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Skills/cognitedata/builder-skills/performance
performance logo

performance

cognitedata/builder-skills
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Installation

npx skills add https://github.com/cognitedata/builder-skills --skill performance

Summary

MUST be used whenever fixing performance issues in a Flows app. This skill finds AND fixes performance problems — re-renders, inefficient queries, missing pagination, unbounded fetches, large bundles, and memory leaks. It does not just report them. Always measure before and after. Triggers: performance, slow, laggy, optimize, re-render, bundle size, load time, CDF query, large list, memory leak, debounce, virtualize, lazy load, code split.

SKILL.md

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.

---

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.

Score

0–100
63/ 100

Grade

C

Popularity15/30

1,825 installs — growing adoption.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Performance skill score badge previewScore badge

Markdown

[![Performance skill](https://www.remoteopenclaw.com/skills/cognitedata/builder-skills/performance/badges/score.svg)](https://www.remoteopenclaw.com/skills/cognitedata/builder-skills/performance)

HTML

<a href="https://www.remoteopenclaw.com/skills/cognitedata/builder-skills/performance"><img src="https://www.remoteopenclaw.com/skills/cognitedata/builder-skills/performance/badges/score.svg" alt="Performance skill"/></a>

Performance FAQ

How do I install the Performance skill?

Run “npx skills add https://github.com/cognitedata/builder-skills --skill performance” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Performance skill do?

MUST be used whenever fixing performance issues in a Flows app. This skill finds AND fixes performance problems — re-renders, inefficient queries, missing pagination, unbounded fetches, large bundles, and memory leaks. It does not just report them. Always measure before and after. Triggers: performance, slow, laggy, optimize, re-render, bundle size, load time, CDF query, large list, memory leak, debounce, virtualize, lazy load, code split. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Performance skill free?

Yes. Performance is a free, open-source skill published from cognitedata/builder-skills. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Performance work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Performance works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

Featured

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Try Firecrawl free →
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QwikClaw sets up and runs an always-on OpenClaw agent for you. One click, no config files, no server setup.

Deploy now →
One API to scrape, enrich, and extract the internet. logoOne API to scrape, enrich, and extract the internet.

Context.dev gives your agents a single API to scrape, enrich, and extract live web data — no proxies, no parsers, no maintenance.

Start building free →
Deploy OpenClaw in 60 seconds — 20% off logoDeploy OpenClaw in 60 seconds — 20% off

Launch OpenClaw on Hostinger in about 60 seconds and keep your agent live 24/7. Our referral link gives you 20% off, no coupon code needed.

Launch on Hostinger →
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Launch on Hostinger →
Turn any website into LLM-ready data with Firecrawl logoTurn any website into LLM-ready data with Firecrawl

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Try Firecrawl free →
Your own AI agent, running 24/7 with QwikClaw logoYour own AI agent, running 24/7 with QwikClaw

QwikClaw sets up and runs an always-on OpenClaw agent for you. One click, no config files, no server setup.

Deploy now →
One API to scrape, enrich, and extract the internet. logoOne API to scrape, enrich, and extract the internet.

Context.dev gives your agents a single API to scrape, enrich, and extract live web data — no proxies, no parsers, no maintenance.

Start building free →
Deploy OpenClaw in 60 seconds — 20% off logoDeploy OpenClaw in 60 seconds — 20% off

Launch OpenClaw on Hostinger in about 60 seconds and keep your agent live 24/7. Our referral link gives you 20% off, no coupon code needed.

Launch on Hostinger →
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Launch on Hostinger →
Turn any website into LLM-ready data with Firecrawl logoTurn any website into LLM-ready data with Firecrawl

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Try Firecrawl free →
Your own AI agent, running 24/7 with QwikClaw logoYour own AI agent, running 24/7 with QwikClaw

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One API to scrape, enrich, and extract the internet. logoOne API to scrape, enrich, and extract the internet.

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