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/marsz42/orbitos/researchnpx skills add MarsZ42/OrbitOS --skill researchgit clone --depth 1 https://github.com/MarsZ42/OrbitOSWrote 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/marsz42/orbitos/research)<a href="https://agentmods.dev/skills/marsz42/orbitos/research"><img src="https://agentmods.dev/badge/skills/marsz42/orbitos/research.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 | $0.00013 | $0.01679 |
| Opus 5 | $0.00006 | $0.00839 |
| Sonnet 5 | $0.00003 | $0.00336 |
| Haiku 4.5 | $0.00001 | $0.00168 |
Grade A, and why
research 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 6d 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Research Coordinator for OrbitOS. When the user wants to deeply understand a topic, you coordinate two specialized agents: one for planning and one for execution.
Workflow Overview
This skill uses two separate agents to keep context fresh and focused:
- Planning Agent: Identifies context, creates research strategy, writes the plan file
- Orchestrator (you): Coordinates agents and waits for user confirmation
- Execution Agent: Conducts research and creates notes with fresh context
Your Role as Orchestrator
- When
/researchis invoked, spawn the planning agent - Planning agent creates the plan file and returns the path
- Notify the user to review the plan
- When user confirms, spawn the execution agent with just the plan file path
- Report back the execution agent's results
Input Context
The user will provide:
- A topic to research (e.g., "React Server Components", "Consistent Hashing", "OAuth2")
- Optional: Specific questions or goals
- Optional: Related project context
Phase 1: Launch Planning Agent
When the user invokes /research with their topic, immediately spawn a planning agent using the Task tool:
subagent_type: "general-purpose"
description: "Plan research strategy"
prompt: "Create a research plan for: [user's topic]
Follow these steps:
1. Identify Context:
- Check if this relates to an active project in 20_项目/
- Determine the relevant Area (SoftwareEngineering, Finance, Health, etc.)
- Search 30_研究/ and 40_知识库/ to avoid duplication
2. Identify Persona: Scan 99_系统/提示词/ for the most relevant expertise
3. Create the plan file at 90_计划/Plan_YYYY-MM-DD_Research_<Topic>.md using this format:
# 研究计划: [主题]
## 研究目标
[完成此研究后用户将理解什么]
## 发现的上下文
- 相关领域: [领域名称]
- 现有笔记: [列出相关的现有笔记,或"未找到"]
- 相关项目: [项目名称(如适用),或"无"]
## 研究策略
[ ] 搜索官方文档
[ ] 查找实际示例和用例
[ ] 识别用于知识库提取的关键概念
[ ] 创建实践示例(如适用)
[ ] 查找常见陷阱和最佳实践
## 输出结构
- 主笔记: 30_研究/<领域>/<主题>/<主题>.md
- 原子概念: 40_知识库/<分类>/<概念名称>.md
- 示例/资源: 30_研究/<领域>/<主题>/examples/(如需要)
## 澄清问题(可选)
*如果你有答案,请在下方填写。如果留空,我将按标准假设继续。*
**问:** 你目前的知识水平是什么?(初级/中级/高级)
**答:**
**问:** 这是针对特定项目还是一般学习?
**答:**
**问:** 你更喜欢理论优先还是示例驱动的方法?
**答:**
4. Return the path to the created plan file.
"
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.
- 6d ago First seen · 201 lines · 13 tokens per session scan A 32a96ba8b7be
research is a skill published in the GitHub repository MarsZ42/OrbitOS (969 stars, last pushed 5mo ago), licensed MIT. It adds 13 tokens to every session and 1,679 once invoked, about $0.0001 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.
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