'structural' 검색 결과

Explore code through structural search: find functions and classes, view file outlines, and open only the implementations you need. It uses tree-sitter, a parser that identifies code structure, to limit how much source text is loaded.

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• Finding functions related to a feature
• Navigating the structure of a large source file
• Reading only a specific function's implementation

Review and add automated accessibility checks for components, pages, and continuous integration workflows. Use axe-based tools to detect structural and attribute issues, while identifying checks that still need keyboard or screen reader testing.

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• Review accessibility coverage for shared components
• Add accessibility checks to page flow tests
• Run accessibility tests in CI

Run protein structure prediction, molecular design, and docking tools on Tamarind Bio without a local GPU. Guides tool selection, input validation, job submission, batch processing, and result retrieval through its REST API or MCP server.

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• Predicting protein structures in the cloud
• Running binder or antibody design tools
• Submitting batches of sequences for analysis

Read, analyze, modify, and visualize molecular structures with RDKit in Python. Calculate molecular properties, search for similar structures or specific structural patterns, apply reaction rules, and generate 2D or 3D coordinates with detailed control over processing.

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• Calculating properties for a collection of molecules
• Searching for molecules with similar structures
• Filtering molecules by specific structural patterns

Screen compound libraries for drug discovery using drug-likeness guidelines, structural alerts that flag patterns for review, and molecular complexity measures. Combine criteria to shortlist candidates and retain filtering results with reasons for further assessment.

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• Screen compound libraries with drug-likeness rules
• Flag PAINS and other structures for review
• Combine property, structure, and complexity filters

Resolve a design question by building a temporary, runnable prototype. Explore logic through clickable scenarios or compare three structurally different interface variants, then record the user's decision while keeping the prototype separate from production code.

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• Comparing three different interface structures
• Exploring state transitions through clickable scenarios
• Resolving design discussions that need a visible example

Review module relationships and change history to identify structural improvements in a codebase. Receive ranked candidates with file references, suggested changes, and risks, plus a recommendation for where to start, without modifying code.

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• Reviewing architecture after a batch of changes
• Investigating changes that touch too many files
• Prioritizing structural improvements

Generate trading signals using Smart Money Concepts, a framework for interpreting institutional trading behavior. Analyze trend continuation, reversals, price gaps, and order concentration zones, then combine structural signals and gaps to determine a trading direction.

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• Build signals from trend continuation and reversal patterns
• Filter structural signals using price gaps
• Adjust swing detection and closing-price break rules

Plan team structure and hiring by defining needed roles, headcount, timing, and costs. Produce text-based org charts and prioritized hiring roadmaps, and identify structural risks such as excessive management overhead or dependence on one person.

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• Plan required roles, headcount, and hiring dates
• Review reporting lines and team boundaries
• Set priorities and sequence upcoming hires

Design and review system architecture, compare databases and structural approaches, and document technical decisions. Included tools generate architecture diagrams, inspect dependencies, and assess project structure to identify circular relationships, excessive coupling, and opportunities for improvement.

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• Comparing monolith and microservices approaches
• Choosing a database for project requirements
• Generating diagrams from an existing project

Analyze pull requests and source code across multiple programming languages to identify complexity, security risks, and structural problems. Produce prioritized findings and structured review reports to guide code review decisions.

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• Prioritizing files for a pull request review
• Finding structural problems in source code
• Checking changes for exposed secrets and injection risks

Reorganize NemoClaw documentation into focused pages with clearer navigation and a single home for each topic. Plan or implement structural changes while preserving useful information and existing published URLs, then validate routes, links, and builds.

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• Splitting long documentation pages into focused topics
• Consolidating duplicated troubleshooting and reference content
• Reorganizing navigation while preserving published URLs

Plan a 10–12 page systems research paper for conferences such as OSDI and SOSP. Get section page budgets, paragraph roles, and writing patterns that connect the research problem, design decisions, and evaluation evidence.

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• Planning sections within a systems conference page limit
• Structuring an abstract and introduction
• Explaining design choices alongside alternatives

Plan systems conference papers with section page budgets, paragraph outlines, and writing patterns. Organize design choices, experimental evidence, and related work into a clear argument, then review the draft with submission checklists.

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• Planning sections and page budgets for a systems paper
• Explaining design choices and alternatives
• Organizing evaluation experiments and results

Develop security design improvements from vulnerability reports, incident documents, source code, or scan results. Compare architectural options with before-and-after diagrams, engineering tradeoffs, and migration plans to support a concrete design decision.

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• Find structural causes shared by several vulnerabilities
• Compare security architecture options and costs
• Explain proposed changes with before-and-after diagrams

Compare code structure across two commits, tags, or directories to identify security-relevant changes. Produces a report on new attack paths, privilege boundaries, complexity, and how widely a change could affect other functions.

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• Compare code structure before a release
• Investigate new attack paths after code changes
• Track untrusted data flows across a refactor

Inspect DWARF, the debug information stored with compiled code, to find functions and types, check structural integrity, and clarify specification details. Also helps write parsing tools using libraries such as pyelftools, libdwarf, or gimli.

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• Finding functions and types in debug information
• Checking DWARF structural integrity
• Comparing debug-information quality metrics

Develop security design improvements from vulnerability reports, incident documents, source code, or scan results. Compare architectural options with before-and-after diagrams, engineering tradeoffs, and migration plans to support a concrete design decision.

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• Find structural causes shared by several vulnerabilities
• Compare security architecture options and costs
• Explain proposed changes with before-and-after diagrams

Validate an implementation plan before execution through structural checks and specialist agent reviews. Check dependencies, files, tests, and architectural decisions, then update the plan to resolve findings, requesting human decisions when existing guidance is insufficient.

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• Checking an implementation plan for missing sections or dependencies
• Reviewing security or database changes before implementation
• Checking consistency with architectural decisions and project rules

Write and review GitHub Actions workflows for syntax and structural correctness. Check quoting around expressions and special characters, then validate with actionlint, a workflow checker, to catch errors that ordinary YAML parsing can miss.

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• Reviewing new or edited GitHub Actions workflow files
• Checking quotes around expressions containing special characters
• Investigating workflow failures before any jobs start

Convert SKILL.md files into SSL JSON, a structured format describing invocation conditions, workflow stages, and individual actions. Receive a validation report covering format errors, invalid references, and rejected outputs.

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• Converting skill documents into structured JSON
• Reviewing skill stages and individual actions
• Checking workflow references and transitions

This skill covers Bayesian estimation and inference in quantitative social science. Use when the user is specifying priors, running MCMC, diagnosing chain convergence, or reporting posterior summaries — including hierarchical models, Bayesian structural models, and small-sample settings where priors regularize. Triggers on "Bayesian estimation", "Bayesian inference", "MCMC", "Markov chain Monte Carlo", "Stan", "PyMC", "NumPyro", "prior", "posterior", "credible interval", "Bayesian structural", "Bayesian BLP", "Bayesian DSGE", "hierarchical model", "random effects Bayesian", "posterior predictive check", "Bayes factor", "prior predictive check", "NUTS", "HMC", "Hamiltonian Monte Carlo", "R-hat", "rhat", "effective sample size", "ESS", "Bayesian calibration", "posterior distribution", "prior elicitation", "weakly informative prior", "brms", "rstanarm", "cmdstanpy", "pymc", "arviz".

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This skill covers game-theoretic methods in structural econometrics and industrial organization. Use when the user is working with strategic interactions, equilibrium analysis, or game-theoretic structural models — including entry games, conduct testing, auction models with strategic bidding, bargaining, or matching markets. Triggers on "Nash equilibrium", "subgame perfect", "best response", "strategic interaction", "entry game", "conduct testing", "auction", "mechanism design", "matching market", "bargaining", "BNE", "Bayesian Nash", "static game", "dynamic game", "repeated game", "multiple equilibria", "equilibrium selection", "discrete game", "oligopoly", "game-theoretic", "player", "payoff", "strategy", "dominant strategy", "Bresnahan-Reiss", "Ciliberto-Tamer", "partial identification", "set identification", or markup test.

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Detect structural variants from long-read alignments using Sniffles, cuteSV, and SVIM. Use when detecting deletions, insertions, inversions, translocations, or complex rearrangements from ONT or PacBio data, especially those missed by short-read methods.

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