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Read and update application settings through APIs, including system prompts, reasoning limits, IP filters, and login requirements. Also manage proxy and cache settings, with guidance on how reasoning settings affect requests sent to AI providers.
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Define reusable reasoning and decision patterns for agents, embed them in executable workflows, and review their performance. Share successful approaches, including conditional decision trees, stepwise problem solving, analogies, and evaluation from multiple perspectives.
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Build agents that record task outcomes and use past experience to choose future approaches. Implement pattern matching, strategy comparisons, persistent learning records, and metrics for checking how recommendations and results change over time.
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Document a solved and verified problem when its reasoning is missing from code, tests, and existing documentation. Save one durable lesson in the repository so future agents can find it and avoid repeating substantial investigation.
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Practice legal reasoning through hypothetical cases and follow-up questions. The skill challenges weak explanations, tracks recurring mistakes, and prompts you to work out the answer yourself, confirming your reasoning once you reach a sound conclusion.
中文简介正在准备中。当前显示英语简介。
Production-ready CSS transitions for web apps. Use when implementing notification badges, dropdowns, modals, panel reveals, page transitions, card resizes, number pop-ins, text swaps, icon swaps, success checks, avatar group hovers, error state shakes, search/input clear, skeleton loaders, shimmer text, sliding tabs, tooltips, staggered text reveals, card hover tilt, plus-to-menu morph, accordions, toasts, like buttons, learn-more hovers, checkbox checks, spinning counters, toggles, AI thinking states, reasoning streams, streaming text, matrix dot loaders, or banner stacking. Triggers on "add a transition", "animate the dropdown", "make the modal open smoothly", "swap icon", "page slide", "stagger animation", "open / close transition", "make it animate", "fade between", "success animation", "form error", "shake on invalid", "hover lift", "avatar stack hover", "clear the search", "skeleton loader", "loading shimmer", "shimmer text", "sliding tabs", "segmented control", "tooltip", "reveal text", "tilt card", "3D hover tilt", "cursor glare", "plus to menu", "FAB morph", "accordion", "collapsible", "expand / collapse", "disclosure", "toast", "snackbar", "like button", "heart animation", "learn more arrow", "checkbox", "check animation", "spinning counter", "odometer", "slot machine digits", "toggle", "switch", "thinking states", "AI status line", "agent reasoning", "reasoning stream", "streaming text", "stream words in", "matrix loader", "dot loader", "banner stack", "stacked toasts". Also "motion tokens", "scan for ad-hoc transitions", "replace hardcoded durations with motion tokens", "tokenize my animations", and the commands transitions reveal, transitions review, transitions apply, transitions refine.
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Use when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed patterns
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ENHANCE overlay on [[dspy]] — the upfront rubric for choosing a reasoning SHAPE (Predict / ChainOfThought / ReAct / ProgramOfThought) BEFORE you write a prompt or pick an optimizer. The local `dspy` skill lists the modules but never surfaces the *selection criterion*: reasoning shape is chosen by task structure, not by reflexively defaulting to CoT. Activate every time a new LM-calling node/step is added to a pipeline. Do NOT activate for one-shot prompts, optimizer/teleprompter choice (that is the dspy SOP's job), or non-LM control flow. Search keywords: chain of thought vs ReAct, when to use CoT, reasoning type, ReAct vs CoT vs PoT, which dspy module, predict vs chain of thought.
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Summarize long Isabelle or Coq proof scripts into high-level logical steps and reasoning flow. Use when users need to: (1) Understand the structure of a complex proof, (2) Document proof strategies for others, (3) Extract the key reasoning steps from verbose proof scripts, (4) Create readable proof outlines from detailed tactical proofs. Produces hierarchical outlines with moderate detail showing proof structure, main cases, key lemmas, and reasoning flow for both Isabelle/Isar and Coq proofs.
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Apply semi-formal certificate reasoning to code analysis — patch verification, fault localization, patch equivalence. Use when reviewing patches, hunting bugs across scopes, comparing fixes, or when code reasoning requires tracing execution across files/modules. Triggers on code review, bug localization, patch comparison, name shadowing, scope analysis, regression checking.
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Audit the reasoning of a document or argument (memo, proposal, investment analysis, board paper, article, the user's own draft): claims, evidence, unstated assumptions, logical gaps, fallacies, and what would falsify it, each anchored to quoted text. Use whenever the user wants reasoning examined, in any language ("audit this argument", "is this analysis sound", "poke holes in this proposal", "review the logic of my draft"; Vietnamese "phản biện giúp tôi", "soi lập luận này", "tài liệu này có lỗ hổng gì", "góp ý bản nháp của tôi"), even when they never say "critical thinking". Also use when the user drops a document and asks whether to trust or act on it, or asks to see their reasoning profile. Not for company-level strategic bets (strategy-board) or learning a topic through dialogue (socratic-questor).
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AI multi-path reasoning coherence using bee democracy — independent evaluation of competing approaches, waggle dance as reasoning-out-loud, quorum sensing for confidence thresholds, and deadlock resolution. Use when forage-solutions has identified multiple valid approaches and a selection must be made, when oscillating between options without committing, when justifying an architecture or tool choice with structured reasoning, or before an irreversible action where the cost of the wrong choice is high.
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Work with BioReason-Pro predictions and reasoning traces for protein function. Use when: comparing BioReason outputs against curated reviews, converting web exports to YAML, reviewing reasoning trace quality, or running the SFT/RL comparison. Triggers: "bioreason", "GO-GPT predictions", "reasoning trace review", "bioreason comparison", "SFT vs RL".
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Automatically assess task complexity and adjust reasoning level. Triggers on every user message to evaluate whether extended thinking (reasoning mode) would improve response quality. Use this as a pre-processing step before answering complex questions.
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Use for bounded reasoning, partial planning, commitment policy, deliberation cost, and reconsideration-threshold design instead of assuming free deliberation. NOT for exhaustive planning, static optimization, or domains where reconsideration cost is irrelevant.
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Expert causal reasoning and mental model construction for diagnosis and prediction in complex domains
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Identify and counteract cognitive biases in medical decision-making through systematic error analysis and contextual algorithm application. For diagnostic reasoning, treatment decisions, and clinical judgment improvement. NOT for basic medical knowledge, technical procedures, or non-clinical healthcare domains.
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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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Structured second-pass reasoning after code review or plan evaluation. Offers named reasoning methods (pre-mortem, inversion, red team, Socratic, etc.) to re-examine findings through a specific lens. Use after /mk:review verdict or any time deeper analysis is needed on an existing output. NOT for initial review (see mk:review); NOT for creating implementation plans (see mk:plan-creator).
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Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).
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Identify and counteract cognitive biases in medical decision-making through systematic error analysis and contextual algorithm application. For diagnostic reasoning, treatment decisions, and clinical judgment improvement. NOT for basic medical knowledge, technical procedures, or non-clinical healthcare domains.
中文简介正在准备中。当前显示原文说明。
Expert causal reasoning and mental model construction for diagnosis and prediction in complex domains
中文简介正在准备中。当前显示原文说明。
Use for bounded reasoning, partial planning, commitment policy, deliberation cost, and reconsideration-threshold design instead of assuming free deliberation. NOT for exhaustive planning, static optimization, or domains where reconsideration cost is irrelevant.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。