Agent Skills

Linux perf profiler skill for CPU performance analysis. Use when collecting sampling profiles with perf record, generating perf report, measuring hardware counters (cache misses, branch mispredicts, IPC), identifying hot functions, or feeding perf data into flamegraph tools. Activates on queries about perf, Linux performance counters, PMU events, off-CPU profiling, perf stat, perf annotate, or sampling-based profiling on Linux.

Install

npx skills add https://github.com/mohitmishra786/low-level-dev-skills --skill linux-perf
SKILL.md

Linux perf

Purpose

Guide agents through perf for CPU profiling: sampling, hardware counter measurement, hotspot identification, and integration with flamegraph generation.

Triggers

  • "Which function is consuming the most CPU?"
  • "How do I measure cache misses / IPC?"
  • "How do I use perf to find hotspots?"
  • "How do I generate a flamegraph from perf data?"
  • "perf shows [unknown] or [kernel] frames"

Workflow

1. Prerequisites

# Install
sudo apt install linux-perf    # Debian/Ubuntu (version-matched)
sudo dnf install perf          # Fedora/RHEL

# Check permissions
# By default perf requires root or paranoid level ≤ 1
cat /proc/sys/kernel/perf_event_paranoid
# 2 = only CPU stats (not kernel), 1 = user+kernel, 0 = all, -1 = no restrictions

# Temporarily lower (session only)
sudo sysctl -w kernel.perf_event_paranoid=1

# Persistent
echo 'kernel.perf_event_paranoid=1' | sudo tee /etc/sysctl.d/99-perf.conf
sudo sysctl -p /etc/sysctl.d/99-perf.conf

Compile the target with debug symbols for useful frame data:

gcc -g -O2 -fno-omit-frame-pointer -o prog main.c
# -fno-omit-frame-pointer: essential for frame-pointer-based unwinding
# Alternative: compile with DWARF CFI and use --call-graph=dwarf

2. perf stat — quick counters

# Basic hardware counters
perf stat ./prog

# With specific events
perf stat -e cache-misses,cache-references,instructions,cycles,branch-misses ./prog

# Wall-clock comparison: N runs
perf stat -r 5 ./prog

# Attach to existing process
perf stat -p 12345 sleep 10

Interpret perf stat output:

  • IPC (instructions per cycle) < 1.0: memory-bound or stalled pipeline
  • cache-miss rate > 5%: significant cache pressure
  • branch-miss rate > 5%: branch predictor struggling

3. perf record — sampling

# Default: sample at 1000 Hz (cycles event)
perf record -g ./prog

# Specify frequency
perf record -F 999 -g ./prog

# Specific event
perf record -e cache-misses -g ./prog

# Attach to running process
perf record -F 999 -g -p 12345 sleep 30

# Off-CPU profiling (time spent waiting)
perf record -e sched:sched_switch -ag sleep 10

# DWARF call graphs (better for binaries without frame pointers)
perf record -F 999 --call-graph=dwarf ./prog

# Save to named file
perf record -o myapp.perf.data -g ./prog

4. perf report — interactive analysis

perf report                          # reads perf.data
perf report -i myapp.perf.data
perf report --no-children            # self time only (not cumulative)
perf report --sort comm,dso,sym      # sort by fields
perf report --stdio                  # non-interactive text output

Navigation in TUI:

  • Enter — expand a symbol
  • a — annotate (show assembly with hit counts)
  • s — show source (needs debug info)
  • d — filter by DSO (library)
  • t — filter by thread
  • ? — help

5. perf annotate — hot instructions

# Show assembly with hit percentages
perf annotate sym_name

# From report: press 'a' on a symbol
# Or directly:
perf annotate -i perf.data --symbol=hot_function --stdio

High hit count on a mov or vmovdqa suggests a cache miss at that load.

6. perf top — live profiling

# Live top, like 'top' but for functions
sudo perf top -g

# Filter by process
sudo perf top -p 12345

7. Feed into flamegraphs

# Generate perf script output
perf script > out.perf

# Use Brendan Gregg's FlameGraph tools
git clone https://github.com/brendangregg/FlameGraph
./FlameGraph/stackcollapse-perf.pl out.perf > out.folded
./FlameGraph/flamegraph.pl out.folded > flamegraph.svg

# Open flamegraph.svg in browser

See skills/profilers/flamegraphs for reading flamegraphs and interpreting results.

8. Common issues

Problem Cause Fix
Permission denied perf_event_paranoid too high Lower paranoid level or run with sudo
[unknown] frames Missing frame pointers or debug info Recompile with -fno-omit-frame-pointer or use --call-graph=dwarf
[kernel] everywhere Kernel symbols not visible Use sudo perf record; install linux-image-$(uname -r)-dbgsym
No kallsyms Kernel symbols unavailable `echo 0
Empty report for short program Program exits too fast Use -F 9999 or instrument longer workload
DWARF unwinding slow Large DWARF stack Limit with --call-graph dwarf,512

9. Useful events

# List all available events
perf list

# Common hardware events
cycles
instructions
cache-references
cache-misses
branch-instructions
branch-misses
stalled-cycles-frontend
stalled-cycles-backend

# Software events
context-switches
cpu-migrations
page-faults

# Tracepoints (requires root)
sched:sched_switch
syscalls:sys_enter_read

For a counter reference and interpretation guide, see references/events.md.

Related skills

  • Use skills/profilers/flamegraphs for SVG flamegraph generation and reading
  • Use skills/profilers/valgrind for cache simulation and memory profiling
  • Use skills/compilers/gcc or skills/compilers/clang for PGO from perf data (AutoFDO)

Related skills

azure-diagnosticsmicrosoft608KDebug Azure production issues on Azure using AppLens, Azure Monitor, resource health, and safe triage. WHEN: debug production issues, troubleshoot app service, app service high CPU, app service deployment failure, troubleshoot container apps, troubleshoot functions, troubleshoot AKS, VM RDP, Linux SSH, VM black screen, can't connect to VM, reset VM password, NSG or firewall blocking, kubectl cannot connect, kube-system/CoreDNS failures, pod pending, crashloop, node not ready, upgrade failures, aazure-preparemicrosoft608KPrepare azd-based Azure projects for deployment: generates azure.yaml, infrastructure (Bicep/Terraform), and Dockerfiles for the Azure Developer CLI (azd) workflow. USE ONLY when the user explicitly wants to use azd as the deployment tool, or the project already has an azure.yaml file. DO NOT USE FOR: non-azd deployments, Python App Service code-only deploys (use python-appservice-deploy), or cross-cloud migration (use azure-cloud-migrate). WHEN: prepare app for azd, create azure.yaml, set up azazure-aimicrosoft608KUse for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.azure-deploymicrosoft607KExecute Azure deployments for ALREADY-PREPARED applications that have existing .azure/deployment-plan.md and infrastructure files. DO NOT use this skill when the user asks to CREATE a new application — use azure-prepare instead. This skill runs azd up, azd deploy, terraform apply, and az deployment commands with built-in error recovery. Requires .azure/deployment-plan.md from azure-prepare and validated status from azure-validate. WHEN: \"run azd up\", \"run azd deploy\", \"execute deployment\",

Search skills and MCP servers

Fuzzy search across 23,137 skills and servers