Other skills

Expert causal reasoning and mental model construction for diagnosis and prediction in complex domains

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Implement executable BDI reasoning with explicit negation, paraconsistent revision, trigger-based commitment updates, and abduction. Use for runtime agent semantics and conflicting desires. NOT for purely axiomatic modal logic, black-box planners, or classical logic without operational semantics.

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Naturalistic causal reasoning for failures and disputed attribution. Use when postmortems or root-cause debates need reframing. NOT for causal inference, fault trees, or fully observed failures.

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JADE platform for building FIPA-compliant multi-agent systems with standardized agent communication

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Integration of BDI agent architecture with soft systems methodology for complex organizational problem-solving

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Describe patterns that may indicate bad-faith or manipulative communication including gish gallop, whataboutism, tone policing, motte-and-bailey, sealioning, lying by omission, deflection, and strategic evasion. Use when the user suspects manipulation, notices repeated non-engagement, wants to analyze debate tactics, or mentions 'bad faith', 'deflection', 'manipulation', 'dodging the question', 'avoiding the issue', 'gaslighting', 'moving goalposts'. Describes behavioral patterns without making psychological diagnoses.

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Strategic patterns for solving intractable problems through cascading approximation, self-improvement, and heterogeneous evaluation from DeepMind's AlphaGo system

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Foundational concurrent computation model where actors communicate exclusively through asynchronous message passing

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Apply normative BDI reasoning to agents that must detect norms, choose which commitments to internalize, and resolve conflicts by comparing consequences. Use when obligations, prohibitions, or policies collide in autonomous systems. NOT for simple fixed-priority rules, pure constraint satisfaction, or domains where no real normative conflict exists.

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Use when asked to babysit or watch a PR, when watching an open pull request, after opening or pushing to a PR, or when asked to monitor CI, review comments, merge conflicts, whether a PR is ready to merge, or to trigger a re-review.

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Manages the Morphir knowledge base under kb/ — OKF bundles and concept documents. Use when adding content to a bundle, creating a new bundle, checking the knowledge base for conformance or provenance drift, building or querying its SQLite index, managing intent through its lifecycle, recording or reading architectural decisions as Decision Records, or navigating, searching and listing its bundles, concepts and links.

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Manages intent — features, enhancements, bugs and the reasoning behind them — recorded as prose in the knowledge base with a lifecycle. Use when capturing new work, moving work through backlog, refinement or implementation, releasing or cancelling work, or asking what is pending, in flight, or no longer valid.

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Validate and normalize declared local review-run exports into a bounded canonical summary. Use only when the user explicitly asks to normalize review runs or use the bundled synthetic fixture.

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Prepare a traceable local Fusion engineering review draft with source identities, artifacts and unresolved assurance gates. Not for sending, publication or release approval.

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Deterministically score comparable imported review-run lanes with root-cause metrics and uncertainty. Use only when the user explicitly asks to benchmark normalized local review runs or use the bundled synthetic fixture.

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Recommend a conservative shadow-only review reference architecture from valid offline evidence. Use only when the user explicitly asks for a reference-architecture recommendation or uses the bundled synthetic fixture.

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Prepare qualified Fusion CAM setups, operations, toolpaths and quarantined NC candidates using explicit manufacturing assets and authority. Never controls physical equipment.

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Implement randomization methods for RCTs. Use when user mentions: random assignment, treatment allocation, stratification, blocking, permutation randomization, cluster randomization, randomization balance, misfit handling, re-randomization.

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Causal inference with treatment/control groups over time. Use when user mentions: treatment effects, policy evaluation, pre/post comparison, parallel trends, DID, DiD, difference in differences, natural experiment, quasi-experimental.

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协作校准 / 认知校准 / 问题脑暴：当用户有模糊感受说不清问题、方案越补越复杂、想挑战假设、 想重新定义问题、想知道成熟领域里怎么叫这个问题时使用。 可用中文唤起："帮我想想""先聊一下""一起脑暴""我感觉有问题但不清楚是什么" "先别执行，帮我看清问题""挑战我的假设""这个方案是不是想错了""帮我做认知校准" "双向钢人""钢人论证一下""正反最强论证，给我明确判断"。 也适用于：用户直接说"帮我做 X 功能""接入 Y 平台"但背景复杂、或对话早期感觉方向可能跑偏时， AI 主动识别并建议进入脑暴模式。 这里的“挑战假设”主要指问题定义、目标、约束和领域定位层面的假设；如果问题已确认且用户已有具体方案、 计划、架构或决策要压力测试，应转用 grill-me。不用于翻译、摘要、格式整理、单文件小改等简单执行任务。

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Use when / 当用户进入一个新项目或跨仓库工作区，需要扫描产品、用户、业务工作流、技术架构、仓库边界、领域语言、阅读入口和历史踩坑，并生成或持续维护全局 PROJECT_CONTEXT.md。适合用户说“先完整看下项目并沉淀上下文”“给这个项目建上下文文档”“把这次踩坑更新到项目上下文”。不用于单个 PRD 起草、纯目录治理或一次性代码实现。

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方案拷问 / 压力测试：当用户有一个产品方案、架构设计、计划或决策，想被连续追问、反方挑战、 压测取舍和失败模式时使用。可用中文唤起：“拷问我的方案”“压力测试这个设计”“帮我问 hard questions” “这个方案哪里会翻车”“grill me”。目标是一问一答把决策树走清楚，不是直接替用户写最终方案。 跨 Skill 运行时只输出 Challenge/Critic Handoff，并把一个 gap 返回最小责任节点；不重写完整方案。 如果用户还不知道真正问题是什么，先用 ai-collaboration-calibration；如果用户要标准 PRD 交付准备度评审， 用 prd-review。

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方案闭环：当用户显式调用 `$solution-loop`，或明确要求进入“方案闭环”时使用。 面向已经存在候选方案、但需要反方挑战和定点修订的场景，在 brainstorming Maker 与 grill-me Critic 之间最多循环三轮，直到方案确认或进入 Human Gate；不用于从零脑暴方案。

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Agent trace 诊断器：当用户提供 trace、日志 JSON、agent 执行记录、工具调用序列，或说“看下这个日志/trace，定位根因”“只说明问题不要改文件”时使用；用中文输出核心根因、可视化执行流程图、trace 证据链、可能文件/行和修复建议，默认只读。不用于没有日志证据的新功能设计、普通代码解释或直接修复代码。

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