'reasoning' 검색 결과

Understand and extend GitNexus taint analysis, which tracks data from input sources to potentially dangerous operations. Use its implementation guidance to investigate missed or incorrect findings, add detection models, and validate analysis changes.

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• Investigating missed or incorrect data-flow findings
• Adding input sources, dangerous operations, or sanitizers
• Reviewing analysis that follows data across functions

Use GitNexus to inspect which conditions control a statement and how variable values flow within a function. Understand early exits, investigate empty query results, and guide changes to the tool’s control and data dependency queries.

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• Check which conditions control a statement
• Trace a variable within a function
• Investigate empty pdg_query results

Solve competition mathematics problems and examine proofs through independent agents that actively look for gaps, counterexamples, and misused theorems. Produces a checked argument or clearly states what remains unresolved, with optional PDF output.

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• Solve an IMO or Putnam problem
• Check a competition proof for gaps
• Find counterexamples to claims within a proof

Prepare GRPO reinforcement learning configurations for tasks with programmatically checkable answers, such as math, code, and structured output. Design and inspect reward functions, then select training variants when runs show instability or exploit flaws in scoring.

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• Preparing GRPO training for tasks with automatic correctness checks
• Designing rewards for output format and correctness
• Inspecting reward scores before training

Write and maintain Architecture Decision Records (ADRs), documents explaining important technical choices. Capture the context, options, decision, and consequences, then organize records and track their status so teams can revisit the reasoning.

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• Record the reasons for choosing a framework or database
• Document design options and their trade-offs
• Explain past technical decisions to new team members

Split multi-step coding, research, or testing into focused assignments for sub-agents, which are agents handling specific parts of a task. The parent agent retains design decisions, integration, and final verification while independent work runs in parallel.

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• Exploring separate groups of files in parallel
• Assigning code changes with clear file ownership
• Running focused tests alongside other work

Maintain the reasoning behind portfolio holdings and watchlist companies. Record new developments, assess whether they support or weaken each argument, and produce an updated scorecard with confidence levels, upcoming events, and possible position changes.

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• Recording the rationale for a new investment
• Updating an investment thesis after earnings or news
• Reviewing arguments that support or challenge a holding

Help junior developers and AI newcomers understand code through guided questions and progressive hints. Support debugging, concept learning, and critical review of AI-generated code so learners can explain their reasoning and work toward solutions themselves.

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• Understand errors through guided questions
• Explain AI-generated code in your own words
• Learn with pseudocode and progressive hints

Use a constructive thinking partner to review assumptions, separate evidence from interpretation, and test alternative explanations. Refine conclusions and wording so claims match the available evidence and support decisions under uncertainty.

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• Checking assumptions before an important decision
• Reviewing whether evidence supports a conclusion
• Testing alternative explanations and counterexamples

Turn a Python script into a beginner-friendly project by improving its structure and naming, adding comments that explain the reasoning, and creating a README.md tutorial covering setup, code logic, and example usage.

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• Turning a Python script into learning material
• Explaining code logic to beginners
• Documenting setup steps and example usage

Turn code files into learning resources by adding comments that explain syntax, programming concepts, and design choices. Adjust explanation depth and repetition to the reader’s knowledge while preserving file structure, encoding, and syntax.

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• Add beginner-friendly explanations to existing code
• Document the reasoning behind syntax and design choices
• Adjust comment depth for readers with different experience

Add or verify model support in SGLang's Rust processor so chat requests produce the same prompt text and token IDs as Python. Use reference-generated test cases to find differences in tools, reasoning settings, conversation history, and errors.

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• Adding model support to sglang-processor
• Porting model rendering from sgl-router
• Rechecking prompt behavior after a Dynamo update

Apply 12 structured investing frameworks to evidence already collected. Each framework orders the questions to ask, defines reasons to reject a case, and identifies its limitations, helping explain why different approaches reach different conclusions.

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• Assess the same company through two or three frameworks
• Check the evidence behind an appealing growth story
• Identify conditions that rule out an investment case

Test whether you understand a concept and whether evidence supports a decision using frameworks drawn from Richard Feynman’s work. Explore concrete examples, simple demonstrations, counterevidence, and the limits of analogies to expose gaps in reasoning.

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• Checking understanding beyond memorized terminology
• Finding counterevidence to a favored conclusion
• Reviewing processes that prioritize form over results

Document major technical decisions with their context, alternatives, reasoning, and consequences. The skill provides Architecture Decision Record templates and maintenance practices so teams can review past choices, track replacements, and explain decisions to new members.

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• Recording reasons for technology choices
• Comparing design options and trade-offs
• Explaining past decisions to new team members

Make system design decisions by clarifying requirements, identifying constraints, and comparing the benefits and drawbacks of different approaches. Record the reasoning in architecture decision records, or ADRs, while considering simpler alternatives and the team's experience.

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• Organizing requirements before designing a system
• Comparing architectural patterns and trade-offs
• Documenting the reasons behind design decisions

Make resume achievements more specific by identifying useful numbers, such as workload, time saved, or revenue. Guided questions uncover measurable contributions, while conservative estimates and documented reasoning help describe experience when exact records are unavailable.

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• Add meaningful numbers to resume achievements
• Find measurable results in everyday work
• Estimate workload when exact records are unavailable

Set up model training with torchforge for reinforcement learning, where rewards guide improvement. Covers training configurations, custom reward and loss functions, and distributed execution with Monarch and TorchTitan, keeping algorithm experiments separate from infrastructure concerns.

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• Train mathematical reasoning models with GRPO
• Experiment with custom reward and loss functions
• Scale training across multiple GPUs or nodes

Set up reinforcement learning, which trains models using rewards, with verl. Follow workflows for preparing data, configuring GRPO or PPO training, scaling across GPUs, and addressing memory errors or unstable training.

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• Training a math reasoning model with GRPO
• Configuring PPO training with a critic model
• Scaling large model training across multiple nodes

Assess language models with over 60 academic benchmark tests using consistent prompts and scoring. Compare models, track performance during training, and organize research results with support for HuggingFace models, vLLM, and APIs.

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• Compare models on standard knowledge and reasoning tests
• Track model performance across training checkpoints
• Organize evaluation results for research reports

Run Gemini CLI to review code, assess technical plans, and analyze large collections of files. Choose a model and execution settings for the task, then capture findings about security, maintainability, architecture, or technical debt.

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• Reviewing security and quality across multiple files
• Checking architecture plans for missing components
• Analyzing the structure of a large codebase

Design autonomous AI agents that break goals into tasks, use tools, and review their own work. Covers reasoning and action loops, separate planning and execution, and reliability measures such as cost limits, validation, and logging.

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• Choosing how an agent plans and executes tasks
• Breaking goals into constrained steps
• Adding self-review and correction to an agent

Assign unowned leads and opportunities using company routing rules and existing account relationships. Prepare a recommendation with its reasoning, draft handoff notes, and apply ownership changes after a person accepts or overrides each recommendation.

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• Assigning newly received leads to sales representatives
• Reviewing a queue of unassigned opportunities
• Preparing handoff notes with assignment reasons

Review FastGPT pull requests in stages, starting with the intended change and key execution paths. Check security, performance, tests, and compatibility, then organize findings by severity into a review report and line-level comments.

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• Checking a pull request before merging
• Reasoning through normal, edge, and error cases
• Assessing API and database compatibility risks