| 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
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
