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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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Expert causal reasoning and mental model construction for diagnosis and prediction in complex domains
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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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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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Atom of Thoughts (AoT) reasoning - decompose complex problems into atomic units with confidence tracking and backtracking. For genuinely complex reasoning, not everyday questions. Triggers on: atomise, complex reasoning, decompose problem, structured thinking, verify hypothesis.
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帮助在Hermes中安装、配置和排查Honcho，让用户偏好与事实可跨对话保留。支持调整记忆读取方式、信息量及记录频率，并配置多个档案。
Analyze Claude Code and Codex usage logs to identify spending drivers, including cache usage, model choices, reasoning, and long sessions. Receive evidence-backed recommendations and hypothetical savings estimates to help decide which changes to try.
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Manage code changes across multiple AI agents and record their operations and outcomes. Use those records to find successful patterns, review past failures, and get suggestions for similar development or deployment tasks.
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Build and troubleshoot TorchDrug 0.2.1 workflows for molecular property prediction, molecule generation, protein modeling, and knowledge graph reasoning. Connect datasets, models, training tasks, and evaluation while checking version compatibility and common implementation errors.
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Answer questions about past decisions, usual practices, and changes over time by reasoning across Hindsight memories. When information spans multiple memory banks, combine the answers and identify their sources, while acknowledging gaps or contradictions.
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Review and rewrite architecture decision records, documents that explain design choices, from a newcomer’s perspective. Identify missing context, buried decisions, and temporary details, then reorganize each document so future readers can follow the reasoning.
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Clarify decision reasoning and combine multiple perspectives into explanations suited to the audience. Helps refine PR descriptions, design notes, and nontechnical summaries while making assumptions, tradeoffs, and uncertainty explicit.
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Builds an evidence map for a disputed decision, linking supporting evidence, counterevidence, qualifications, and missing facts to precise source passages. Produces a structured JSON artifact that makes the reasoning and unresolved questions available for review.
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Guides language model training with GRPO in TRL, comparing several responses and rewarding better results. Covers dataset preparation, scoring rules for correctness and format, training configuration, monitoring, and saving models for later use.
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Process large email, document, and transcript archives by routing items to AI models according to their importance. Rule-based privacy checks run before model calls, while extracted information becomes linked knowledge pages with progress checkpoints.
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Compare technology options, review a system design, or plan a new component from requirements and constraints. The skill organizes the reasoning into an architecture decision record, a document covering alternatives, trade-offs, consequences, and next steps.
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Analyze a difficult problem with two agents chosen for contrasting reasoning styles. They independently investigate causes and plan a solution, then exchange arguments to resolve disagreements and produce a shared recommendation without changing files.
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Analyze a problem by separating fundamental facts from policies and untested assumptions. The skill produces a component breakdown, a constraint table, and a rebuilt solution, helping explain which inherited assumptions may be limiting the available options.
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Analyze records of an AI agent’s decisions and tool use to find repeated failures, lost goals, and misunderstood instructions. Use the findings to refine prompts, rerun tasks, and turn debugging lessons into reusable skills.
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Review consequential or ambiguous decisions through ten stages of structured reflection. Separate observations from assumptions, examine alternatives and risks, and produce a recommendation supported by evidence, with verification needs and conditions for revisiting the decision.
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Explore computer science and AI research directions using eight creativity frameworks. Combine ideas across fields, question hidden assumptions, and transfer mechanisms from other disciplines to develop research questions with testable predictions.
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Fine-tune language models with TRL using GRPO, a method that compares scored responses to the same prompt. Covers training data, scoring functions, GPU-conscious settings, and monitoring for tasks with measurable correctness or required output formats.
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Examine important choices and assess claims by rebuilding their reasoning from basic knowledge. Use mental models, reusable ways to understand problems, to compare conclusions, identify unknowns, and consider long-term consequences.
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