How do I install the Rust Profiling skill?

Run npx skillmds@latest add blacktop/rust-profiling 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 Rust Profiling safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts, makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Rust Profiling?

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 Rust Profiling free to use?

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

Who published Rust Profiling?

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

Rust Profiling by blacktop — Agent Skill

Name: Rust Profiling
Rating: 4.2 (5 reviews)
Author: blacktop

Rust Profiling

Profile Rust code using samply and related evidence to identify CPU bottlenecks, allocation hot paths, and allocator-fragmentation symptoms. Use when performance is slow, RSS grows unexpectedly, before optimizing, or when the user asks to profile.

blacktop c766010 5 files · 39.3 KB Updated Sep 9, 2026

File contents

Rust Profiling with Samply

Profile Rust binaries to find CPU bottlenecks and allocation pressure using samply, then use OS-level memory evidence when RSS or fragmentation is the symptom.

Quick Start

# 1. Ensure profiling profile exists in Cargo.toml (see reference.md)
# 2. Build with debug symbols
cargo build --profile profiling

# 3. Profile (opens Firefox Profiler UI)
samply record ./target/profiling/<binary> [args...]

# 4. Or save for CLI analysis
samply record --save-only -o profile.json ./target/profiling/<binary>
python3 ~/.agents/skills/rust-profiling/scripts/analyze_profile.py profile.json

Skill Files

File	Purpose
`reference.md`	Cargo.toml setup, samply options, troubleshooting
`examples.md`	Common profiling scenarios and analysis patterns
`optimization.md`	Post-profiling fixes: source patterns, release-profile tuning, PGO, BOLT, what doesn't work
`scripts/analyze_profile.py`	CLI tool to analyze saved profile.json files

When to Use

Performance is slower than expected
RSS or heap usage grows unexpectedly under load
Before optimizing (measure first!)
After optimization (verify improvement)
Investigating CPU-bound operations or allocation-heavy hot paths

When NOT to Use

The task is a Rust correctness bug, API design question, or refactor without a performance symptom.
The user already has a narrow benchmark request that does not need profiling guidance.
The issue is build time, binary size, or dependency hygiene rather than runtime CPU, memory, or contention.

What to Look For

Pattern	Meaning	Action
High self-time	Function itself is slow	Direct optimization target
High total-time	Called often or slow callees	Check call frequency
`malloc`/`alloc` in hot path	Allocation overhead	Preallocate, reuse, pool, or use bounded/shared buffers
RSS grows then plateaus while profiles show allocation churn	Possible allocator fragmentation, not necessarily a leak	Compare allocators, then reduce high-rate small allocations
`pthread_mutex`/`parking_lot`	Lock contention	Reduce lock scope or use lock-free

---name: rust-profilingdescription: Profile Rust code using samply and related evidence to identify CPU bottlenecks, allocation hot paths, and allocator-fragmentation symptoms. Use when performance is slow, RSS grows unexpectedly, before optimizing, or when the user asks to profile.--- # Rust Profiling with Samply Profile Rust binaries to find CPU bottlenecks and allocation pressure using [samply](https://github.com/mstange/samply), then use OS-level memory evidence when RSS or fragmentation is the symptom. ## Quick Start ```bash # 1. Ensure profiling profile exists in Cargo.toml (see reference.md) # 2. Build with debug symbols cargo build --profile profiling # 3. Profile (opens Firefox Profiler UI) samply record ./target/profiling/<binary> [args...] # 4. Or save for CLI analysis samply record --save-only -o profile.json ./target/profiling/<binary> python3 ~/.agents/skills/rust-profiling/scripts/analyze_profile.py profile.json ``` ## Skill Files | File | Purpose | |------|---------| | `reference.md` | Cargo.toml setup, samply options, troubleshooting | | `examples.md` | Common profiling scenarios and analysis patterns | | `optimization.md` | Post-profiling fixes: source patterns, release-profile tuning, PGO, BOLT, what doesn't work | | `scripts/analyze_profile.py` | CLI tool to analyze saved profile.json files | ## When to Use - Performance is slower than expected - RSS or heap usage grows unexpectedly under load - Before optimizing (measure first!) - After optimization (verify improvement) - Investigating CPU-bound operations or allocation-heavy hot paths ## When NOT to Use - The task is a Rust correctness bug, API design question, or refactor without a performance symptom. - The user already has a narrow benchmark request that does not need profiling guidance. - The issue is build time, binary size, or dependency hygiene rather than runtime CPU, memory, or contention. ## What to Look For | Pattern | Meaning | Action | |---------|---------|--------| | High self-time | Function itself is slow | Direct optimization target | | High total-time | Called often or slow callees | Check call frequency | | `malloc`/`alloc` in hot path | Allocation overhead | Preallocate, reuse, pool, or use bounded/shared buffers | | RSS grows then plateaus while profiles show allocation churn | Possible allocator fragmentation, not necessarily a leak | Compare allocators, then reduce high-rate small allocations | | `pthread_mutex`/`parking_lot` | Lock contention | Reduce lock scope or use lock-free |

Frequently asked questions