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"Apply public choice theory to analyze political decision-making as rational self-interested behavior. Use this skill when the user needs to evaluate government policy failures, rent-seeking costs, voting outcomes, or bureaucratic incentives, especially when the assumption of benevolent government is questionable.".
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"Apply governance theory to analyze multi-level, network, and collaborative governance arrangements beyond traditional government. Use this skill when the user needs to evaluate public-private partnerships, analyze multi-stakeholder governance structures, compare governance models across sectors, or assess institutional arrangements for collective decision-making — even if they say 'who governs this', 'public-private collaboration', or 'how are decisions made across organizations'.".
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"Apply behavioral economics concepts including bounded rationality, prospect theory, mental accounting, and nudge theory to analyze decision-making biases. Use this skill when the user needs to understand why people make irrational economic decisions, design choice architectures, or apply nudges to influence behavior — even if they say 'why do customers make bad choices', 'how do we encourage people to save more', or 'design a better default option'.".
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"SME and family-business management for Taiwan's industrial structure (98% SME, 70%+ family-controlled listed). Integrates four pillars: resource-constrained decision-making, family governance (Tagiuri-Davis three circles), four-layer succession, and professionalization. Use for sub-200-employee strategy, capital-constrained prioritization, family-vs-professional-manager conflict, founder handover, family-constitution design, or SME financing limits. Triggers: 『二代接班』『家族企業』『中小企業』『家族憲章』『家族辦公室』『傳承規劃』『小公司策略』『資源有限』『家族跟專業經理人』『三圓模型』『隱形冠軍』. For 成大 EMBA SME strategy, 政大 EMBA family-business succession, 逢甲 EMBA SME management cases. Fills Asgard gaps in resource-constrained strategy and Chinese-family governance.".
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Structures a clear, actionable malware analysis report covering summary, sample identity, capabilities, IOCs, ATT&CK mapping, and detection guidance for both technical and decision-making audiences. Activates for requests to write, structure, or review a malware analysis or reverse-engineering report.
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Structure and run a post-interview candidate debrief. Use when the user says "run the debrief", "how do we decide on this candidate", "let's calibrate", "we need to make a hire/no-hire call", "prep the debrief meeting", or wants to structure the decision-making process after interviews are complete - even if they don't explicitly say "debrief". Also use when a recruiter wants to prevent groupthink or HiPPO effect (highest paid person's opinion) from dominating the decision.
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Mobile-first design thinking and decision-making for iOS and Android apps. Touch interaction, performance patterns, platform conventions. Teaches principles, not fixed values. Use when building React Native, Flutter, or native mobile apps.
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Python development principles and decision-making. Framework selection, async patterns, type hints, project structure. Teaches thinking, not copying.
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Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.
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Node.js development principles and decision-making. Framework selection, async patterns, security, and architecture. Teaches thinking, not copying.
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API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.
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Server management principles and decision-making. Process management, monitoring strategy, and scaling decisions. Teaches thinking, not commands.
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Design thinking and decision-making for web UI. Use when designing components, layouts, color schemes, typography, or creating aesthetic interfaces. Teaches principles, not fixed values.
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Production deployment principles and decision-making. Safe deployment workflows, rollback strategies, and verification. Teaches thinking, not scripts.
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Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.
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This skill provides a way to retrieve information from the AgentScope library for analysis and decision-making.
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Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.
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API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.
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当用户需要做重大决策、团队存在群体思维、或想将决策权下放到一线时触发此技能。帮助建立知情指挥决策模型，让最有信息的人做最终决定。
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当管理者犹豫是否留用"还行但不够出色"的员工、想知道何时该让人离开、或团队中有人离开反而让人松了口气时触发。提供基于"留任测试"的决策框架和体面离职流程。
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Splits scarce time, budget or traffic across competing products, channels, campaigns or variants by treating them as a multi-armed bandit - Thompson sampling over beta posteriors, with optimism for anything not yet tried enough to judge. Use when deciding what to work on next, how to divide a marketing budget, which product to prioritise, whether to kill something that is underperforming, or when picking a winner among test variants.
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Decides how far ahead to plan, what to discount future payoffs by, and which backlog items can be dropped without analysis - using receding-horizon planning, an explicit discount factor, and branch-and-bound pruning against the incumbent. Use when building a roadmap, prioritising a backlog, arguing about short-term versus long-term, setting quarterly or annual plans, or when planning has become an end in itself.
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Turns a vague "what should we do about X" into a scored decision - explicit actions, an explicit unknown, an explicit prior, and one utility scale - then picks by maximum expected utility and checks the result for framing effects. Use when facing a choice between options under uncertainty, when a decision has stalled in circular debate, when someone asks "should we do A or B", or before writing any decision record, strategy memo, or roadmap commitment.
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Prices research, tests, surveys, dashboards and experiments before running them, by computing how much the result would raise expected utility - which is zero whenever no outcome would change the decision. Use when someone proposes an A/B test, user survey, market study, competitor analysis, analytics build, consultant, pilot, or "let's gather more data", and when deciding how large a study needs to be.
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