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Compose Performance

CommunityPopular
chrisbanes
compose-performance

Use when investigating Jetpack Compose recomposition cost, compiler stability reports, skippability, unstable parameters, frame-rate State reads, cross-phase snapshot back-writing, or @ReadOnlyComposable contracts.

Overview

Publisherchrisbanes
Repositoryskills
Skill namecompose-performance
Stars
1.1K
Forks
53
Bundled files
4
LicenseApache-2.0
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.

  • 4 bundled files

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

  • Open source

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

Installation

Install the Compose Performance 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/chrisbanes/skills/tree/main/skills/compose-performance
  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/chrisbanes/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/compose-performance .claude/skills/compose-performance
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Compose Performance 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 Compose Performance 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 Compose Performance 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.

Compose performance

Core principle

Measure one user-visible transition, identify the runtime axis that causes the work, then apply the smallest correction at the phase or boundary where that axis begins.

Procedure

  1. Reproduce one concrete transition and capture the observable evidence: recomposition counts, compiler reports, profiler data, or a clear trace.
  2. Classify the primary axis: parameter stability and skipping, State read phase, or snapshot state written back into an earlier phase.
  3. Check for a false lead: a real data change, a correctness defect, or an unchanged lazy item that is expected to recompose.
  4. Read the corresponding focused reference before proposing a change.
  5. For review work, pair every finding with the smallest evidence-supported repair. For a false stability promise, explicitly say to replace mutable non-snapshot properties with immutable data or snapshot-observable state, verify that contract, and only then decide whether an annotation remains needed. For a phase problem, name the layout or draw consumer where the changing read or calculation should move. A cross-phase write proves invalidation or an extra pass; do not claim oscillation or an infinite loop unless evidence shows values repeatedly changing. Do not stop at diagnosis. When only source code is available, separate the proven feedback path from its unmeasured runtime cost. Give a conditional repair direction based on the intended measurement boundary; do not prescribe a callback swap, equality guard, or content-layout rewrite as a performance fix without evidence that it changes the observed problem. For an Int held in mutableStateOf with the default structural equality policy, an unchanged value already avoids invalidation.
  6. Change one axis at a time and re-measure the same transition.
  7. Finish when the evidence improves at the observed boundary without hiding state changes, caching stale values, or moving work to a less correct owner.

Topic router

SignalRead
Cause is unknown, or several axes may interactDiagnosis
classes.txt, composables.txt, strong skipping, unstable parameters, or collection stabilityStability
Scroll, animation, gesture, layout, or draw State read at frame rate; measured state fed back into compositionDeferred reads
A composable only reads composition locals or accessor-style valuesComposition contracts
State ownership or effect lifecycle is the root causeCompose state and effects

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 Compose Performance AI skill do?

Use when investigating Jetpack Compose recomposition cost, compiler stability reports, skippability, unstable parameters, frame-rate State reads, cross-phase snapshot back-writing, or @ReadOnlyComposable contracts.

Why use Compose Performance on TypingMind?

Because you install it once and use it with any model. Compose Performance 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 Compose Performance in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chrisbanes/skills/tree/main/skills/compose-performance. 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 Compose Performance?

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 Compose Performance?

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

Is the Compose Performance AI skill free?

Yes. It is published on GitHub by chrisbanes under the Apache-2.0 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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