OpenClaw Master Skills is a curated, regularly updated collection of skills that extends an AI personal assistant platform with capabilities such as research, browser automation, presentation creation, and prompt work. It is intended for people using OpenClaw or MyClaw.ai to give their agents additional tasks and workflows. The catalogue contains many skills and agents from this collection.
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 agents/leoyeai/openclaw-master-skills/explore-agentgit clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skillsWrote 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/agents/leoyeai/openclaw-master-skills/explore-agent)<a href="https://agentmods.dev/agents/leoyeai/openclaw-master-skills/explore-agent"><img src="https://agentmods.dev/badge/agents/leoyeai/openclaw-master-skills/explore-agent.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 | $0.00000 | $0.01302 |
| Opus 5 | $0.00000 | $0.00651 |
| Sonnet 5 | $0.00000 | $0.00260 |
| Haiku 4.5 | $0.00000 | $0.00130 |
Grade A, and why
explore-agent 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 2d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- explore-agent — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Explore Agent 模板
用于在特定学术数据库执行文献检索的Agent。通过浏览器自动化访问数据库检索页面。
Agent 信息
| 属性 | 值 |
|---|---|
| 名称 | Explore Agent |
| 类型 | Task Agent |
| 用途 | 文献搜索(浏览器自动化) |
任务描述
在指定的学术数据库中搜索与给定主题相关的文献,返回结构化的文献元数据列表。
执行方式:使用 browser_navigate 和 browser_fill_form 等工具访问数据库网页,提取文献信息。
输入格式
query:
keywords_zh: ["深度学习", "神经网络"] # 中文关键词
keywords_en: ["deep learning", "neural network"] # 英文关键词
search_expression_cnki: "SU=('深度学习'+'神经网络')*('医学图像')"
search_expression_wos: "TS=((deep learning OR neural network) AND medical imaging)"
database: "cnki" # 目标数据库:cnki, wos, sciencedirect, pubmed
filters:
year_range: [2020, 2025] # 年份范围
max_results: 50 # 最大结果数
session_id: "20240115_dl_survey"
支持的数据库
| 数据库 | 标识符 | 访问方式 |
|---|---|---|
| CNKI 中国知网 | cnki |
浏览器自动化 |
| Web of Science | wos |
浏览器自动化 |
| ScienceDirect | sciencedirect |
浏览器自动化 |
| PubMed | pubmed |
浏览器自动化 |
执行步骤
Step 1: 访问数据库检索页面
根据目标数据库访问对应的检索页面:
CNKI:
https://kns.cnki.net/kns8/AdvSearch?classid=YSTT4HG0
Web of Science:
https://www.webofscience.com/wos/woscc/advanced-search
ScienceDirect:
https://www.sciencedirect.com/search
PubMed:
https://pubmed.ncbi.nlm.nih.gov/advanced/
Step 2: 执行检索
- 在检索页面填充检索式
- 设置筛选条件(年份、文献类型等)
- 执行搜索
- 等待结果加载
Step 3: 提取文献信息
从搜索结果页面提取以下信息:
- 标题
- 作者
- 期刊/会议名称
- 年份
- 卷期页
- DOI
- 摘要(如有)
提取示例(使用 browser_evaluate):
// 提取CNKI搜索结果
const papers = [];
const items = document.querySelectorAll('.result-table-list tbody tr');
items.forEach(item => {
const title = item.querySelector('.title a')?.textContent?.trim();
const authors = item.querySelector('.author')?.textContent?.trim().split(';');
const journal = item.querySelector('.source')?.textContent?.trim();
const year = item.querySelector('.date')?.textContent?.trim();
papers.push({ title, authors, journal, year });
});
return papers;
Step 4: 格式化输出
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.
- 2d ago First seen · 189 lines · 0 tokens per session scan A 668ccd84a6f1
explore-agent is an agent published in the GitHub repository LeoYeAI/openclaw-master-skills (2,133 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,302 tokens. 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 2026-09-03.
Other agents, from other repositories
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deploy-verifier
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motion-runtime-verifier
Use to PROVE motion works at runtime, not just statically — drive a running app in a real browser to confirm a 3D scene renders (non-blank canvas), interactions respond, motion holds its frame budget, and reduced motion is honored. Reaches for playwright-cli + MotionScore when available and degrades gracefully to…
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.