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Simplify Trace

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tldraw
simplify-trace

Summarize a large Chrome DevTools performance trace into a compact markdown report so it can be reasoned about without loading the whole file. Use when given a Chrome/DevTools/Performance-panel trace (a multi-MB `Trace-*.json` or `*.json` with `traceEvents`) and asked to find what is slow, what runs too often, long tasks, jank, or hot JS functions.

Overview

Publishertldraw
Repositorytldraw
Skill namesimplify-trace
Stars
50.4K
Forks
3.5K
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Simplify Trace 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.
  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/tldraw/tldraw.git /tmp/tldraw
mkdir -p .claude/skills
cp -r /tmp/tldraw/skills/simplify-trace .claude/skills/simplify-trace
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Simplify Trace 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 Simplify Trace 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 Simplify Trace 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.

Simplify trace

Chrome DevTools performance traces are tens to hundreds of MB of JSON — far too large to read directly. This skill turns one into a few-KB markdown report that surfaces the work taking too long or happening too often.

Usage

Run the script on the trace file. It prints markdown to stdout (or --out FILE):

bash
node skills/simplify-trace/scripts/simplify-trace.mjs <trace.json> [--top N] [--long-task-ms MS] [--window START-END] [--out report.md]
  • --top N — rows per table (default 25).
  • --long-task-ms MS — long-task threshold (default 50).
  • --window START-END — scope the whole report to a time slice (offsets in ms from trace start).
  • --only a,b / --except a,b / --all — pick which sections to emit.
  • --match TEXT — case-insensitive filter for rows (event names, function names, URLs).
  • --list — print the available section keys and exit.
  • --out FILE — write to a file instead of stdout.
  • Traces over ~500 MB: prefix node --max-old-space-size=8192.

Default to running with --out to a temp file for big traces, then read the report. Don't read the raw trace.

Sections

Default sections: summary, longtasks, events, frequent, functions, categories. Opt-in: timeline (per-second main-thread busy time — use to locate activity), network (resource/fetch waterfall with TTFB/duration/size), websocket (WebSocket lifecycle — /app/file doc sync). Run --list for the full set. Interrogate narrowly, e.g.:

bash
# where is the activity? then window to it
node …/simplify-trace.mjs trace.json --only timeline
# what was the network/socket doing during the action?
node …/simplify-trace.mjs trace.json --only network,websocket --window 62000-69000 --match tldraw

The trace's metadata.startTime (ISO/UTC) anchors offset 0 to wall-clock, so trace offsets can be lined up against server logs (zero-cache, sync-worker) by timestamp. For an idle, network-quiet gap, the trace shows when but not why — add performance.mark()/console.timeStamp() in the client path and they appear on the trace timeline.

"What happens when I do X" traces

A recording of a single action (switch file, open menu) is mostly idle setup time, which dilutes the action across the whole trace. Window to the action instead:

  1. Run once with no window. Note where activity is — the long tasks' offsets, or bucket main-thread busy-time per second with a quick inline script to find the active span.
  2. Re-run with --window START-END around that span. All tables then describe only the action.

The recording artifact CpuProfiler::StartProfiling (the profiler turning on, ~50–60ms) is excluded from the long-task table, including when it's nested inside a RunTask. If long tasks shows "None", the action genuinely has no single blocking task — look at aggregate self time, GC, and animation-loop events instead.

What the report contains

  • Header — event count, wall-clock span, sampled JS CPU time, and idle %.
  • Long tasks — top-level tasks over the threshold (main-thread jank), with the time offset where each occurred.
  • Hottest event types (self time) — where engine/browser time actually goes (layout, GC, paint, function calls), excluding time spent in nested children.
  • Most frequent event types (count) — work happening too often.
  • Hottest JS functions — bottom-up self time from the embedded V8 CPU profile, with file:line. Synthetic (idle)/(program) frames are excluded here (idle is in the header).
  • Self time by category — high-level breakdown across trace categories.

How to read it

  • A function high in self time is the actual cost; high total but low self means the cost is in its callees — follow the call tree.
  • High count with low avg = death by a thousand cuts (often a reactive/render loop firing too often); investigate why it fires, not its per-call cost.
  • Long tasks point at when jank happened; cross-reference the offset against what the user was doing.
  • Minified names (r, Tg) come with a file:line — use it to locate the source.

The script only summarizes; it does not modify the trace.

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 Simplify Trace AI skill do?

Summarize a large Chrome DevTools performance trace into a compact markdown report so it can be reasoned about without loading the whole file. Use when given a Chrome/DevTools/Performance-panel trace (a multi-MB `Trace-*.json` or `*.json` with `traceEvents`) and asked to find what is slow, what runs too often, long tasks, jank, or hot JS functions.

Why use Simplify Trace on TypingMind?

Because you install it once and use it with any model. Simplify Trace 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 Simplify Trace in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tldraw/tldraw/tree/main/skills/simplify-trace. 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 Simplify Trace?

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 Simplify Trace?

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

Is the Simplify Trace AI skill free?

It is published on GitHub by tldraw. Check the repository for licensing terms. 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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