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Argent React Native Optimization

OrganizationPopular
software-mansion
argent-react-native-optimization

Optimizes a React Native app by profiling first to find real bottlenecks, then sweeping for mechanical issues. Entry-point for all performance work. Use when the app feels slow, user asks to optimize, fix re-renders, reduce jank, or improve startup. Delegates to argent-react-native-profiler for measurement.

Overview

Publishersoftware-mansion
Repositoryargent
Skill nameargent-react-native-optimization
Stars
2.9K
Forks
124
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

    Published by software-mansion on GitHub. Read the source before you install it.

Installation

Install the Argent React Native Optimization 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/software-mansion/argent/tree/main/packages/skills/skills/argent-react-native-optimization
  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/software-mansion/argent.git /tmp/argent
mkdir -p .claude/skills
cp -r /tmp/argent/packages/skills/skills/argent-react-native-optimization .claude/skills/argent-react-native-optimization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Argent React Native Optimization 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 Argent React Native Optimization 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 Argent React Native Optimization 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.

Rules

  • Do not apply shotgun optimizations. Measure first, define what "good enough" looks like (target metric + threshold), fix the top offender, re-measure honestly.
  • Quick scan — react-profiler-renders for a live render count table. Identifies hot components instantly.
  • Deep measure — load argent-react-native-profiler skill. react-profiler-start → interact → react-profiler-stop → react-profiler-analyze.
  • Inspect — react-profiler-component-source per finding. react-profiler-fiber-tree to trace component ancestry and render cost.
  • Verify correctness - before fixing, recollect information from steps above and make a logical conclusion whether the approach is worth undertaking.
  • Fix — apply one fix. Validate with debugger-evaluate before committing.
  • Re-measure — report whether the target metric improved, regressed, or stayed flat. Check for regressions in other areas. If no net benefit or unacceptable tradeoffs, revert.
  • Profile for discovery, not only verification. Use the profiler to find issues static analysis missed, not only to confirm fixes.
  • One fix per cycle for architectural changes. Mechanical batch fixes (inline styles, index keys) can be grouped — re-profile once after the batch. When the measurement involves device interaction, record it as a flow (argent-create-flow skill) before the first run so all subsequent cycles replay identical steps.
  • React Compiler: if react-profiler-analyze reports reactCompilerEnabled: true, do NOT propose useCallback/useMemo/React.memo unless you confirmed compiler bail-out via react-profiler-fiber-tree (absent useMemoCache).
  • Sub-agents: Phases 1–2 dispatch sub-agents — one per file for lint results, one per checklist item for semantic. Sub-agents CANNOT touch the device - all profiling and E2E verification must happen in the main agent.

Pipeline

Lint and semantic sweeps catch deterministic issues cheaply. Profiling finds runtime bottlenecks that static analysis misses. Do both.

Copy this checklist into your TODO list:

Optimization Progress:
- [ ] Phase 1: Lint sweep (deterministic — catch mechanical issues without a running app)
- [ ] Phase 2: Semantic sweep (judgment — memoization, lists, animations, etc.)
- [ ] Phase 3: Baseline profile (find real bottlenecks, fix top offenders)
- [ ] Phase 4: Verify no regressions (crashes, errors, red screens)

Phase 1: Lint sweep

Run ESLint once at the project root with a comprehensive RN performance ruleset. Dispatch sub-agents to fix results — one per file. See references/lint-rules.md for ruleset and procedure.

Phase 2: Semantic sweep

Review each area requiring judgment — memoization, list rendering, animations, async patterns, effect cleanup, state hygiene, context architecture. Dispatch one sub-agent per checklist item. See references/semantic-checklist.md for full checklist.

Phase 3: Visual profiling

  1. Load argent-react-native-profiler skill, start dual profiling
  2. Exercise key user flows (navigate screens the user specified, or all major flows)
  3. Analyze with react-profiler-analyze + native-profiler-analyze + profiler-combined-report
  4. Cross-reference profiling results with Phase 1–2 findings
  5. Fix highest-impact issues. Re-profile after architectural changes; batch mechanical fixes. If a recorded flow breaks after a fix (e.g., UI layout changed), follow argent-create-flow skill to repair the flow rather than silently discarding it.

Phase 4: Verify no regressions

Navigate every screen and UI flow within scope, confirm each renders without errors. If no scope was specified, verify the entire app — cover all reachable screens via argent-device-interact. Use debugger-log-registry to check for runtime errors (if it returns status: "not_connected" there is no log file — follow its guidance to reconnect first) and take screenshots to check for red/yellow error screens. Check for regressions introduced by fixes (e.g., fewer re-renders but higher CPU, or new jank in a different screen). Main agent only.

App-wide optimization

  1. Phase 1: run lint centrally (one command), dispatch sub-agents to fix per-file in parallel
  2. Phase 2: one sub-agent per checklist item for semantic sweep
  3. Phase 3: main agent profiles top offending screens; fixes architectural issues top-down
  4. Phase 4: main agent navigates all screens to verify nothing crashes

After the entire run, run lint again to verify no new issues were introduced with your changes. This also helps ensure you haven't missed any issues which could've been fixed.

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 Argent React Native Optimization AI skill do?

Optimizes a React Native app by profiling first to find real bottlenecks, then sweeping for mechanical issues. Entry-point for all performance work. Use when the app feels slow, user asks to optimize, fix re-renders, reduce jank, or improve startup. Delegates to argent-react-native-profiler for measurement.

Why use Argent React Native Optimization on TypingMind?

Because you install it once and use it with any model. Argent React Native Optimization 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 Argent React Native Optimization in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/software-mansion/argent/tree/main/packages/skills/skills/argent-react-native-optimization. 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 Argent React Native Optimization?

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 Argent React Native Optimization?

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

Is the Argent React Native Optimization AI skill free?

Yes. It is published on GitHub by software-mansion 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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