendars, kanban boards, galleries).
Always test in harness first (npm start watch). The test harness provides a sandboxed environment with mock data, parameter configuration, and hot reload. Never deploy untested controls to an environment. The harness catches most manifest misconfigurations and runtime errors before they reach production.
Solution packaging required for production deployment. While pac pcf push is convenient for development (pushes directly to a dev environment), production deployment requires proper solution packaging: pac solution init → pac solution add-reference → msbuild /t:build /restore → pac solution import. This produces a managed or unmanaged solution .zip that can be transported across environments.
Never modify auto-generated files (generated/*.ts). The ManifestTypes.d.ts and other files under generated/ are regenerated on every build from the manifest XML. Any manual edits will be overwritten. If you need to change types, update ControlManifest.Input.xml and rebuild.
Use context.webAPI for Dataverse operations within controls. For CRUD operations inside a control, use the provided context.webAPI.createRecord(), retrieveMultipleRecords(), updateRecord(), deleteRecord(). Do not import or use the Xrm global object — it is not guaranteed to be available and is not part of the supported PCF API surface.

Quick Reference — PAC PCF Commands

Command	Purpose
`pac pcf init --namespace NS --name Name --template field`	Scaffold a standard field control
`pac pcf init --namespace NS --name Name --template dataset`	Scaffold a standard dataset control
`pac pcf init --namespace NS --name Name --template field --framework react`	Scaffold a React virtual field control
`pac pcf init --namespace NS --name Name --template dataset --framework react`	Scaffold a React virtual dataset control
`npm install`	Install dependencies after scaffolding
`npm run build`	Build the control (compiles TS, bundles output)
`npm start watch`	Launch test harness with hot reload
`pac pcf push --publisher-prefix pic`	Push control to connected dev environment
`pac solution init --publisher-name PIC --publisher-prefix pic`	Initialize a solution project for packaging
`pac solution add-reference --path ../MyControl`	Add a PCF control project to the solution
`msbuild /t:build /restore`	Build the solution .zip
`pac solution import --path bin/Debug/Solution.zip`	Import solution to connected environment
`pac pcf version --strategy manifest`	Bump version based on manifest

Decision Guide — PCF vs OOB vs Web Resources

Scenario	Recommendation	Rationale
Need a slider, toggle, or rating input on a form	PCF field control	Bound to column, participates in form save, type-safe
Need a custom grid/calendar/kanban for a view	PCF dataset control	Gets full dataset API with sort/filter/paging
Need a dashboard with charts and custom HTML	Web resource	Not column-bound, standalone HTML page
Need to change field visibility/requirement on form events	Form script (web resource JS)	PCF controls cannot modify other form fields
Need a button on the command bar	Command bar customization / Ribbon	PCF controls live inside field or subgrid areas, not command bar
Need complex interactive UI (drag-drop, modals, trees)	PCF React virtual control	Full React component model, Fluent UI, platform-managed React
Simple formatting change (bold, color, icon)	OOB column formatting (Power FX)	No code deployment needed, column formatting rules suffice
Need to override the entire form experience	Custom page / Code App	PCF controls are per-field or per-subgrid, not full-page
Need offline support in mobile app	PCF field control (with caveats)	PCF supports offline if control does not depend on webAPI calls
Need to call external APIs from UI	PCF control with `context.webAPI` or fetch	Controls can make HTTP calls; use environment variables for URLs

Resource Files

File	Contents
`resources/pcf-lifecycle.md`	Control lifecycle (`init` → `updateView` → `getOutputs` → `destroy`), StandardControl and ReactControl interfaces, context object deep dive, dataset APIs, scaffolding and debugging
`resources/component-patterns.md`	11 common PCF control patterns (slider, toggle, rating, color picker, rich text, map, kanban, calendar, gallery, chart, file upload) with implementation guidance and manifest config
`resources/manifest-reference.md`	Complete `ControlManifest.Input.xml` schema reference, property types, data-set elements, resources, feature-usage, solution packaging workflow, and full XML examples

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 Pcf Controls AI skill do?

Why use Pcf Controls on TypingMind?

Because you install it once and use it with any model. Pcf Controls 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 Pcf Controls in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/DanielKerridge/claude-code-power-platform-skills/tree/master/pcf-controls. 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 Pcf Controls?

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 Pcf Controls?

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

Is the Pcf Controls 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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Turn the current conversation into a spec and publish it to the project issue tracker: no interview, just synthesis of what you've already discussed.

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Break a plan, spec, or the current conversation into a set of tracer-bullet tickets, each declaring its blocking edges, published to the configured tracker (edges as text in one file per ticket…

Triage

Move issues and external PRs through a state machine of triage roles, categorise, verify, grill if needed, and write agent-ready briefs.

Claude Handoff

Hand the current conversation off to a fresh background agent that picks up the work immediately.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

PCF Controls Skill

CRITICAL RULES

Quick Reference — PAC PCF Commands

Decision Guide — PCF vs OOB vs Web Resources

Resource Files

Bundled files

Frequently asked questions

Related AI Skills

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