Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/chaterm/terminal-skillsnpx agentmods add skills/chaterm/terminal-skills/profilingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/chaterm/terminal-skills/profiling)<a href="https://agentmods.dev/skills/chaterm/terminal-skills/profiling"><img src="https://agentmods.dev/badge/skills/chaterm/terminal-skills/profiling.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00005 | $0.01298 |
| Opus 5 | $0.00003 | $0.00649 |
| Sonnet 5 | $0.00001 | $0.00260 |
| Haiku 4.5 | $0.00001 | $0.00130 |
Grade A, and why
profiling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
性能分析
概述
CPU/内存分析、火焰图、追踪技能。
perf 工具
基础命令
# 安装
apt install linux-tools-common linux-tools-$(uname -r)
# CPU 采样
perf record -g -p PID
perf record -g -a -- sleep 30
# 查看报告
perf report
perf report --stdio
# 实时统计
perf top
perf top -p PID
# 统计事件
perf stat command
perf stat -p PID sleep 10
常用事件
# CPU 周期
perf record -e cycles -p PID
# 缓存未命中
perf record -e cache-misses -p PID
# 上下文切换
perf record -e context-switches -p PID
# 列出可用事件
perf list
火焰图
# 采集数据
perf record -F 99 -g -p PID -- sleep 30
# 生成火焰图
perf script | stackcollapse-perf.pl | flamegraph.pl > flamegraph.svg
# 或使用 FlameGraph 工具
git clone https://github.com/brendangregg/FlameGraph
perf script | ./FlameGraph/stackcollapse-perf.pl | ./FlameGraph/flamegraph.pl > out.svg
strace 追踪
基础用法
# 追踪进程
strace -p PID
# 追踪命令
strace command
# 统计系统调用
strace -c command
strace -c -p PID
# 追踪特定调用
strace -e open,read,write command
strace -e trace=network command
strace -e trace=file command
高级选项
# 显示时间戳
strace -t command
strace -tt command # 微秒
# 显示耗时
strace -T command
# 跟踪子进程
strace -f command
# 输出到文件
strace -o trace.log command
ltrace 库调用
# 追踪库调用
ltrace command
ltrace -p PID
# 统计
ltrace -c command
# 特定库
ltrace -l libc.so.6 command
内存分析
valgrind
# 内存泄漏检测
valgrind --leak-check=full ./program
# 内存错误
valgrind --tool=memcheck ./program
# 缓存分析
valgrind --tool=cachegrind ./program
# 调用图
valgrind --tool=callgrind ./program
kcachegrind callgrind.out.*
pmap
# 查看进程内存映射
pmap PID
pmap -x PID
# 详细信息
pmap -XX PID
smem
# 内存使用统计
smem
smem -u # 按用户
smem -p # 按进程
smem -k # 人类可读
系统分析
vmstat
# 每秒刷新
vmstat 1
# 输出说明
# r: 运行队列
# b: 阻塞进程
# si/so: 交换
# bi/bo: 块 IO
# us/sy/id/wa: CPU 使用
iostat
# 磁盘统计
iostat -x 1
# 输出说明
# %util: 设备利用率
# await: 平均等待时间
# r/s, w/s: 读写 IOPS
pidstat
# CPU 使用
pidstat -u 1
# 内存使用
pidstat -r 1
# IO 使用
pidstat -d 1
# 特定进程
pidstat -p PID 1
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 255 lines · 5 tokens per session scan A ce558186b49e
profiling is a skill published in the GitHub repository chaterm/terminal-skills (58 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 5 tokens to every session and 1,298 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
performance-optimizer
Systematic performance profiling and optimization for Python and web backends — measure first, fix second, verify the fix.
performance-analysis
Performance analysis, bottleneck detection, and optimization recommendations. Use when profiling slow code or systems, hunting a performance regression, or producing an optimization plan with measurable targets.
performance-optimizer
Profile, diagnose, and fix performance bottlenecks in applications. Use when optimizing slow queries, reducing load times, improving runtime performance, or reducing memory usage.
k8s-performance
A Kubernetes diagnostic and repair method for services using too much CPU, memory, or disk input/output, or responding slowly. Kubernetes is software for running and managing containers.
performance-optimizer
A performance improvement assistant that analyses applications, identifies bottlenecks, and suggests ways to make them faster.
profiling
Diagnose frame-rate bottlenecks (CPU vs GPU bound FIRST), control Unreal Insights traces, sample live frame times, and annotate performance captures. Use when FPS is low/bad, the game is slow, or you need to find what is limiting the frame rate.