How do I install the Alloc Profile skill?

Run npx skillmds@latest add timeplus-io/alloc-profile in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Alloc Profile safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Alloc Profile?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Alloc Profile free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Alloc Profile?

timeplus-io (@timeplus-io) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Alloc Profile by timeplus-io — Agent Skill

Name: Alloc Profile
Author: timeplus-io

Alloc Profile

Analyze jemalloc or async-profiler allocation profiles in collapsed stack format. Make sure to use this skill whenever the user mentions memory profiling, heap analysis, allocation hotspots, jemalloc, async-profiler, collapsed stacks, .folded files, or asks about memory usage patterns.

timeplus-io 07a8995 2 files · 7.0 KB Updated Sep 17, 2026

File contents

Allocation Profile

Inputs

$0 (optional): path to a .collapsed or .folded profile

If no path is given, search near the current directory:

find . -maxdepth 4 \( -name "*.collapsed" -o -name "*.folded" \) | sort

Tooling

Use the bundled analyzer script:

python3 .claude/skills/alloc-profile/scripts/analyze_alloc_profile.py <profile>

What the analyzer reports

Total samples/bytes and unique stack traces
Top stack traces
Top outermost meaningful frames
Top leaf allocation frames

This gives both:

"why did we allocate?" via outermost frames
"what code actually allocated?" via leaf frames

Interpretation guidance

Large outermost buckets often point to the higher-level operation to optimize first.
Large leaf buckets inside allocators or containers are usually symptoms; look one or two frames above for product code.
For streaming paths, pay special attention to joins, windows, aggregation state, and block materialization.
For historical paths, focus on scans, merges, deserialization, and sort/aggregation buffers.

Related repo context

Jemalloc profiling notes live in tests/instructions/jemalloc_memory_profile.txt.
Generic heap-profiler notes live in tests/instructions/heap-profiler.txt.

Output expectations

Summarize:

biggest operations by memory share
likely owning subsystem
likely next inspection target in code
whether the profile looks like steady-state memory, burst allocation, or leak-like retention

---name: alloc-profiledescription: Analyze jemalloc or async-profiler allocation profiles in collapsed stack format. Make sure to use this skill whenever the user mentions memory profiling, heap analysis, allocation hotspots, jemalloc, async-profiler, collapsed stacks, .folded files, or asks about memory usage patterns.---# Allocation Profile## Inputs- `$0` (optional): path to a `.collapsed` or `.folded` profileIf no path is given, search near the current directory:```bashfind . -maxdepth 4 $ -name "*.collapsed" -o -name "*.folded" $ | sort```## ToolingUse the bundled analyzer script:```bashpython3 .claude/skills/alloc-profile/scripts/analyze_alloc_profile.py <profile>```## What the analyzer reports- Total samples/bytes and unique stack traces- Top stack traces- Top outermost meaningful frames- Top leaf allocation framesThis gives both:- "why did we allocate?" via outermost frames- "what code actually allocated?" via leaf frames## Interpretation guidance- Large outermost buckets often point to the higher-level operation to optimize first.- Large leaf buckets inside allocators or containers are usually symptoms; look one or two frames above for product code.- For streaming paths, pay special attention to joins, windows, aggregation state, and block materialization.- For historical paths, focus on scans, merges, deserialization, and sort/aggregation buffers.## Related repo context- Jemalloc profiling notes live in [tests/instructions/jemalloc_memory_profile.txt](../../../tests/instructions/jemalloc_memory_profile.txt).- Generic heap-profiler notes live in [tests/instructions/heap-profiler.txt](../../../tests/instructions/heap-profiler.txt).## Output expectationsSummarize:1. biggest operations by memory share2. likely owning subsystem3. likely next inspection target in code4. whether the profile looks like steady-state memory, burst allocation, or leak-like retention

Frequently asked questions