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Skill
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Audit score 90

performance

cognitedata/builder-skills

Fix Flows app performance: re-renders, query patterns, pagination, memory leaks, and LLM costs.

What is performance?

Systematically diagnose and fix performance issues in Flows apps by measuring baselines, eliminating unnecessary re-renders, optimizing CDF query patterns, moving filters server-side, and controlling LLM costs. Use this when the app is slow, laggy, or has high bundle/memory overhead.

  • Measure baseline performance with Lighthouse and React Profiler before making changes
  • Identify and fix unnecessary re-renders using useMemo, useCallback, and React.memo
  • Rewrite read-heavy CDF API calls from list/retrieve to query/search endpoints
  • Move client-side filtering and pagination logic into server-side API requests
  • Detect and prevent unbounded LLM completions over query results with caching and limits

How to install performance

npx skills add https://github.com/cognitedata/builder-skills --skill performance
Claude Code
Cursor
Windsurf
Cline

How to use performance

  1. 1.Run pnpm run build and pnpm run preview, then capture baseline Lighthouse score and React Profiler metrics
  2. 2.Search for inline object/array creation and event handlers in JSX; wrap with useMemo and useCallback
  3. 3.Find instances.list calls in read-heavy paths and rewrite to instances.query with equivalent filters
  4. 4.Identify client-side .filter() calls after SDK responses and move the filter logic into the API request
  5. 5.Check for unbounded LLM completions over query results; implement caching and per-user limits if needed
  6. 6.Re-measure Lighthouse and Profiler after each fix to confirm improvement

Use cases

Good for
  • Optimizing a slow asset table or dropdown that re-renders on every parent update
  • Converting instances.list calls to instances.query for Elasticsearch-backed read performance
  • Moving client-side .filter() operations into CDF query filter parameters
  • Reducing LLM API costs by caching completions and limiting per-item agent calls
  • Fixing memory leaks and bundle size bloat in production builds
Who it's for
  • Frontend engineers optimizing Flows app performance
  • Full-stack developers tuning CDF query patterns and pagination
  • Teams managing LLM costs in agent-heavy workflows
  • Developers profiling React components with DevTools

performance FAQ

When should I use instances.query vs instances.list?

Use instances.query for read-heavy UI (tables, dropdowns, search results) because it hits Elasticsearch. Use instances.list only for writing, syncing, or when you need semantics not available in query/search.

How do I move client-side filtering to the server?

Take the .filter() condition (e.g., n => n.properties.status === 'active') and express it in the API's filter parameter (e.g., { equals: { property: [...], value: 'active' } }), then remove the client-side .filter() call.

What's the hard rule for LLM completions over query results?

Do not map chat.completions over DMS rows. Use one sendAgentMessage or agent resource instead. If per-item completions are unavoidable, limit to 5 items, cache by space:externalId:lastUpdatedTime, and require user initiation.

Why measure before and after optimization?

Blind optimization wastes time and can introduce bugs. Baseline metrics (Lighthouse, Profiler render times) prove which changes actually help and prevent regressions.

What counts as a client-side filter I should move server-side?

Any .filter(), .find(), or .some() call on a full SDK result set that applies a condition the API supports (equals, range, text search). Only keep client-side filtering for trivial local state like a 10-item user preference list.

Full instructions (SKILL.md)

Source of truth, from cognitedata/builder-skills.


name: performance description: "MUST be used when fixing Flows app performance — re-renders, query patterns, pagination, unbounded fetches, LLM-over-query-results, bundles, memory leaks. Measure before and after. Triggers: performance, slow, laggy, optimize, re-render, bundle size, CDF query, virtualize, chat completions, LLM cost." allowed-tools: Read, Glob, Grep, Shell, Write metadata: argument-hint: "[file, component, or area to optimize — e.g. 'src/components/AssetTable.tsx']"

Performance Fix

Systematically find and fix performance issues in $ARGUMENTS (or the whole app if no argument is given). Always measure first — never optimize blindly.


Step 1 — Measure baseline before touching anything

Run the production build and capture metrics before making any changes:

pnpm run build
pnpm run preview

Open the app in Chrome and capture:

  • Lighthouse score (Performance tab → Run audit)
  • React Profiler (React DevTools → Profiler → Record an interaction)
    • Note the components with the longest render times and highest render counts

Record baseline numbers. Every fix must be measured against these.


Step 2 — Find and fix unnecessary re-renders

Read the component tree (start from src/App.tsx) and search for these patterns:

grep -rn --include="*.tsx" \
  -E "value=\{\{|onClick=\{\(\)" src/

For each instance found, apply the fix directly:

Inline object/array creation in JSX → wrap with useMemo:

// BAD — new object on every render causes children to re-render
<Chart options={{ color: "red" }} />

// FIX — wrap with useMemo
const chartOptions = useMemo(() => ({ color: "red" }), []);
<Chart options={chartOptions} />

Event handlers recreated on every render → wrap with useCallback:

// BAD
<Button onClick={() => doSomething(id)} />

// FIX — wrap with useCallback
const handleClick = useCallback(() => doSomething(id), [id]);
<Button onClick={handleClick} />

Context that changes on every render → memoize the context value:

// BAD — new object reference every render
<MyContext.Provider value={{ user, sdk }}>

// FIX — memoize the context value
const ctxValue = useMemo(() => ({ user, sdk }), [user, sdk]);
<MyContext.Provider value={ctxValue}>

Apply React.memo to pure presentational components that receive stable props. Do NOT wrap every component — only those confirmed to re-render unnecessarily via the Profiler.


Step 3 — Find and fix DMS query patterns

For read-heavy workloads, prefer APIs that hit the search/Elasticsearch path (query or search on instances) rather than list paths that stress Postgres.

# Find all DMS instance API calls
grep -rn --include="*.ts" --include="*.tsx" -E "instances\.(list|search|query|aggregate|retrieve)" src/

# Find direct SDK calls to other CDF resources
grep -rn --include="*.ts" --include="*.tsx" -E "\.(assets|timeseries|events|files|sequences|relationships)\.(list|search|retrieve)" src/

For each instances.list call in a read-heavy path (e.g. populating a table, dropdown, or search results), rewrite it to use instances.query with the equivalent filter. Preserve the existing filter logic but express it in the query API format:

// BAD — instances.list hits Postgres, expensive for read-heavy UI
const result = await client.instances.list({
  instanceType: "node",
  filter: { equals: { property: ["node", "space"], value: "my-space" } },
  limit: 100,
});

// FIX — rewrite to instances.query which hits Elasticsearch
const result = await client.instances.query({
  with: {
    nodes: {
      nodes: {
        filter: { equals: { property: ["node", "space"], value: "my-space" } },
      },
      limit: 100,
    },
  },
  select: {
    nodes: {},
  },
});
API usedWhen it's correctWhen to rewrite
instances.queryRead with filters that map to Elasticsearch (text, equals, range)—
instances.searchFull-text or fuzzy search—
instances.listWriting, syncing, or need for semantics not available on query/searchRewrite to instances.query if used for read-heavy UI display
instances.retrieveFetching by known external IDs—
instances.aggregateCounts, histograms—

For deeper rationale on search vs relational paths, cardinality, and materialization tradeoffs, consult the semantic-knowledge/ directory if available in the workspace.

Hard gate — LLM over query results

grep -rn --include="*.ts" --include="*.tsx" -E "chat\.completions|agents/chat|useAtlasChat|openai|anthropic" src/

Do not map completions over DMS rows. Fix: one sendAgentMessage or agent resource (integrate-fusion-agent). If per-item completions remain: 5 / ceiling 50, cache by space:externalId:lastUpdatedTime, user-initiated only.


Step 4 — Find and fix client-side filtering (move to server-side)

Filters, limits, and projections must be applied in the API request — not by downloading large result sets and filtering in the browser.

# Find client-side filtering after data fetch (common anti-pattern)
grep -rn --include="*.ts" --include="*.tsx" -B 5 "\.filter(" src/ | grep -B 5 "data\|items\|result\|response\|nodes"

# Find .map() or .reduce() on full datasets that suggest client-side processing
grep -rn --include="*.ts" --include="*.tsx" -E "\.(map|reduce|find|some|every)\(" src/hooks/ src/services/ src/api/

For each client-side filter pattern, move the filter logic into the SDK call's filter parameter and remove the .filter() call:

// BAD — fetches all nodes then filters client-side
const result = await client.instances.query({ ... });
const activeNodes = result.items.nodes.filter(n => n.properties.status === "active");

// FIX — move filter into the API request, remove client-side .filter()
const result = await client.instances.query({
  with: {
    nodes: {
      nodes: {
        filter: {
          and: [
            existingFilters,
            { equals: { property: ["mySpace", "myView/v1", "status"], value: "active" } },
          ],
        },
      },
      limit: 100,
    },
  },
  select: { nodes: {} },
});
const activeNodes = result.items.nodes; // no client-side filter needed
IssueFix
.filter() after SDK call on full result setMove the filter into the API request's filter parameter and delete the .filter()
No properties selection in DMS queryAdd a sources or properties parameter to fetch only needed fields
Fetching all items then rendering a subsetAdd limit and filter to the API call to fetch only what's displayed
Client-side text search on fetched arrayReplace with the SDK's search endpoint

Hard rule: If the API supports a filter for the criterion being applied client-side, move it server-side now. Client-side filtering is acceptable only for trivial local state (e.g. filtering a cached list of 10 user preferences). If the API does not support the exact filter, add a code comment explaining why client-side filtering is necessary.


Step 5 — Find and fix CDF data fetching and pagination

Read all CDF SDK calls (search for sdk., client., useQuery, useCogniteClient).

# Find pagination patterns
grep -rn --include="*.ts" --include="*.tsx" -E "(nextCursor|cursor|hasNextPage|fetchNextPage|offset|skip|page)" src/

# Find "fetch all" loops
grep -rn --include="*.ts" --include="*.tsx" -B 3 -A 3 "while.*cursor\|while.*hasMore\|while.*nextPage" src/

For each call, find the issue and apply the fix:

IssueFix to apply
No limit setAdd limit: 100 (or the actual page size needed) to the SDK call
Fetching all propertiesAdd a properties filter to select only required fields
Fetching on every renderMove inside useQuery/useMemo with a stable dependency array
Sequential requests that could be parallelRewrite to Promise.all or batched SDK methods
Missing limit parameterAdd explicit limit matching the UI's page size (e.g. 25, 50, 100)
Offset-based pagination for large datasetsReplace with cursor-based pagination using nextCursor from the response
"Fetch all" loop (exhausts cursors up front)Replace with on-demand pagination using TanStack Query's useInfiniteQuery

Fixing fetch-all loops — replace the while loop with useInfiniteQuery:

// BAD — fetches ALL pages before rendering
let allItems = [];
let cursor = undefined;
while (true) {
  const result = await client.instances.list({ limit: 1000, cursor });
  allItems.push(...result.items);
  if (!result.nextCursor) break;
  cursor = result.nextCursor;
}

// FIX — paginate on demand with useInfiniteQuery
const { data, fetchNextPage, hasNextPage } = useInfiniteQuery({
  queryKey: ["instances", filters],
  queryFn: ({ pageParam }) =>
    client.instances.list({ limit: 100, cursor: pageParam, ...filters }),
  getNextPageParam: (lastPage) => lastPage.nextCursor ?? undefined,
  staleTime: 30_000,
});

Fixing offset-based pagination — switch to cursor-based:

// BAD — offset pagination degrades at scale
const result = await client.instances.list({ limit: 100, offset: page * 100 });

// FIX — cursor-based pagination
const result = await client.instances.list({ limit: 100, cursor: nextCursor });

Step 6 — Find and fix excessive API call rates

# Find search/filter inputs that trigger queries
grep -rn --include="*.tsx" --include="*.ts" -E "onChange|onInput|onSearch|onFilter" src/ | grep -i "search\|filter\|query"

# Find debounce usage
grep -rn --include="*.ts" --include="*.tsx" -i -E "debounce|useDebouncedValue|useDebounce" src/

# Find polling/interval patterns
grep -rn --include="*.ts" --include="*.tsx" -E "setInterval|refetchInterval|pollingInterval|refetchOnWindowFocus" src/

# Find useQuery options that control refetch behavior
grep -rn --include="*.ts" --include="*.tsx" -E "staleTime|cacheTime|gcTime|refetchOnMount|refetchOnWindowFocus" src/

For each issue found, apply the fix:

Search inputs that fire on every keystroke → add debounce with 300ms delay:

// BAD — fires API call on every keystroke
const [search, setSearch] = useState("");
const { data } = useQuery({ queryKey: ["search", search], queryFn: () => api.search(search) });

// FIX — create or use a useDebouncedValue hook with 300ms delay
function useDebouncedValue<T>(value: T, delay = 300): T {
  const [debounced, setDebounced] = useState(value);
  useEffect(() => {
    const timer = setTimeout(() => setDebounced(value), delay);
    return () => clearTimeout(timer);
  }, [value, delay]);
  return debounced;
}

const [search, setSearch] = useState("");
const debouncedSearch = useDebouncedValue(search, 300);
const { data } = useQuery({
  queryKey: ["search", debouncedSearch],
  queryFn: () => api.search(debouncedSearch),
  enabled: debouncedSearch.length > 0,
});

useQuery calls without staleTime → add appropriate staleTime:

// BAD — refetches on every mount/focus
useQuery({ queryKey: ["data"], queryFn: fetchData });

// FIX — add staleTime to prevent unnecessary refetches
useQuery({ queryKey: ["data"], queryFn: fetchData, staleTime: 30_000 });

Duplicate parallel identical requests → lift the query to a shared hook:

// BAD — multiple components each call the same query independently
// ComponentA.tsx: useQuery({ queryKey: ["assets"], queryFn: fetchAssets });
// ComponentB.tsx: useQuery({ queryKey: ["assets"], queryFn: fetchAssets });

// FIX — create a shared hook, import it from both components
// hooks/useAssets.ts
export function useAssets() {
  return useQuery({ queryKey: ["assets"], queryFn: fetchAssets, staleTime: 30_000 });
}
IssueFix to apply
Search input fires query on every keystrokeAdd useDebouncedValue hook with 300ms delay
Polling with no backoff or very short intervalSet interval to ≥30s with exponential backoff on errors
Re-fetching on every render (no caching)Add staleTime: 30_000 (or appropriate) to useQuery options
refetchOnWindowFocus: true for expensive queriesSet refetchOnWindowFocus: false or use a longer stale time
Duplicate parallel identical requestsLift the query to a shared hook and import from both components
Multiple components triggering the same fetchExtract to a shared hook in hooks/ directory

Step 7 — Find and fix large un-virtualized lists

Search for lists that render more than ~50 items:

grep -rn --include="*.tsx" -E "\.(map|forEach)\(" src/

For any list where the data source could exceed 50 items, replace the plain .map() render with a virtualized list. Install @tanstack/react-virtual if not present:

pnpm add @tanstack/react-virtual

Apply the virtualizer pattern directly:

// BAD — renders all items in the DOM
<div>
  {items.map((item) => (
    <div key={item.id}>{item.name}</div>
  ))}
</div>

// FIX — replace with virtualized list
import { useVirtualizer } from "@tanstack/react-virtual";

const parentRef = useRef<HTMLDivElement>(null);
const rowVirtualizer = useVirtualizer({
  count: items.length,
  getScrollElement: () => parentRef.current,
  estimateSize: () => 48,
});

return (
  <div ref={parentRef} style={{ height: "600px", overflow: "auto" }}>
    <div style={{ height: rowVirtualizer.getTotalSize(), position: "relative" }}>
      {rowVirtualizer.getVirtualItems().map((virtualRow) => (
        <div
          key={virtualRow.key}
          style={{
            position: "absolute",
            top: 0,
            left: 0,
            width: "100%",
            height: `${virtualRow.size}px`,
            transform: `translateY(${virtualRow.start}px)`,
          }}
        >
          {items[virtualRow.index].name}
        </div>
      ))}
    </div>
  </div>
);

Step 8 — Find and fix missing code splitting

Read the router setup and identify routes that are imported statically but not shown on the landing page.

For each statically imported heavy page, convert to lazy import with React.lazy() and Suspense:

// BAD — statically imported, loaded in initial bundle
import { ReportPage } from "./pages/ReportPage";

// FIX — convert to lazy import
import { lazy, Suspense } from "react";
const ReportPage = lazy(() => import("./pages/ReportPage"));

// In the route — wrap with Suspense
<Suspense fallback={<PageSkeleton />}>
  <ReportPage />
</Suspense>

Similarly, large third-party components (chart libraries, PDF viewers, map renderers) should be dynamically imported inside the component that needs them, not at the module level. Apply the transformation directly to each heavy import found.


Step 9 — Analyse and fix bundle size

# Install if not already present, then run
pnpm add -D rollup-plugin-visualizer

Add to vite.config.ts temporarily:

import { visualizer } from "rollup-plugin-visualizer";

export default defineConfig({
  plugins: [
    react(),
    visualizer({ open: true, gzipSize: true, brotliSize: true }),
  ],
});

Run pnpm run build and inspect the treemap. For any chunk > 100 KB (gzipped) that is not a necessary initial dependency, apply the fix:

IssueFix to apply
lodash (full bundle)Replace with lodash-es individual imports or native equivalents (e.g., Array.prototype.map, Object.entries, structuredClone)
momentReplace with date-fns or native Intl.DateTimeFormat
Chart libraries not tree-shakenSwitch to named imports (e.g., import { LineChart } from "echarts/charts")
Large library used in one placeDynamically import it with React.lazy or inline import()
// BAD
import _ from "lodash";
const sorted = _.sortBy(items, "name");

// FIX — use lodash-es or native
import sortBy from "lodash-es/sortBy";
const sorted = sortBy(items, "name");
// OR native:
const sorted = [...items].sort((a, b) => a.name.localeCompare(b.name));
// BAD
import moment from "moment";
const formatted = moment(date).format("YYYY-MM-DD");

// FIX — use date-fns
import { format } from "date-fns";
const formatted = format(date, "yyyy-MM-dd");

After analysis, remove the visualizer plugin from vite.config.ts and uninstall it:

pnpm remove rollup-plugin-visualizer

Step 10 — Find and fix memory leaks

Search for useEffect hooks that set up subscriptions, timers, or event listeners without cleanup:

grep -rn --include="*.tsx" --include="*.ts" -A 10 "useEffect" src/

For every useEffect that calls addEventListener, setInterval, setTimeout, subscribe, or sets up a CDF streaming connection, add the missing cleanup function:

Fetch without abort → add AbortController:

// BAD — no cleanup, fetch continues after unmount
useEffect(() => {
  fetchData(id);
}, [id]);

// FIX — add AbortController for cleanup
useEffect(() => {
  const controller = new AbortController();
  fetchData(id, controller.signal);
  return () => controller.abort();
}, [id]);

Timer without cleanup → add clearInterval/clearTimeout:

// BAD — interval keeps running after unmount
useEffect(() => {
  const id = setInterval(() => poll(), 5000);
}, []);

// FIX — add clearInterval cleanup
useEffect(() => {
  const id = setInterval(() => poll(), 5000);
  return () => clearInterval(id);
}, []);

Event listener without cleanup → add removeEventListener:

// BAD — listener accumulates on each render
useEffect(() => {
  window.addEventListener("resize", handleResize);
}, []);

// FIX — add removeEventListener cleanup
useEffect(() => {
  window.addEventListener("resize", handleResize);
  return () => window.removeEventListener("resize", handleResize);
}, []);

Step 11 — Measure after and report the delta

Re-run the same Lighthouse audit and React Profiler session from Step 1. Report the delta and list every file changed:

MetricBeforeAfterChange
Lighthouse Performance7291+19
Largest Contentful Paint3.2 s1.8 s−1.4 s
Total Blocking Time420 ms80 ms−340 ms
Bundle size (gzipped)410 KB290 KB−120 KB
AssetTable render count (on filter change)82−6

If a step produced no improvement, state that explicitly. Do not fabricate numbers.


Done

List every file changed with the absolute path and a one-line explanation of what was fixed. If further gains require server-side or infrastructure changes (e.g., CDF response caching, CDN configuration), note them separately as out-of-scope recommendations.