'reasoning' 검색 결과

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

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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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Set up and troubleshoot Honcho memory for Hermes so user preferences and facts carry across conversations. Configure how memory is recalled, manage separate agent identities across profiles, and adjust context size, observation settings, and reasoning frequency.

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• Setting up cloud or self-hosted memory for Hermes
• Investigating missing memories or profile synchronization issues
• Sharing user context across multiple agent profiles

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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• Investigating unexpectedly high agent costs
• Reviewing cache usage
• Reconsidering models used by sub-agents

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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• Coordinate code changes across multiple AI agents
• Get deployment suggestions from past results
• Review failures from similar merge attempts

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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• Training and evaluating molecular property prediction models
• Building molecule generation workflows
• Learning representations of protein sequences and structures

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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• Check what was decided in previous discussions
• Review how the team usually handles a task
• Trace how a practice changed over time

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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• Reviewing design decisions from a new team member’s perspective
• Making an ADR’s decision and reasoning easier to follow
• Removing temporary project context from long-lived records

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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• Explain the reasoning behind a team decision
• Refine PR descriptions and design notes
• Explain technical details to nontechnical readers

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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• Comparing evidence for a disputed technical choice
• Synthesizing research with conflicting findings
• Reviewing a proposal's evidence and unresolved questions

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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• Training models to follow JSON or XML formats
• Designing rewards for math and coding tasks
• Adjusting training to available GPU memory

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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• Extract useful information from large email archives
• Organize decisions and advice from meeting transcripts
• Route reports to suitable models by importance

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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• Compare technologies before choosing one
• Document the reasoning behind a design decision
• Review a proposed system design

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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• Investigating the root cause of a persistent problem
• Reconsidering an approach after repeated failed attempts
• Comparing perspectives on a difficult planning problem

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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• Breaking down the causes of high costs
• Reviewing requirements inherited from past projects
• Reconsidering a design that has stalled

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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• Investigating repeated agent failures
• Reviewing incorrect tool choices
• Refining prompts through repeated evaluation

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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• Compare options and their tradeoffs
• Review architecture choices and wider effects
• Investigate incidents and challenge assumptions

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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• Finding research directions beyond incremental improvements
• Connecting mechanisms across distant disciplines
• Reframing a research problem that feels stuck

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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• Train models to follow JSON or XML output formats
• Reward correct answers in math or coding tasks
• Balance correctness, format, and style during training

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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• Choosing a direction with long-term consequences
• Assessing the reasoning behind an expert's claims
• Building judgment in an unfamiliar field

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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• Compare technologies before choosing one
• Document the reasoning behind a design decision
• Review a proposed system design

Review a product launch against the team's legal framework and risk standards. Produce a category-by-category memo with required actions, owners, and deadlines, plus a separate tracker comment that omits internal legal reasoning.

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• Reviewing legal issues before a product launch
• Listing required actions, owners, and deadlines
• Checking privacy and AI issues in a new feature

Helps an agent take over an existing role after a planned handover. It guides checks of inherited records, identity, authority, and recurring duties, while explaining how to recover the predecessor’s reasoning and preserve lessons for the next agent.

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• Getting oriented after a planned agent handover
• Checking inherited records and recurring duties
• Recovering the reasoning behind earlier decisions

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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