org2-performance-guard

org2-performance-guard is a skill for Codex from org2AI/ORG2. It costs 112 tokens per session (2,991 once invoked), scanned A, original, AGPL-3.0.

A review skill for ORG2 code that checks how software uses computer resources and handles its lifecycle. It covers CPU, memory, disk and network work, timers, polling, subscriptions, workers, streaming, caches, and related data flows.

In plain words
What is it for?
Use it when adding or reviewing background work, timers, event listeners, realtime connections, pagination, streaming, caching, synchronization, or transcript and identity processing.
Why use it?
It helps catch changes that waste resources, keep running after they should stop, or appear successful even when work failed. This reduces performance problems and misleadingly successful operations.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it when adding or reviewing background work, timers, event listeners, realtime connections, pagination, streaming, caching, synchronization, or transcript and identity processing.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/org2ai/org2/org2-performance-guard
View source ↗ org2AI/ORG2
About the project

ORG-2 is a local-first application for running coding agents while recording, replaying, and reviewing how they build software. Teams use it to connect requests, agent decisions, actions, and resulting code across sessions and supported agent command-line tools. Its catalogue add-ons provide skills, rules, and instructions for this agent-building workflow.

org2AI/ORG2 · 2,630 stars · on GitHub

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.

Any agent
npx skills add org2AI/ORG2 --skill org2-performance-guard
Clone the repo
git clone --depth 1 https://github.com/org2AI/ORG2

Made for: Codex.

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 org2-performance-guard

README.md
[![agentmods](https://agentmods.dev/badge/skills/org2ai/org2/org2-performance-guard/github.svg)](https://agentmods.dev/skills/org2ai/org2/org2-performance-guard)
Your own site
<a href="https://agentmods.dev/skills/org2ai/org2/org2-performance-guard"><img src="https://agentmods.dev/badge/skills/org2ai/org2/org2-performance-guard/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for org2-performance-guard

Your own site · 80×15
<a href="https://agentmods.dev/skills/org2ai/org2/org2-performance-guard"><img src="https://agentmods.dev/badge/skills/org2ai/org2/org2-performance-guard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 112 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,991 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin unknown 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 5.1 $0.00112 $0.02991
Opus 5.5 $0.00045 $0.01196
Sonnet 5.5 $0.00022 $0.00598
Haiku 4.5 $0.00011 $0.00299

Measured 1mo ago against content hash 127f45a9b1f7, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

org2-performance-guard 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 1mo 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.

.orgii/skills/org2-performance-guard/SKILL.md · 211 lines

The source is not reproduced here

Licensed AGPL-3.0

The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.

Read it on GitHub

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 1mo ago First seen · 211 lines · 112 tokens per session scan A 127f45a9b1f7

Subscribe to this mod's changes

org2-performance-guard is a skill published in the GitHub repository org2AI/ORG2 (2,630 stars, last pushed 8d ago), licensed AGPL-3.0. It adds 112 tokens to every session and 2,991 once invoked, about $0.0004 per session on Opus 5.5. 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.

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