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.
npx agentmods add skills/orkait/hyperstack/optimizernpx skills add orkait/hyperstack --skill optimizergit clone --depth 1 https://github.com/orkait/hyperstackWhat 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 | $0.00116 | $0.01733 |
| Opus 5 | $0.00058 | $0.00866 |
| Sonnet 5 | $0.00023 | $0.00347 |
| Haiku 4.5 | $0.00012 | $0.00173 |
Grade A, and why
optimizer 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 today.
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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- today First seen · 98 lines · 116 tokens per session scan A ad11d0c80936
optimizer is a skill published in the GitHub repository orkait/hyperstack (1 stars, last pushed 1mo ago), with no licence file. It adds 116 tokens to every session and 1,733 once invoked, about $0.0006 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.
Other skills, from other repositories
feedback-learning
Classify and record explicit corrective feedback without turning skills or instructions into append-only knowledge dumps.
blog-post
Writes and structures long-form blog posts, creates tutorial outlines, and optimizes content for SEO with cover image generation. Use when the user asks to write a blog post, article, how-to guide, tutorial, technical writeup, thought leadership piece, or long-form content.
remember
Review the current conversation and capture valuable knowledge — best practices, coding conventions, architecture decisions, workflows, and user feedback — into persistent memory (AGENTS.md) or reusable skills. Use when the user says: (1) remember this, (2) save what we learned, (3) update memory, (4) capture…
customizations-in-the-agent-host
Architecture and hard-won debugging lessons for customization enablement (plugins, MCP servers, agents, skills, instructions) in the agent host. Use when changing how customizations are discovered, published, enabled/disabled, or handed to a provider SDK; when a customization shows the wrong enabled state in the UI…
dg-piagent
Use when working with pi-agent / @earendil-works/pi-coding-agent SDK — 创建 agent、自定义工具、 编写扩展、修改系统提示词、管理会话、配置模型、处理认证、加载 skills/prompts/context files、 实现完全控制模式、或任何需要调用 pi-agent SDK API 的任务时使用。 也用于企业内网接口接入评估：用户给出企业/内网 LLM 接口文档或地址，问「能不能接」「怎么配」时， 按接口核对清单给出接入建议 + models.json 配置初稿。…
self-improve-with-tinyfish
Enables Hermes to create new reusable skills for itself by researching live web sources with TinyFish Search and Fetch, analyzing source coverage, writing SKILL.md files, and installing them into Hermes memory. Use when the user asks Hermes to learn, teach itself, upgrade itself, or save a reusable capability.