pyperformance-suite-run

pyperformance-suite-run is a skill for Claude Code, Codex from sisibeloved/cpython-optimize-skill. It costs 42 tokens per session (1,414 once invoked), scanned A, original, MIT.

A procedure for formally running a selected group or the full pyperformance suite. It produces JSON files containing measured Python performance results.

In plain words
What is it for?
Use it for official subset or full performance validation, baseline-and-candidate runs, and creating results for later comparison or reporting.
Why use it?
It keeps benchmark runs consistent by documenting CPU assignment, warmup, environment settings, and the exact baseline or candidate being tested.

Skill for Claude CodeCodex

Part of the cpython-optimize-skill plugin — 35 skills, 9 agents, 2 hooks, 1 MCP server shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/sisibeloved/cpython-optimize-skill/pyperformance-suite-run
Any agent
npx skills add sisibeloved/cpython-optimize-skill --skill pyperformance-suite-run
Clone the repo
git clone --depth 1 https://github.com/sisibeloved/cpython-optimize-skill

Made for: Claude Code, Codex.

Or install cpython-optimize-skill, the plugin that ships this one along with the rest of its 35 skills, 9 agents, 2 hooks, 1 MCP server.

Wrote 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.

agentmods badge for pyperformance-suite-run

README.md
[![agentmods](https://agentmods.dev/badge/skills/sisibeloved/cpython-optimize-skill/pyperformance-suite-run.svg)](https://agentmods.dev/skills/sisibeloved/cpython-optimize-skill/pyperformance-suite-run)
Your own site
<a href="https://agentmods.dev/skills/sisibeloved/cpython-optimize-skill/pyperformance-suite-run"><img src="https://agentmods.dev/badge/skills/sisibeloved/cpython-optimize-skill/pyperformance-suite-run.svg" alt="Measured on agentmods" height="20"></a>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,414 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00042 $0.01414
Opus 5 $0.00021 $0.00707
Sonnet 5 $0.00008 $0.00283
Haiku 4.5 $0.00004 $0.00141

Measured 3d ago against content hash 09aa28f65d16, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

pyperformance-suite-run 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 3d 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.

plugins/cpython-optimize-skill/skills/pyperformance-suite-run/SKILL.md · 77 lines

How it starts

The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.

pyperformance Suite Run

负责正式 pyperformance 运行。单 benchmark 调试先用 pyperformance-worker-run

三类测试边界

  • RuntimeTests 是功能测试。
  • test_cinderx/lib test 是集成测试。
  • pyperformance 是性能测试;正式性能测试前必须说明功能测试和集成测试是否已通过、跳过或仍待验证。

使用场景

  • L3 相关子集
  • L4 full 全量性能验证
  • baseline/candidate 正式对照
  • 生成 run.json

规则

  • python -m pyperformance run
  • 运行前必须读取 ../using-cpython-optimize/references/pyperformance-env-contract.md,先列出 driver env、--inherit-environ、worker env 和 baseline/candidate 差异轴。
  • 使用 --affinity 前必须读取 ../using-cpython-optimize/references/pyperformance-affinity-guidance.md,说明它是 CPU 绑核参数;先检查当前可用 CPU,再把用户真实命令中的 affinity 映射到当前环境,不要逐字照抄不可用核号。
  • CinderX JIT 口径必须证明真实 worker 启用 JIT:检查 CinderX .pth、worker pyvenv.cfg / include-system-site-packages 或等价 PYTHONPATH、worker 内 import cinderx / _cinderxcinderx.__file__cinderx.get_import_error()cinderx.is_initialized()
  • 正式非 debug 命令形态必须包含 --affinity--warmup-b <benchmark-selector>(subset 时)、-o <result.json>--inherit-environ
  • --affinity 必须落在当前 nproc / lscpu / taskset -pc $$ / 容器 cpuset 显示的可用 CPU 内;高核号不可用时,重分配可用 CPU 并记录原始 affinity -> 实际 affinity。
  • --inherit-environ 至少覆盖代理、LD_LIBRARY_PATHPYTHONPATH、插件开关和 JIT 关键变量。
  • validation-skill-router deny 了 pyperformance 命令,不要把它当测试失败;先补齐 pyperformance-env-contract.md 要求的 worker venv / .pth / --inherit-environ / JIT 初始化证据,再用 CPYTHON_OPTIMIZE_HOOK_ACK=1 前缀重试同一条正式命令。
  • 不能在未完成前置证据时提前加 CPYTHON_OPTIMIZE_HOOK_ACK=1 绕过 hook;ACK 只表示已经完成环境契约检查。
  • 记录 warmup、loops、CPU affinity、容器线、Python、CinderX commit。
  • 正式数据关闭 HIR/JIT dump、--debug-single-value 和临时诊断变量;这些只用于 L2 调试,不进入正式性能结论。
  • subset/full 选择必须来自 validation-strategy 的晋级理由。
  • 文档和报告只写 <benchmark-selector><result.json><baseline.json><candidate.json> 等占位,不硬编码具体 pyperformance 用例名或文件名。

命令形态

目标集合正式性能测试:

<env-vars> <python> -m pyperformance run \
  --affinity=<cpu-list-or-set> \
  --warmup <n> \
  -b <benchmark-selector> \
  --inherit-environ <comma-separated-env-list> \
  -o <result.json>

Read the full file on GitHub · 77 lines

Changes

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.

  1. 3d ago First seen · 77 lines · 42 tokens per session scan A 09aa28f65d16

Subscribe to this mod's changes

pyperformance-suite-run is a skill published in the GitHub repository sisibeloved/cpython-optimize-skill (2 stars, last pushed 5d ago), licensed MIT. It adds 42 tokens to every session and 1,414 once invoked, about $0.0002 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 2026-08-31.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

obra/superpowers · 37 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens

chat-perf

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

microsoft/vscode · 51 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens