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aetherlang-strategy
Nobel-level strategic business intelligence with game theory, Monte Carlo simulations, market analysis, and implementation roadmaps.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Nobel-level strategic business intelligence with game theory, Monte Carlo simulations, market analysis, and implementation roadmaps.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
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| name | aetherlang-strategy |
| description | Nobel-level strategic business intelligence with game theory, Monte Carlo simulations, market analysis, and implementation roadmaps. |
| version | 1.0.1 |
| author | contrario |
| homepage | https://masterswarm.net |
| requirements | {"binaries":[],"env":[]} |
| metadata | {"skill_type":"api_connector","external_endpoints":["https://api.neurodoc.app/aetherlang/execute"],"operator_note":"api.neurodoc.app operated by NeuroDoc Pro (same as masterswarm.net), Hetzner DE","privacy_policy":"https://masterswarm.net"} |
| license | MIT |
Game theory, Monte Carlo simulations, behavioral economics, and competitive war gaming. The most advanced AI strategy engine available.
Source Code: github.com/contrario/aetherlang Author: NeuroAether (echelonvoids@protonmail.com) License: MIT
⚠️ External API Notice: This skill sends queries to api.neurodoc.app for processing.
Three V3 strategy engines in one skill:
Every response includes:
Ask naturally about any business/strategy topic:
POST https://api.neurodoc.app/aetherlang/execute
Content-Type: application/json
{
"code": "flow Strategy {\n using target \"neuroaether\" version \">=0.2\";\n input text query;\n node Guard: guard mode=\"MODERATE\";\n node Engine: apex analysis=\"strategic\";\n Guard -> Engine;\n output text report from Engine;\n}",
"query": "Your strategy question here"
}
{
"code": "flow Market {\n using target \"neuroaether\" version \">=0.2\";\n input text query;\n node Engine: marketing analysis=\"competitive\";\n output text report from Engine;\n}",
"query": "Your market question here"
}
Returns structured Greek output with markdown headers. Typical response: 4000-8000 characters covering all mandatory sections with quantified data.
Built by NeuroAether — From Kitchen to Code 🧠