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Agent skill

Memory Audit

by FastLED in FastLED/FastLED

Audit embedded code for stack overflow risks, heap fragmentation, static allocation patterns, and memory leaks.

MITAuto-check passedDevelopment

Install Memory Audit

skills CLI
$ npx skills add FastLED/FastLED --skill memory-audit -a claude-code

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

GitHub CLI
$ gh skill install FastLED/FastLED memory-audit --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/FastLED/FastLED.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/memory-audit .claude/skills/memory-audit && 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
memory-audit
GitHub stars
7.5k
Token cost
~488 tokens
SKILL.md length
177 words
Files
1
Skills in repo
23
Repo updated
First seen
Licence
MIT

At a glance

Audit embedded code for stack overflow risks, heap fragmentation, static allocation patterns, and memory leaks.

  • Investigating OOM crashes
  • SKILL.md covers What This Skill Analyzes, How To Use and What You'll Get
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Optimizing memory usage

What it does

Memory Audit is an agent skill from FastLED/FastLED. Audit embedded code for stack overflow risks, heap fragmentation, static allocation patterns, and memory leaks. Use when investigating OOM crashes, optimizing memory usage, or reviewing memory-critical code on constrained devices.

Its SKILL.md is about 490 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Performance optimization and Embedded systems. The repository describes itself as: The FastLED library for colored LED animation on Arduino. Please direct questions/requests for help to the FastLED Reddit community: http://fastled.io/r We'd like to use github… The licence is MIT.

When your agent uses it

  • Investigating OOM crashes
  • Optimizing memory usage
  • Reviewing memory-critical code on constrained devices

Example prompts

  • “/memory-audit”

What it can do on your machine

Read from SKILL.md and the folder at commit 8d6ed12. 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

Memory Audit loads about 488 tokens when it runs. Until then it costs about 61 tokens; SKILL.md has 177 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~61
When it runs · the whole SKILL.md, loaded when a task matches
~488

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 FastLED/FastLED at commit 8d6ed12, republished under its MIT licence (© FastLED). 177 words, ~488 tokens.

Download SKILL.mdSave it as .claude/skills/memory-audit/SKILL.md (or your agent's skills folder).
name
memory-audit
description
Audit embedded code for stack overflow risks, heap fragmentation, static allocation patterns, and memory leaks. Use when investigating OOM crashes, optimizing memory usage, or reviewing memory-critical code on constrained devices.
argument-hint
<file, directory, or component to audit>
context
fork
agent
memory-audit-agent

Audit code for memory safety issues specific to resource-constrained embedded systems.

$ARGUMENTS

What This Skill Analyzes

Stack Usage
  • Functions with large local arrays or structs (risk of stack overflow)
  • Deep call chains and recursion (stack depth estimation)
  • Task/thread stack size adequacy (FreeRTOS xTaskCreate stack parameter)
  • Alloca/VLA usage (variable-length arrays on stack)
Heap Fragmentation
  • Frequent small allocations and deallocations in hot paths
  • Mixed allocation sizes causing fragmentation over time
  • Missing deallocation (memory leaks)
  • Allocation in ISRs or time-critical code (heap locks can cause priority inversion)
Static Allocation Patterns
  • Global/static buffer sizing (too large wastes RAM, too small causes overflow)
  • Compile-time vs runtime allocation trade-offs
  • .bss and .data section usage
  • PSRAM vs internal SRAM placement decisions
Platform-Specific Concerns
  • ESP32: DRAM/IRAM split, PSRAM cache line alignment, DMA-capable memory
  • ARM Cortex-M: Stack/heap collision, MPU configuration, scatter file layout
  • AVR: 2KB SRAM constraint, PROGMEM usage for constants

How To Use

/memory-audit src/platforms/esp/32/drivers/
/memory-audit src/fl/channels/
/memory-audit Check if LED buffer allocation is safe for 1000+ LEDs on ESP32

What You'll Get

  • Memory usage breakdown by category (stack, heap, static)
  • Risk assessment for each finding (Critical/High/Medium/Low)
  • Specific recommendations with code examples
  • Platform-aware advice (e.g., "move to PSRAM" vs "reduce buffer size")

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

Files

Just SKILL.md in .claude/skills/memory-audit of FastLED/FastLED.

Open the folder on GitHubat commit 8d6ed12

Compare with similar skills

Memory Audit 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.

Memory Audit compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Memory Audit this skillFastLED/FastLED7.5k—~488Automated safety check: PassMIT
ExecuTorch Binary Size Reductionpytorch/executorch5.1k—~793Automated safety check: PassCustom licence
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

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More from FastLED/FastLED

All 23 skills in this repo

  • CI Fix

    FastLED/FastLED

    Scan all CI builds and tests, find failures, fetch error logs, and fix the code.

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  • Driver Review

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  • Embedded Debug

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  • Esp32 Log Triage

    FastLED/FastLED

    Parse and classify ESP32 serial log output to identify FastLED-related errors, RMT/I2S/SPI driver faults, timing violations, RTOS issues, and crash signatures.

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  • Platform Port

    FastLED/FastLED

    Guide porting FastLED to new MCU platforms, including int.h types, clockless drivers, SPI implementations, and platform detection.

    7.5k GitHub stars~580 tokensUpdated today
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  • TDD

    FastLED/FastLED

    Guide Test-Driven Development workflow for FastLED. An agent skill from FastLED/FastLED.

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Categories

Questions about Memory Audit

What does Memory Audit do?

Audit embedded code for stack overflow risks, heap fragmentation, static allocation patterns, and memory leaks. Memory Audit is an agent skill from FastLED/FastLED. Audit embedded code for stack overflow risks, heap fragmentation, static allocation patterns, and memory leaks.

When should I use Memory Audit?

Memory Audit fits situations like: investigating OOM crashes; optimizing memory usage; reviewing memory-critical code on constrained devices.

How do I install Memory Audit in Claude Code?

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

How do I install Memory Audit in Codex?

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

Can I use Memory Audit 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 FastLED/FastLED --skill memory-audit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/memory-audit, .gemini/skills/memory-audit, .github/skills/memory-audit and .opencode/skills/memory-audit in your project.

What does Memory Audit need to run?

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

Does Memory Audit 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 Memory Audit 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 Memory Audit use?

Memory Audit is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Memory Audit use?

About 488 tokens (SKILL.md is roughly 2k 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 Memory Audit?

Skills that share tags, products or a category with Memory Audit: ExecuTorch Binary Size Reduction (pytorch/executorch, 5.1k stars), Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), LLM Torch Profiler Analysis (sgl-project/sglang, 37k stars) and Py (crazyguitar/pysheeet, 8.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Memory Audit?

FastLED (a GitHub organization) maintains it in FastLED/FastLED, which has 7,506 GitHub stars. The repository holds 23 skills in this directory. The repository was last updated on October 7, 2026.

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