alloc-profile

name	alloc-profile
description	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 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:

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