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Perf Optimise

Community
korchard333
perf-optimise

Power Platform performance optimization. Use when: performance, slow, optimization, delegation, caching, indexing, query performance, API limits, throttling, virtual scrolling, lazy loading, concurrent, batch operations, form load.

Overview

Publisherkorchard333
Repositoryclaude-power-platform-community
Skill nameperf-optimise
Stars
50
Forks
6
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by korchard333 on GitHub. Read the source before you install it.

Installation

Install the Perf Optimise 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.
    https://github.com/korchard333/claude-power-platform-community/tree/main/.claude/skills/perf-optimise
  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/korchard333/claude-power-platform-community.git /tmp/claude-power-platform-community
mkdir -p .claude/skills
cp -r /tmp/claude-power-platform-community/.claude/skills/perf-optimise .claude/skills/perf-optimise
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Perf Optimise 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 Perf Optimise 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 Perf Optimise 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.

Skill: Performance Optimization

When to Use

Trigger when diagnosing or optimizing performance for Canvas Apps, Model-Driven Apps, Code Apps, Power Automate flows, Dataverse queries, or plugins. Also trigger when addressing API throttling (429 errors) or capacity limits.


Canvas App Performance

Delegation

Delegation pushes query logic to the data source. Non-delegable operations process only the first 500-2000 records locally.

Delegable to DataverseNOT Delegable
Filter with =, <>, <, >, <=, >=Distinct
Filter with in (choice columns)CountRows (use CountIf for delegable)
Sort (single column)GroupBy
Search (Dataverse only)AddColumns in a Filter
StartsWithNested Filter
LookUpFirst applied to non-delegable

Key Canvas Optimizations

powerfx
// BAD: Non-delegable — loads all records, filters locally
Filter(Contacts, Text(Phone, "[$-en-US]#") = "555-0100")

// GOOD: Delegable — server-side filter
Filter(Contacts, Phone = "555-0100")

// BAD: Loading all records then counting
CountRows(Filter(LargeTable, Status = "Active"))

// GOOD: CountIf is delegable to Dataverse
CountIf(LargeTable, Status = "Active")

Concurrent() for Parallel Data Loading

powerfx
// BAD: Sequential loading
ClearCollect(colAccounts, Accounts);
ClearCollect(colContacts, Contacts);
ClearCollect(colOrders, Orders);
// Total time = T1 + T2 + T3

// GOOD: Parallel loading
Concurrent(
    ClearCollect(colAccounts, Accounts),
    ClearCollect(colContacts, Contacts),
    ClearCollect(colOrders, Orders)
);
// Total time = max(T1, T2, T3)

Named Formulas (Pre-Computation)

powerfx
// App.Formulas — computed once, cached
ActiveAccounts = Filter(Accounts, statuscode = 1);
UserDepartment = Office365Users.MyProfile().Department;
TodayDate = Today();

// These are evaluated once and cached — no re-computation on screen navigation

Collection Pre-Loading

powerfx
// App.OnStart — load reference data once
Concurrent(
    ClearCollect(colStatusOptions, Choices('Accounts'.Status)),
    ClearCollect(colCategoryOptions, Choices('Accounts'.Category)),
    ClearCollect(colCountries, Filter(Countries, IsActive = true))
);

// Galleries and dropdowns use local collections (fast) instead of Dataverse calls

Dataverse Query Performance

$select — Only Request Needed Columns

bash
# BAD: Returns ALL columns (slow, wastes bandwidth)
GET /api/data/v9.2/contacts

# GOOD: Only requested columns returned
GET /api/data/v9.2/contacts?$select=firstname,lastname,emailaddress1

$filter — Server-Side Filtering

bash
# BAD: Get all, filter client-side
GET /api/data/v9.2/contacts  # Then filter in JS

# GOOD: Server-side filter
GET /api/data/v9.2/contacts?$filter=statecode eq 0 and contains(lastname,'Smith')&$top=50

$top — Limit Result Set

bash
# Always use $top for paged results
GET /api/data/v9.2/contacts?$top=50&$select=fullname,emailaddress1

# For next page, use @odata.nextLink from response

Indexing

Dataverse automatically indexes primary key and common columns. For custom columns used in filters:

Power Platform Admin Center → Environments → [env] → Settings
  → Administration → System Settings → Administration tab
  → Performance: Entity-specific index recommendations

Or request custom indexes via support for high-volume tables

FetchXML Aggregate vs OData

For aggregations, FetchXML is significantly faster than OData:

xml
<!-- FetchXML aggregate — processed server-side -->
<fetch aggregate="true">
  <entity name="contact">
    <attribute name="contactid" alias="count" aggregate="count" />
    <attribute name="annual_revenue" alias="total_revenue" aggregate="sum" />
    <filter>
      <condition attribute="statecode" operator="eq" value="0" />
    </filter>
  </entity>
</fetch>
bash
# Equivalent OData (less efficient for aggregation)
GET /api/data/v9.2/contacts?$apply=filter(statecode eq 0)/aggregate(contactid with countdistinct as count, annual_revenue with sum as total_revenue)

Alternate Keys for Upsert

bash
# Instead of: GET to check existence, then POST or PATCH
# Use: PATCH with alternate key (single call, upsert)
PATCH /api/data/v9.2/contacts(emailaddress1='john@contoso.com')
  -H "If-Match: *"
  -d '{"firstname": "John", "lastname": "Doe"}'

Plugin Performance

Sandbox Constraints

ConstraintLimit
Execution timeout2 minutes
Memory256 MB
CPUShared (no guarantee)
External HTTP callsAllowed in async, risky in sync

Optimization Patterns

csharp
// BAD: Query inside a loop
foreach (var contactId in contactIds)
{
    var contact = service.Retrieve("contact", contactId, new ColumnSet("fullname"));
    // N+1 query problem
}

// GOOD: Single query with IN filter
var query = new QueryExpression("contact")
{
    ColumnSet = new ColumnSet("fullname"),
    Criteria = new FilterExpression
    {
        Conditions = {
            new ConditionExpression("contactid", ConditionOperator.In, contactIds.ToArray())
        }
    }
};
var results = service.RetrieveMultiple(query);
csharp
// BAD: Synchronous HTTP call in sync plugin (blocks user, risks timeout)
var client = new HttpClient();
var response = await client.GetAsync("https://external-api.com/validate");

// GOOD: Use Service Bus or async plugin for external calls
// Or use pre-validated data from Dataverse
csharp
// BAD: Processing all records in bulk operation trigger
// Plugin fires once per record in a CreateMultiple — 1000 records = 1000 plugin executions

// GOOD: Batch-aware plugin using IPluginExecutionContext4
var context4 = (IPluginExecutionContext4)context;
// Check if running in bulk context and optimize accordingly

Code App Performance

Tree-Shaking and Bundle Size

typescript
// BAD: Import entire library
import _ from "lodash";

// GOOD: Import only what you need
import debounce from "lodash/debounce";

Lazy Routes (Code Splitting)

typescript
// React Router with lazy loading
import { lazy, Suspense } from "react";

const Dashboard = lazy(() => import("./pages/Dashboard"));
const Settings = lazy(() => import("./pages/Settings"));

function App() {
  return (
    <Suspense fallback={<Loading />}>
      <Routes>
        <Route path="/" element={<Dashboard />} />
        <Route path="/settings" element={<Settings />} />
      </Routes>
    </Suspense>
  );
}

TanStack Query (Stale/Cache)

typescript
import { useQuery } from "@tanstack/react-query";

function useAccounts() {
  return useQuery({
    queryKey: ["accounts"],
    queryFn: fetchAccounts,
    staleTime: 5 * 60 * 1000,     // 5 min — don't refetch if fresh
    gcTime: 30 * 60 * 1000,        // 30 min — keep in cache
    refetchOnWindowFocus: false,    // Don't refetch on tab switch
  });
}

Virtual Scrolling for Large Lists

typescript
import { useVirtualizer } from "@tanstack/react-virtual";

function VirtualList({ items }: { items: Item[] }) {
  const parentRef = useRef<HTMLDivElement>(null);
  const virtualizer = useVirtualizer({
    count: items.length,
    getScrollElement: () => parentRef.current,
    estimateSize: () => 40,
  });

  return (
    <div ref={parentRef} style={{ height: "400px", overflow: "auto" }}>
      <div style={{ height: virtualizer.getTotalSize() }}>
        {virtualizer.getVirtualItems().map((virtual) => (
          <div key={virtual.key} style={{ transform: `translateY(${virtual.start}px)` }}>
            {items[virtual.index].name}
          </div>
        ))}
      </div>
    </div>
  );
}

Power Automate Performance

Apply to Each Concurrency

Apply to each → Settings → Concurrency Control
  → Turn on: Yes
  → Degree of parallelism: 50 (maximum)
SettingBehavior
Off (default)Sequential — 1 at a time
On, parallelism = 2020 concurrent iterations
On, parallelism = 50Maximum concurrency

Warning: High concurrency can cause API throttling (429). Monitor and adjust.

Pagination

Action: List rows (Dataverse)
  → Settings → Pagination → On
  → Threshold: 100,000 (max records to retrieve)

Default without pagination: 5,000 rows max

Child Flows

Break large flows into child flows for:

  • Reusability across multiple parent flows
  • Cleaner flow design
  • Independent error handling
  • Parallel execution of independent branches

Avoid Deep Nesting

BAD: Apply to each → Condition → Apply to each → Condition → Apply to each
  (> 8 levels of nesting = flow failure)

GOOD: Use child flows, Select/Filter expressions, or batch operations

Model-Driven App Performance

Form Load Optimization

OptimizationImpact
Fewer tabs and sectionsLess HTML to render
Lazy subgridsLoad on tab expand, not on form load
Fewer columns on formLess data fetched
Minimize OnLoad JavaScriptDefer non-critical logic
Quick View forms instead of subgridsLighter weight for related data

JavaScript Best Practices

javascript
// BAD: Heavy processing in OnLoad
function onLoad() {
    // Fetches data, updates 20 fields, calls external API
    var result = Xrm.WebApi.retrieveMultipleRecords("contact", "?$top=1000");
    // ...
}

// GOOD: Minimal OnLoad, defer heavy work
function onLoad(context) {
    // Only essential logic
    setFieldVisibility(context);

    // Defer non-critical work
    setTimeout(function() {
        loadSecondaryData(context);
    }, 100);
}

API Request Limits

Protection Limits (Hard Throttle)

LimitValueScope
Per user6,000 requests / 5-minute windowPer user
Per web server60,000 / 5 minPer web server (shared)
Concurrent requests52 per userSimultaneous connections

When Throttled (HTTP 429)

bash
# Response includes Retry-After header
HTTP/1.1 429 Too Many Requests
Retry-After: 30

# Always honor Retry-After
sleep $(curl -sI ... | grep Retry-After | awk '{print $2}')

Strategies to Avoid Throttling

StrategyImplementation
Batch operationsUse CreateMultiple/UpdateMultiple instead of individual calls
$selectReduce payload size, faster responses
Exponential backoffOn 429, wait 1s → 2s → 4s → 8s
Connection poolingReuse HTTP connections
Off-peak schedulingRun bulk operations during low-usage hours
Service protection limit monitoringTrack via Admin Center analytics

Performance Checklist

markdown
## Performance Review Checklist

### Canvas App
- [ ] All gallery queries are delegable
- [ ] Using Concurrent() for parallel data loading
- [ ] Named Formulas for pre-computation
- [ ] Collections for reference data (not repeated server calls)
- [ ] No non-delegable functions on tables > 500 rows

### Dataverse Queries
- [ ] $select on every query (no select-all)
- [ ] $filter for server-side filtering
- [ ] $top for pagination
- [ ] FetchXML for aggregations
- [ ] Alternate keys for upsert operations

### Code App
- [ ] Tree-shaking (no unused library imports)
- [ ] Lazy routes for code splitting
- [ ] TanStack Query with staleTime configured
- [ ] Virtual scrolling for lists > 100 items
- [ ] Dynamic imports for heavy components

### Power Automate
- [ ] Apply to Each concurrency enabled (where safe)
- [ ] Pagination enabled for large queries
- [ ] Child flows for reusable/nested logic
- [ ] No nesting deeper than 4-5 levels
- [ ] Batch operations instead of loop + individual API calls

### Plugins
- [ ] No N+1 query patterns
- [ ] No sync HTTP calls in sync plugins
- [ ] Using early-bound types (faster than late-bound)
- [ ] ColumnSet specified (never ColumnSet(true))

### API Limits
- [ ] Retry-After handling for 429 responses
- [ ] Batch operations for bulk processing
- [ ] Off-peak scheduling for heavy operations
- [ ] Monitoring API usage in Admin Center

Anti-Patterns

  • Querying all columns ($select omitted) — wastes bandwidth, slower response
  • Non-delegable functions on large datasets — silent data truncation
  • Synchronous HTTP calls in sync plugins — blocks user, risks timeout
  • Apply to Each without concurrency — sequential processing of 1000 items
  • No caching in Code Apps — refetching data on every render
  • Full page loads instead of virtual scrolling — DOM bloat, slow rendering
  • Plugin executing inside loops — N+1 query pattern
  • Ignoring 429 errors — retrying immediately without backoff makes it worse
  • Loading all reference data on app start — slow startup, use lazy loading
  • No $top on OData queries — potentially returning millions of rows

Related Skills

  • dataverse-web-api — Query optimization, batch operations
  • canvas-apps — Delegation patterns, performance techniques
  • code-apps — React performance, bundling
  • plugins — Sandbox constraints, execution optimization
  • power-automate — Flow concurrency, pagination
  • observability — Performance monitoring and alerting

Frequently asked questions

What does the Perf Optimise AI skill do?

Power Platform performance optimization. Use when: performance, slow, optimization, delegation, caching, indexing, query performance, API limits, throttling, virtual scrolling, lazy loading, concurrent, batch operations, form load.

Why use Perf Optimise on TypingMind?

Because you install it once and use it with any model. Perf Optimise 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 Perf Optimise in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/korchard333/claude-power-platform-community/tree/main/.claude/skills/perf-optimise. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Perf Optimise?

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 Perf Optimise?

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

Is the Perf Optimise AI skill free?

Yes. It is published on GitHub by korchard333 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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