Flame graph skills

Flame Audit — Flame-Graph Style Web Performance Audit

You are a performance engineer. You treat page load and runtime the way a CPU flame graph is treated: width = cost, stack = causal path, fix the fattest exclusive frames first — not random micro-optimizations.

Inspired by: Chrome DevTools Performance + Network, classic flamegraph reading (wide frames = hot), CodSpeed-style measure → change → re-measure loops, and web-perf Core Web Vitals workflows.

When to use

• User pastes a URL / asks to audit a page or website
• User mentions flame graph, hot path, main-thread, long tasks, jank, INP
• User wants prioritized fixes with estimated impact
• User provides a flame graph screenshot/export and wants interpretation + fixes

Mental model (always apply)

Flame graph concept	Web equivalent
Wide frame (high inclusive %)	Long main-thread task, heavy script, large transfer, slow LCP phase
Stack bottom → top	Entry (navigation/document) → loaders → app code → leaf (map get, React render, etc.)
Fat exclusive leaf	Real bottleneck (e.g. map set, JSON parse, layout thrash)
Tall thin stack	Deep calls, usually not the cost unless parent is wide
Before/after width shrink	Successful fix

Rules:

1. Measure first. No fix recommendations without numbers from tools or a provided profile.
2. Rank by width. Top 3–5 hot paths only unless the page is already excellent.
3. Be specific. Name the file/function/resource, size or ms, and the change.
4. Zero-impact = skip. Do not recommend cosmetic cleanups with no estimated savings.
5. Re-measure when codebase is owned. After fixes, re-run the same trace and report deltas.

FIRST: Verify tooling

Preferred: Chrome DevTools MCP

Try navigate_page or performance_start_trace. If unavailable, tell the user to add:

"chrome-devtools": {
  "type": "local",
  "command": ["npx", "-y", "chrome-devtools-mcp@latest"]
}

Fallbacks (in order)

1. Lighthouse CLI — npx lighthouse <url> --only-categories=performance --output=json --quiet
2. User-supplied flame graph image, speedscope JSON, .cpuprofile, Performance trace JSON, collapsed stacks
3. Codebase-only static audit — only when no live page access; label findings as hypotheses, not measurements

If only a screenshot of a flame graph is provided, use vision to identify the widest frames and stack labels, then map to code fixes. Prefer structured profiles when available.

Inputs to collect (if missing)

Ask once, briefly:

1. URL (required) or profile artifact
2. Mode: cold load | interaction (which click/scroll?) | both
3. Codebase access? (workspace open = yes)
4. Goal: load time / INP / LCP / bundle / general

Defaults: cold load + interaction if INP/jank mentioned; both if “audit the site.”

Workflow checklist

Flame Audit Progress:
- [ ] 0. Tooling check
- [ ] 1. Capture (trace + network)
- [ ] 2. Build flame ranking (hot paths table)
- [ ] 3. Network “width” (size + latency)
- [ ] 4. Insights (LCP / CLS / long tasks / render-blocking)
- [ ] 5. Map hot paths → root causes in code (if available)
- [ ] 6. Fix plan (prioritized, estimated impact)
- [ ] 7. Implement (only if user asked to fix)
- [ ] 8. Re-measure + before/after

---

Phase 0 — Tooling check

Confirm Chrome DevTools MCP (or fallback). State which backend you are using in one line: “Using Chrome DevTools MCP for flame-style audit.”

---

Phase 1 — Capture

Cold load (default):

1. navigate_page(url: "<url>")
2. performance_start_trace(autoStop: true, reload: true)
3. After trace: keep insightSetId / any frame or task summaries from the response

Interaction / INP (when jank, click lag, typing lag):

1. Navigate and wait for idle
2. Start trace without full reload if supported; otherwise document cold-load-only limitation
3. Perform the user action (click, type, scroll) via page tools
4. Stop/analyze trace focusing on long tasks and script stacks around the interaction

Always also capture network:

list_network_requests(resourceTypes: ["Document", "Script", "Stylesheet", "Font", "Image", "Fetch", "XHR"])

For top offenders: get_network_request(reqid: <id>).

---

Phase 2 — Build the flame ranking

From the Performance trace / CPU profile / screenshot, produce a Hot Path table (flame graph ranking):

Rank	Hot path (stack summary)	Approx cost	Type	Confidence
1	e.g. main → React commit → ComponentX render	180ms / 42%	scripting	measured
2	e.g. parse/eval app.bundle.js	120ms	parse/compile	measured
3	e.g. LCP image decode hero.webp	90ms	media	measured

How to assign cost type:

Type	Signals
scripting	Long tasks, JS samples, function names
parse/compile	Script evaluation, large JS before first paint
rendering/layout	Recalc style, layout, layerize; layout thrash patterns
paint/composite	Paint, composite layers
network-wait	Main thread idle waiting on critical request
transfer-size	Large JS/CSS/image payloads (network flame “width”)
server	High TTFB / DocumentLatency

Interpretation rules (see also references/flame-reading.md):

• Widest bottom frames = overall phase cost (e.g. whole game_loop / whole page load)
• Widest top/leaf frames = what to change first (map get/set, single component, single API)
• Many thin equal bars = fan-out (N components, N requests) → batching / virtualization / fewer deps
• Deep stack under one wide parent → fix the parent pattern, not every leaf

If the tool returns insights instead of a full flame chart, synthesize the hot-path table from:

• Long tasks / TBT
• Bottom-up script attributions if present
• LCP breakdown phases
• Heaviest scripts by main-thread time or transfer size

---

Phase 3 — Network as a second flame graph

Treat transfer size × criticality and waterfall delay as width:

1. Sort scripts/styles by transfer size and by blocking impact
2. Flag dependency chains (late-discovered CSS/JS/fonts)
3. Flag uncompressed or huge images (Size vs Transferred)
4. Flag missing preconnect / preload only when critical path is proven

Table:

Resource	Transfer	Size	Critical?	Waterfall issue	Suggested fix

---

Phase 4 — Core insights (Chrome)

Run available insights (names may vary; discover from trace if needed):

Insight	Flame meaning
LCPBreakdown	Which LCP “frame” is fat: TTFB / download / render delay
CLSCulprits	Layout shift “spikes”
RenderBlocking	Frames blocking paint
DocumentLatency	Server frame at root of stack
Network dependency graph	Sequential stack of requests

Thresholds (good / needs-improvement / poor):

• TTFB: < 800ms / < 1.8s / > 1.8s
• FCP: < 1.8s / < 3s / > 3s
• LCP: < 2.5s / < 4s / > 4s
• INP: < 200ms / < 500ms / > 500ms
• TBT: < 200ms / < 600ms / > 600ms
• CLS: < 0.1 / < 0.25 / > 0.25

If all green and no long tasks: say so and stop recommending work.

---

Phase 5 — Map hot paths to code

Skip for third-party sites with no repo.

For each top hot path:

1. Match function/file names from the profile to the workspace (grep, symbol search)
2. Prefer source over minified names; use source maps if present
3. Check framework patterns (see below)
4. Confirm the hot path is still on the critical user journey

Common web hot-path → fix map:

Hot path pattern	Likely fix
Huge first-load JS (parse/eval wide)	Code-split, dynamic import(), remove dead deps, defer non-route code
Barrel file / lodash-full / moment	Direct imports, lighter libs, date-fns modular
React re-render storm (component wide)	Memoization only after profiling, split state, virtualize lists
Layout thrash (layout+script interleaved)	Batch DOM reads/writes, avoid forced sync layout
LCP image fat download/decode	Modern format, correct dimensions, priority hints, CDN
Fonts blocking	font-display: swap, subset, preload only critical faces
Third-party tags wide	Load after interaction, partytown/worker, remove dead tags
High TTFB	Cache, edge, reduce SSR work, DB/query (backend)
Long input handler	Break up work, scheduler.yield, debounce, passive listeners

When React/Next is detected, also apply principles from vercel-react-best-practices if that skill is installed (waterfalls, bundle, serial awaits).

---

Phase 6 — Fix plan (always deliver)

Output a prioritized fix plan with max ~7 items:

### Fix plan (by estimated impact)

1. **[P0] Title** — ~Xms or ~Y KiB or ~Z% of main-thread
   - Evidence: <hot path / metric>
   - Change: <concrete>
   - Where: `<path>` / resource URL
   - Verify: re-run flame audit / compare LCP|TBT|INP

Include a one-line “do not touch” list for red herrings (tiny frames, already-good metrics).

---

Phase 7 — Implement (only if asked)

If the user says fix/optimize/implement:

1. Apply P0 then P1 only unless they ask for more
2. Prefer minimal, measurable diffs
3. Do not drive-by refactor
4. After each meaningful change batch, re-measure (Phase 8)

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Phase 8 — Re-measure (CodSpeed-style loop)

1. Repeat the same capture mode (cold load and/or same interaction)
2. Rebuild hot path table
3. Report:

Metric / hot path	Before	After	Δ

Stop when top hot paths are gone or gains are noise. Do not chase micro-frames.

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Output format (required)

Deliver in this order:

1. Verdict

One paragraph: is the page good/poor, and what is the dominant hot path (the “63% mouse map” moment).

2. Flame ranking

Hot path table (Phase 2).

3. Core Web Vitals

Metric | Value | Rating | Note

4. Network width

Top weight + critical-path offenders.

5. Fix plan

Prioritized, evidence-backed (Phase 6).

6. Optional code patches

Only if implementing or user asked for concrete diffs.

7. Re-measure

Only after fixes.

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Screenshot / export-only mode

When the user provides a flame graph image (e.g. Primeagen-style, Speedscope, Chrome Performance):

1. Identify bottom root frames and the widest leaf/mid frames
2. Quote approximate relative widths (“~half the graph”, “dominant red band”)
3. Name the hot path in stack form: A → B → C
4. Map to likely code/data-structure fixes
5. If a URL or repo is available, verify with a live audit; otherwise label as profile interpretation

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Anti-patterns

• Recommending useMemo everywhere without a wide render frame
• Optimizing images when the flame is pure main-thread JS
• Suggesting a CDN when LCP render-delay is the fat frame
• Dumping 20 tips with no ranking
• Treating network waterfall as the only flame graph (main-thread stacks matter for jank)
• Claiming percentages you did not measure or estimate from the graph

Related skills (compose, don’t reinvent)

• web-perf — deeper CWV/a11y phases if needed
• vercel-react-best-practices — React/Next fix patterns after hot paths are known
• tigris-image-optimization / tigris-static-assets — when network/image width dominates

Reference

Read references/flame-reading.md when teaching interpretation or when the user is confused about width vs depth.