MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
调用Strategy Engine MCP服务器执行量化策略。当用户需要运行因子表达式策略、回测交易策略或执行金融分析时调用此技能。基于MCP Server工具的实际默认值设置。
$ npx skills add LeoYeAI/openclaw-master-skills --skill strategy-engine -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills strategy-engine --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/strategy-engine .claude/skills/strategy-engine && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "strategy-engine" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/strategy-engine into .claude/skills/strategy-engine/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "strategy-engine", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/strategy-engineType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add LeoYeAI/openclaw-master-skills --skill strategy-engine -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills strategy-engine --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/strategy-engine .agents/skills/strategy-engine && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "strategy-engine" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/strategy-engine into .agents/skills/strategy-engine/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "strategy-engine", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill strategy-engine -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills strategy-engine --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/strategy-engine .cursor/skills/strategy-engine && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "strategy-engine" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/strategy-engine into .cursor/skills/strategy-engine/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "strategy-engine", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/LeoYeAI/openclaw-master-skills.git --path skills/strategy-engine--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add LeoYeAI/openclaw-master-skills --skill strategy-engine -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills strategy-engine --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/strategy-engine .gemini/skills/strategy-engine && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "strategy-engine" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/strategy-engine into .gemini/skills/strategy-engine/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "strategy-engine", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install LeoYeAI/openclaw-master-skills strategy-engineInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add LeoYeAI/openclaw-master-skills --skill strategy-engine -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/strategy-engine .github/skills/strategy-engine && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "strategy-engine" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/strategy-engine into .github/skills/strategy-engine/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "strategy-engine", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add LeoYeAI/openclaw-master-skills --skill strategy-engine -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install LeoYeAI/openclaw-master-skills strategy-engine --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/strategy-engine .opencode/skills/strategy-engine && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "strategy-engine" agent skill from https://github.com/LeoYeAI/openclaw-master-skills/tree/main/skills/strategy-engine into .opencode/skills/strategy-engine/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "strategy-engine", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
strategy-engine调用Strategy Engine MCP服务器执行量化策略。当用户需要运行因子表达式策略、回测交易策略或执行金融分析时调用此技能。基于MCP Server工具的实际默认值设置。
Strategy Engine is an agent skill from LeoYeAI/openclaw-master-skills. 调用Strategy Engine MCP服务器执行量化策略。当用户需要运行因子表达式策略、回测交易策略或执行金融分析时调用此技能。基于MCP Server工具的实际默认值设置。
Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `_meta.json`).
It sits in Agent Workflows, covering MCP servers. It works with Model Context Protocol. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.
7 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are python and markdown).
From the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
visual.hzyotoy.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Strategy Engine loads about 2.7k tokens when it runs. Until then it costs about 26 tokens; SKILL.md has 311 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 311 words, ~2,698 tokens.
.claude/skills/strategy-engine/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.当用户提供策略条件时,基于MCP Server工具的实际默认值自动组合参数:
# 自动组合参数示例(基于MCP Server工具实际默认值)
mcp_engine_mcp_server_run_expression_selected(
input={
"startDate": "2024-01-17", # 开始日期,DateTime类型
"endDate": "2024-04-17", # 结束日期,DateTime类型
"period": "5m", # 基础周期(默认:5m)
"codes": "", # 合约代码列表(新增字段,默认:空)
"poolId": 10, # 期货加权品种池(默认:10)
"openCondition": "用户提供的开仓条件",
"closeCondition": "用户提供的平仓条件",
"stopCondition": "用户提供的止损条件",
"initCash": 10000000, # 初始资金(默认:10000000)
"direction": 1, # 多头方向(默认:1)
"commssionFee": 0, # 手续费%(默认:0,不需要手续费)
"slippage": 0, # 跳数或跳点值(默认:0,按最小变动价格计算)
"runId": 123456789 # 运行ID(默认:随机生成一串长整型数字)
}
)| 参数 | 类型 | 说明 | 实际默认值 | 示例 |
|---|---|---|---|---|
input | ExpressionSelectedV2Input | 输入参数对象 | - | {...} |
| 属性 | 类型 | 说明 | 实际默认值 | 示例 |
|---|---|---|---|---|
startDate | DateTime | 开始日期 | 当前日期-3个月 | "2024-01-17" |
endDate | DateTime | 结束日期 | 当前日期 | "2024-04-17" |
period | string | 基础周期 | "5m"(5分钟) | "1d", "60m" |
codes | string | 合约代码列表(新增) | 空字符串 | "IF2404,IC2404" |
poolId | int | 品种池ID | 10(期货加权) | 4(股票池) |
openCondition | string | 开仓条件 | 用户提供 | "_ma_5m_30_trend == 1" |
closeCondition | string | 平仓条件 | 用户提供 | "_ma_5m_30_trend == -1" |
stopCondition | string | 止损条件 | 用户提供 | "_palp > 10" |
initCash | float | 初始资金 | 10000000 | 500000 |
direction | int | 交易方向 | 1(多头) | 0(空头) |
commssionFee | float | 手续费% | 0(不需要手续费) | -1(按系统设置手续费参与计算) |
slippage | float | 跳数或跳点值 | 0(按最小变动价格计算) | 1(1个跳点) |
runId | long | 运行ID | 随机生成一串长整型数字 | 123456789 |
| 值 | 说明 | 适用场景 |
|---|---|---|
0 | 不需要手续费 | 默认值,测试策略时使用 |
-1 | 按系统设置手续费参与计算 | 使用系统配置的手续费 |
>0 | 按设置的手续费计算 | 自定义手续费率 |
| 值 | 说明 | 适用场景 |
|---|---|---|
0 | 无滑点 | 默认值,理想交易环境 |
>0 | 按设置的跳点值计算 | 模拟真实交易环境 |
| 值 | 说明 | 适用场景 |
|---|---|---|
| 空字符串 | 使用品种池进行回测 | 默认值,使用poolId指定的品种池 |
| 具体合约代码 | 指定具体合约进行回测 | 如"IF2404,IC2404",多个合约以逗号分隔 |
| 情况 | codes值 | poolId值 | 说明 |
|---|---|---|---|
| 使用品种池 | 空 | >0 | 默认情况,使用poolId指定的品种池 |
| 使用具体合约 | 非空 | 0 | 系统自动将poolId置为0,使用指定合约 |
| 无效配置 | 空 | 0 | 错误:必须提供合约代码或有效品种池 |
| 值 | 说明 | 适用场景 |
|---|---|---|
| 随机长整型数字 | 每次运行可随机生成一串长整型数字 | 用于标识每次策略运行的唯一ID |
| 周期 | 说明 | 适用策略 | 默认值 |
|---|---|---|---|
"1m" | 1分钟 | 高频交易 | - |
"5m" | 5分钟 | 短线交易 | ✅ |
"15m" | 15分钟 | 中短线策略 | - |
"30m" | 30分钟 | 中短线策略 | - |
"60m" | 1小时或60分钟 | 短期趋势 | - |
"1d" | 1日或日线或天 | 中长线策略 | - |
"1w" | 1周或周 | 长期投资 | - |
"1mon" | 1月或月 | 长期投资 | - |
| poolId | 说明 | 适用市场 | 默认值 |
|---|---|---|---|
10 | 期货加权品种池 | 期货市场 | ✅ |
4 | 股票品种池 | 股票市场 | - |
6 | ETF品种池 | ETF市场 | - |
# 重要概念区分:
# 1. 基础周期(period):K线数据的周期(5m、30m、1d等)
# 2. 均线周期:技术指标的计算周期(30、60、120等)
# 规则1:用户明确指定基础周期时,使用用户指定的周期
if "基础周期" in user_input or "K线周期" in user_input or "数据周期" in user_input:
if "30分钟" in user_input:
period = "30m"
elif "60分钟" in user_input or "1小时" in user_input:
period = "60m"
elif "5分钟" in user_input:
period = "5m"
elif "15分钟" in user_input:
period = "15m"
elif "日" in user_input or "天" in user_input:
period = "1d"
elif "周" in user_input:
period = "1w"
elif "月" in user_input:
period = "1mon"
else:
# 规则2:用户未明确指定基础周期时,使用默认周期5m
# 注意:用户提到"30分钟均线"指的是均线周期,不是基础周期!
period = "5m" # 默认值# 识别用户提到的均线周期(用于构建FactorLang表达式)
if "30分钟均线" in user_input:
# 用户提到的是均线周期30,基础周期仍然是5m
ma_period = "5m" # 基础周期
ma_length = "30" # 均线长度
elif "60分钟均线" in user_input:
ma_period = "5m" # 基础周期
ma_length = "60" # 均线长度
elif "120分钟均线" in user_input:
ma_period = "5m" # 基础周期
ma_length = "120" # 均线长度
elif "240分钟均线" in user_input:
ma_period = "5m" # 基础周期
ma_length = "240" # 均线长度
else:
# 默认均线周期
ma_period = "5m"
ma_length = "30"# 重要:所有时间范围都基于当前日期动态计算
# 当前日期:2026年3月25日(根据环境信息)
# 规则1:用户明确指定时间范围时,使用用户指定的范围
if "近5年" in user_input:
startDate = "2021-03-25" # 当前日期-5年
endDate = "2026-03-25" # 当前日期
elif "近3年" in user_input:
startDate = "2023-03-25" # 当前日期-3年
endDate = "2026-03-25" # 当前日期
elif "近1年" in user_input:
startDate = "2025-03-25" # 当前日期-1年
endDate = "2026-03-25" # 当前日期
else:
# 规则2:用户未指定时间范围时,使用默认近3个月
startDate = "2025-12-25" # 当前日期-3个月
endDate = "2026-03-25" # 当前日期# 规则1:用户使用中文描述时,自动转换为FactorLang变量
if "盈亏点" in stop_condition or "点数" in stop_condition:
stop_condition = stop_condition.replace("盈亏点", "_palp")
elif "盈亏%" in stop_condition or "百分比" in stop_condition:
stop_condition = stop_condition.replace("盈亏%", "_palr")
# 规则2:确保使用正确的变量
if "_profit_loss_percent" in stop_condition:
stop_condition = stop_condition.replace("_profit_loss_percent", "_palr")# 用户输入:开仓条件,30分钟均线120朝上且日级别是金叉状态
# AI推断:用户提到的是均线周期30,不是基础周期,使用默认5m基础周期
mcp_engine_mcp_server_run_expression_selected(
input={
"startDate": "2024-01-17", # 近3个月(默认)
"endDate": "2024-04-17", # 当前日期(默认)
"period": "5m", # 5分钟基础周期(默认)
"poolId": 10, # 期货加权池(默认)
"openCondition": "_ma_5m_120_trend == 1 && _dkx_1d_cross_status == 1",
"closeCondition": "_ma_5m_120_trend == -1 && _dkx_1d_cross_status == -1",
"stopCondition": "_palp > 10", # 盈亏点数大于10
"initCash": 10000000, # 1000万初始资金(默认)
"direction": 1, # 多头方向(默认)
"commssionFee": -1, # 手续费%(默认:按系统设置)
"slippage": 0, # 滑点(默认:无滑点)
"runId": 123456789 # 运行ID(默认:随机生成)
}
)# 用户输入:开仓条件,基础周期30分钟,均线120朝上
# AI推断:用户明确指定基础周期30m
mcp_engine_mcp_server_run_expression_selected(
input={
"startDate": "2024-01-17",
"endDate": "2024-04-17",
"period": "30m", # 用户指定基础周期30m
"poolId": 10,
"openCondition": "_ma_30m_120_trend == 1 && _dkx_1d_cross_status == 1",
"closeCondition": "_ma_30m_120_trend == -1 && _dkx_1d_cross_status == -1",
"stopCondition": "_palp > 10",
"initCash": 10000000,
"direction": 1,
"commssionFee": -1, # 手续费%(默认:按系统设置)
"slippage": 0, # 滑点(默认:无滑点)
"runId": 123456789 # 运行ID(默认:随机生成)
}
)# 用户输入:开仓条件,均线朝上,手续费0.1%,滑点1个跳点
# AI推断:用户指定手续费和滑点参数
mcp_engine_mcp_server_run_expression_selected(
input={
"startDate": "2024-01-17",
"endDate": "2024-04-17",
"period": "5m", # 5分钟基础周期(默认)
"poolId": 10,
"openCondition": "_ma_5m_30_trend == 1",
"closeCondition": "_ma_5m_30_trend == -1",
"stopCondition": "_palp > 10",
"initCash": 10000000,
"direction": 1,
"commssionFee": 0.1, # 用户指定手续费0.1%
"slippage": 1, # 用户指定滑点1个跳点
"runId": 123456789 # 运行ID(默认:随机生成)
}
)# 用户输入:开仓条件,均线朝上,指定IF2404和IC2404合约
# AI推断:用户指定具体合约代码,系统自动将poolId置为0
mcp_engine_mcp_server_run_expression_selected(
input={
"startDate": "2024-01-17",
"endDate": "2024-04-17",
"period": "5m", # 5分钟基础周期(默认)
"codes": "IF2404,IC2404", # 用户指定具体合约代码
"poolId": 10, # 系统会自动置为0,使用指定合约
"openCondition": "_ma_5m_30_trend == 1",
"closeCondition": "_ma_5m_30_trend == -1",
"stopCondition": "_palp > 10",
"initCash": 10000000,
"direction": 1,
"commssionFee": 0, # 不需要手续费(默认)
"slippage": 0, # 无滑点(默认)
"runId": 123456789 # 运行ID(默认:随机生成)
}
)"5m""5m""5m""近3个月"input对象包含所有参数DateTime类型codes、commssionFee、slippage和runId参数codes字段poolId字段poolId置为0"5m"input对象结构commssionFee和slippage参数runId用于标识每次运行数据来源:基于MCP Server工具类的实际默认值设置 调用时机:当用户需要运行因子表达式策略、回测交易策略或执行金融分析时自动调用此技能。
版本:v3.0(同步MCP工具最新参数结构,新增codes字段,支持具体合约代码回测)
"timeConsuming": 310 表示运行耗时310毫秒(0.31秒)| 耗时范围 | 说明 | 性能评估 |
|---|---|---|
| < 100ms | 极快 | 优秀 |
| 100-500ms | 快速 | 良好 |
| 500-1000ms | 正常 | 一般 |
| > 1000ms | 较慢 | 需要优化 |
策略运行完成后,系统会生成一个唯一的查看链接:
https://visual.hzyotoy.com/?data_dir=xzr&data_id=123456789&initCash=10000000点击链接或复制URL到浏览器中查看完整策略分析报告
| 参数 | 说明 | 示例 |
|---|---|---|
data_dir | 数据目录 | xzr |
data_id | 运行ID | 123456789 |
initCash | 初始资金 | 10000000 |
AI执行要求:必须严格遵守本SKILL中的参数结构匹配规则、类型要求和结果查看规范!
© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 1 other file in skills/strategy-engine of LeoYeAI/openclaw-master-skills.
Open the folder on GitHubat commit e5199b5
Strategy Engine next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Strategy Engine this skillLeoYeAI/openclaw-master-skills | 2.2k | — | ~2.7k | Automated safety check: Pass | MIT | |
| MCP Server Builderanthropics/skills | 180k | 62 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| MCP Server BuildershareAI-lab/learn-claude-code | 78k | 5 repos | ~1.2k | Automated safety check: Pass | MIT | |
| MCP Integration for Pluginsanthropics/claude-plugins-official | 37k | 11 repos | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Fastmcp Client CLIPrefectHQ/fastmcp | 28k | 1 repos | ~823 | Automated safety check: Pass | Apache-2.0 | |
| Crush Configurationcharmbracelet/crush | 29k | — | ~3.7k | Automated safety check: Pass | Custom licence |
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
shareAI-lab/learn-claude-code
Walks through building MCP servers in Python or TypeScript that expose tools, resources and prompts to Claude, with templates, registration and testing.
anthropics/claude-plugins-official
Explains how to bundle Model Context Protocol servers in a Claude Code plugin, covering config files, stdio, SSE, HTTP and WebSocket server types, and authentication.
PrefectHQ/fastmcp
Query and invoke tools on MCP servers using fastmcp list and fastmcp call.
charmbracelet/crush
Explains how to configure the Crush coding agent with crushrc or crush.json, covering providers, models, LSPs, MCP servers, hooks, permissions and config precedence.
mksglu/context-mode
Routes large command, file, API and browser output through context-mode tools so only the needed result enters the agent's context, instead of dumping it via Bash.
LeoYeAI/openclaw-master-skills
Manages pipelines on a DevOps quality and efficiency platform through its OpenAPI: list workspaces and templates, create, update, run and cancel pipelines, and read run records.
LeoYeAI/openclaw-master-skills
Patches OpenClaw's Feishu extension so an edited document triggers an isolated agent session that reads the doc and replies inline, turning it into a live chat space.
LeoYeAI/openclaw-master-skills
Multi-context memory management system for OpenClaw agents with group-isolated storage, global shared memory, workspace organization, and group-specific skills isolation.
LeoYeAI/openclaw-master-skills
Runs a brand's AI-search visibility work end to end: diagnosing how AI platforms represent it, repositioning it, producing AI-optimized content and monitoring ongoing mentions.
LeoYeAI/openclaw-master-skills
Installs and authenticates the gws CLI, then automates Gmail, Drive, Sheets, Calendar, Docs, Chat and Tasks with ready-made recipes, persona bundles and security audits.
LeoYeAI/openclaw-master-skills
Runs four advisor roles, a fitness coach, nutritionist, data analyst and TCM practitioner, to build a health profile and track workouts, diet and wellness over time.
Works with
Categories
调用Strategy Engine MCP服务器执行量化策略。当用户需要运行因子表达式策略、回测交易策略或执行金融分析时调用此技能。基于MCP Server工具的实际默认值设置。. Strategy Engine is an agent skill from LeoYeAI/openclaw-master-skills.
Strategy Engine fits situations like: tasks that involve MCP servers.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill strategy-engine -a claude-code`. Or copy the skill folder (skills/strategy-engine in LeoYeAI/openclaw-master-skills) into .claude/skills/strategy-engine in your project. Claude Code loads it when a task matches its description.
Run `npx skills add LeoYeAI/openclaw-master-skills --skill strategy-engine -a codex`. Or copy the skill folder (skills/strategy-engine in LeoYeAI/openclaw-master-skills) into .agents/skills/strategy-engine in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill strategy-engine -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/strategy-engine, .gemini/skills/strategy-engine, .github/skills/strategy-engine and .opencode/skills/strategy-engine in your project.
SKILL.md names no scripts, command-line tools or credentials: Strategy Engine is instructions for the agent only. Our summary lists: Python 3.
SKILL.md names 1 domain. In commands or code: visual.hzyotoy.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Strategy Engine is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Strategy Engine: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 37k stars) and Fastmcp Client CLI (PrefectHQ/fastmcp, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,158 GitHub stars. The repository holds 1,215 skills in this directory. The repository was last updated on July 20, 2026.
Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.