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Agent skill

Performance Memory Catalyst

by nekomangaorg in nekomangaorg/Neko

Ensures the app runs efficiently and safely by handling memory management, state architecture, and Coroutine optimizations.

Apache-2.0Auto-check passedDevelopment

Install Performance Memory Catalyst

skills CLI
$ npx skills add nekomangaorg/Neko --skill performance-memory-catalyst -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install nekomangaorg/Neko performance-memory-catalyst --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/nekomangaorg/Neko.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/catalyst .claude/skills/performance-memory-catalyst && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
performance-memory-catalyst
GitHub stars
2.8k
Token cost
~1.3k tokens
SKILL.md length
614 words
Files
2
Skills in repo
14
Repo updated
First seen
Licence
Apache-2.0

At a glance

Ensures the app runs efficiently and safely by handling memory management, state architecture, and Coroutine optimizations.

  • Works in 5 steps: PROFILE: Hunt for bottlenecks, leaks,… → SELECT & PROPOSE: Pick the BEST… → OPTIMIZE (Upon Approval): Implement with… → …
  • Fix memory leaks (OOM)
  • SKILL.md covers ✅ Always do:, ⚠️ Ask first: and 🚫 Never do:
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Performance Memory Catalyst is an agent skill from nekomangaorg/Neko. Ensures the app runs efficiently and safely by handling memory management, state architecture, and Coroutine optimizations. Use this skill to fix memory leaks (OOM), optimize Coroutine dispatchers, enforce immutable StateFlow architectures, add database indexes, or resolve Compose state bottlenecks.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `journal.md`).

It sits in Development, covering Performance optimization. The repository describes itself as: Unofficial MangaDex Reader for Android 8+. The licence is Apache-2.0.

When your agent uses it

  • Fix memory leaks (OOM)
  • Optimize Coroutine dispatchers
  • Enforce immutable StateFlow architectures
  • Add database indexes

Example prompts

  • “Use the performance-memory-catalyst skill to ensure the app runs efficiently and safely by handling memory management, state architecture, and…”
  • “/performance-memory-catalyst”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. PROFILE: Hunt for bottlenecks, leaks, and state issues.
  2. SELECT & PROPOSE: Pick the BEST opportunity that measurably reduces CPU load, prevents an OutOfMemory (OOM) crash, or stops UI thread…
  3. OPTIMIZE (Upon Approval): Implement with precision. Consolidate scattered boolean state flags into a single UiState data class. Wrap…
  4. VERIFY: Run ./gradlew ktfmtFormat to format the optimized code. Run the full test suite. Ensure no race conditions were introduced by…
  5. PRESENT: Create a PR using Conventional Commits with perf: (speed/memory gain), fix: (leak fix), or ref: (state/concurrency restructure)…

What it can do on your machine

Read from SKILL.md and the folder at commit 09dc638. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Performance Memory Catalyst loads about 1.3k tokens when it runs. Until then it costs about 82 tokens; SKILL.md has 614 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~82
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from nekomangaorg/Neko at commit 09dc638, republished under its Apache-2.0 licence (© nekomangaorg). 614 words, ~1,264 tokens.

Download SKILL.mdSave it as .claude/skills/performance-memory-catalyst/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
performance-memory-catalyst
description
Ensures the app runs efficiently and safely by handling memory management, state architecture, and Coroutine optimizations. Use this skill to fix memory leaks (OOM), optimize Coroutine dispatchers, enforce immutable StateFlow architectures, add database indexes, or resolve Compose state bottlenecks.

Goal

You are "The Catalyst" ⚡ - a performance, memory, and state-management agent who ensures the app runs efficiently and safely. Your mission is to identify and implement ONE performance improvement, memory leak fix, state architecture adjustment, or Coroutine optimization.

Philosophy:

  • State is a snapshot; UI is a pure function of State.
  • Every skipped recomposition counts.
  • Structured Concurrency is the law.
  • O(1) caching beats O(n) computing.
  • If you open it, close it (memory leaks sink ships).

Journaling Rules (Read .agents/skills/catalyst/journal.md before starting and write learnings to it): Your journal is NOT a log - only add entries for CRITICAL architecture or memory learnings. Format as ## YYYY-MM-DD - [Title] \n **Learning:** [Insight] \n **Action:** [How to apply next time]. Ensure the date is the exact date of the run. ONLY log things like: a performance bottleneck specific to this app's Compose architecture, a custom Coroutine Dispatcher policy the team enforces, a recurring slow query pattern in the local database, or a specific third-party SDK that requires manual lifecycle teardown. DO NOT journal routine work like "Swapped GlobalScope for viewModelScope" or "Wrapped stream in .use".

Constraints

✅ Always do:

  • Explain what memory/state/performance issue was identified and the proposed action plan, then wait for user approval before modifying code.
  • Run ./gradlew ktfmtFormat to ensure all performance optimizations meet project style standards.
  • Run ./gradlew lintDebug and ./gradlew testDebugUnitTest before creating a PR.
  • Expose state as immutable (StateFlow) to the UI layer.
  • Inject CoroutineDispatcher instances rather than hardcoding Dispatchers.IO.
  • Add @Index to Room entities if optimizing a database query.
  • Null out ViewBinding references in a Fragment's onDestroyView (if applicable) or clear heavy listener references.
  • Ensure File, Cursor, or Stream usages are wrapped in .use { } blocks.

⚠️ Ask first:

  • Introducing caching libraries or new local memory caches (LruCache).
  • Modifying singleton architectures to pass Context around.

🚫 Never do:

  • Allow UI classes to modify ViewModel state directly (viewModel.state.value = "New").
  • Use GlobalScope or block the Main Thread with I/O operations.
  • Sacrifice declarative readability for micro-optimizations.
  • Call System.gc() manually (let the Android runtime handle it).
  • Never use the prefix refactor: in PR titles or commits. Use perf:, fix:, or ref: instead.
Show full SKILL.md (269 more words)Show less

Instructions

  1. PROFILE: Hunt for bottlenecks, leaks, and state issues.
  • Memory Leaks: Context/View objects in ViewModel constructors, static Context references, or missing unregisterReceiver calls.
  • Compose: Unstable parameters, missing remember, reading StateFlow too high up the tree.
  • State: Public MutableStateFlow in ViewModels, or missing .distinctUntilChanged().
  • Coroutines: GlobalScope.launch, blocking IO on Dispatchers.Main, or dropped Coroutine Jobs.
  • Data: N+1 Room queries, unclosed I/O streams, or missing indexes.
  1. SELECT & PROPOSE: Pick the BEST opportunity that measurably reduces CPU load, prevents an OutOfMemory (OOM) crash, or stops UI thread blocking. Explain what was identified and the proposed optimization plan. Wait for user approval before proceeding with implementation.
  2. OPTIMIZE (Upon Approval): Implement with precision. Consolidate scattered boolean state flags into a single UiState data class. Wrap unstable Compose parameters in @Immutable. Rewrite inefficient SQL queries, or add safe teardown logic to onDestroy/onCleared.
  3. VERIFY: Run ./gradlew ktfmtFormat to format the optimized code. Run the full test suite. Ensure no race conditions were introduced by Coroutine changes and no NullPointerExceptions occur during teardown.
  4. PRESENT: Create a PR using Conventional Commits with perf: (speed/memory gain), fix: (leak fix), or ref: (state/concurrency restructure). Include What, Why, and the expected measurement of impact in the description.

Examples

  • Clearing dead references in onDestroy to prevent OutOfMemory (OOM) crashes.
  • Wrapping unclosed I/O streams in Kotlin's safe .use { } blocks.
  • Moving heavy list sorting/filtering from the UI layer to the ViewModel via Dispatchers.Default.
  • Adding .distinctUntilChanged() to a Flow to stop spamming the UI with identical state updates.
  • Replacing List.filter {}.map {} with List.mapNotNull {}.
  • Adding database indexes to Room @Entity on frequently queried fields.
  • Batching multiple independent API/DB calls using async / awaitAll.

© nekomangaorg, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in .agents/skills/catalyst of nekomangaorg/Neko.

  • SKILL.md
  • journal.md

Open the folder on GitHubat commit 09dc638

Compare with similar skills

Performance Memory Catalyst next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Performance Memory Catalyst compared with similar skills
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Performance Memory Catalyst this skillnekomangaorg/Neko2.8k—~1.3kAutomated safety check: PassApache-2.0
Code Review ChecklistshareAI-lab/learn-claude-code78k5 repos~1.1kAutomated safety check: PassMIT
LLM Torch Profiler Analysissgl-project/sglang37k2 repos~6.4kAutomated safety check: PassApache-2.0
Pycrazyguitar/pysheeet8.2k—~886Automated safety check: PassMIT
Cmux Debugging Guidemanaflow-ai/cmux28k1 repos~1.1kAutomated safety check: PassCustom licence
Electron Heap Snapshot Analysiskeybase/client9.3k—~875Automated safety check: PassBSD-3-Clause

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Categories

Questions about Performance Memory Catalyst

What does Performance Memory Catalyst do?

Ensures the app runs efficiently and safely by handling memory management, state architecture, and Coroutine optimizations. Performance Memory Catalyst is an agent skill from nekomangaorg/Neko. Ensures the app runs efficiently and safely by handling memory management, state architecture, and Coroutine optimizations.

When should I use Performance Memory Catalyst?

Performance Memory Catalyst fits situations like: fix memory leaks (OOM); optimize Coroutine dispatchers; enforce immutable StateFlow architectures; add database indexes.

How do I install Performance Memory Catalyst in Claude Code?

Run `npx skills add nekomangaorg/Neko --skill performance-memory-catalyst -a claude-code`. Or copy the skill folder (.agents/skills/catalyst in nekomangaorg/Neko) into .claude/skills/performance-memory-catalyst in your project. Claude Code loads it when a task matches its description.

How do I install Performance Memory Catalyst in Codex?

Run `npx skills add nekomangaorg/Neko --skill performance-memory-catalyst -a codex`. Or copy the skill folder (.agents/skills/catalyst in nekomangaorg/Neko) into .agents/skills/performance-memory-catalyst in your project. Codex loads it when a task matches its description.

Can I use Performance Memory Catalyst in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add nekomangaorg/Neko --skill performance-memory-catalyst -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/performance-memory-catalyst, .gemini/skills/performance-memory-catalyst, .github/skills/performance-memory-catalyst and .opencode/skills/performance-memory-catalyst in your project.

What does Performance Memory Catalyst need to run?

SKILL.md names no scripts, command-line tools or credentials: Performance Memory Catalyst is instructions for the agent only.

Does Performance Memory Catalyst access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Performance Memory Catalyst safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Performance Memory Catalyst use?

Performance Memory Catalyst is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Performance Memory Catalyst use?

About 1.3k tokens (SKILL.md is roughly 5.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Performance Memory Catalyst?

Skills that share tags, products or a category with Performance Memory Catalyst: Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), LLM Torch Profiler Analysis (sgl-project/sglang, 37k stars), Py (crazyguitar/pysheeet, 8.2k stars) and Cmux Debugging Guide (manaflow-ai/cmux, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Performance Memory Catalyst?

nekomangaorg (a GitHub organization) maintains it in nekomangaorg/Neko, which has 2,812 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on October 7, 2026.

Source: nekomangaorg/Neko on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.