Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Use when comparing competing states or explaining why weaker parties prevail. Analyzes material resources, leadership quality, and intangible factors like popular support, governance quality, moral legitimacy, and application of benevolence (仁义).
Use when analyzing dynastic cycles, predicting political decline, or extracting governance lessons from history. Traces patterns of rise and fall (兴坏之端) across origins, development, turning points, and collapse.
Use when verifying rumors or sensitive intelligence without direct confrontation. Involves selecting an emotionally reactive proxy, designing a calibrated provocation to trigger unguarded responses, and interpreting reactions to confirm or deny suspected information, as demonstrated by Prince Shangchen.
Use when analyzing rebellion risk or diagnosing why uprisings succeed or fail. Applies the principle 安民可与行义而危民易与为非 to assess popular contentment vs. endangerment using economic, legal, and safety indicators. References Chen She's rebellion.
Use when analyzing whether virtue correlates with worldly success or questioning cosmic justice (天道). Compares virtuous sufferers (伯夷, 颜回) against prosperous villains (盗跖) using Sima Qian's method to assess 天道无亲常与善人 and inform ethical decision-making.
Use when mapping Xiongnu governance hierarchy. Covers the Shanyu (单于), Four Great Kings, 24 Wanqi commanders, territorial east/west/center divisions, noble clans (呼衍, 兰, 须卜), and subordinate officer structures.
Use when analyzing Xiongnu cavalry warfare doctrine. Covers engagement rules (有利则进不利则退), deception tactics (诱敌, 示弱, 详败), lunar timing for attacks, and individual spoils-based motivation systems.
Use when analyzing Xiongnu governance cycles. Covers the three annual assemblies (正月 court, 五月 Longcheng worship, autumn Dailin census), daily solar/lunar rituals, and meeting protocols for tribal administration.
Facilitates deliberate skill development during AI-assisted coding. Offers interactive learning exercises after architectural work (new files, schema changes, refactors). Use when completing features, making design decisions, or when user asks to understand code better. Supports the user's stated goal of understanding design choices as learning opportunities.