957건 · 관련도순
Finds and loads relevant Google skills for choosing or setting up services, databases, storage, authentication, and other platform components. It supplies guidance for comparing Google options, understanding trade-offs, and recognizing when another choice fits better.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Design production GKE clusters, which run containerized applications on Google Cloud, using recommended Autopilot settings. Review networking, security, monitoring, and upgrade choices, then compare existing clusters against the configuration and identify deviations and remedies.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Design or review a system’s overall structure, compare technology choices, and plan for growth and failures. Produce architecture diagrams and Architecture Decision Records, documents that explain key choices, alternatives, and their consequences.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Clarify implementation choices before planning a development phase. The skill reviews existing project context and code, asks about unresolved decisions, and records the answers in a context file for research and planning agents.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Plan a system or service from requirements through components, APIs, data storage, and reliability. Produce a structured design document with diagrams, stated assumptions, trade-offs between choices, and decisions to revisit as usage grows.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build course and workshop slides that keep learning objectives visible on the left. The deck includes concepts with examples, multiple-choice self-check questions, a wrap-up, and a preview of the next module, with warm paper tones and Playfair typography.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Classify agent work as ready to continue, needing human judgment, or intentionally paused. Structure decision requests with evidence, a recommended choice, and clear outcomes, then connect the recorded decision to the next responsible agent.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Open the Creative Production start screen when a creative request is still undefined. The app presents starter choices that lead to an editable prompt, providing an entry point before a concrete production request is submitted.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Design appointment-based service websites with calm photography, spacious layouts, and clear treatment comparisons. Carry the same visual style through location, practitioner, and time selection, including booking confirmation, validation errors, retries, and preserved choices.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Apply purpose-based checks while building an interface, or audit an existing project with prioritized findings. Reviews visual choices, content, working interactions, and evidence for claims while requiring a clear design direction.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Explain code, design concepts, and system behavior at a chosen level of detail. Get a brief overview or a deeper walkthrough covering execution flow, design choices, alternatives, usage examples, and related code.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Fill in related settings for .NET project creation while preserving explicit user choices. Produce a table explaining each choice and an exact dotnet new command, checking available options against the selected template’s help.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Send feedback about a Weave Router model choice or model performance. A bundled script passes the feedback to the router for recording, and the agent then reports the router’s response.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Switch automatic model selection to lower-cost options for agent tasks, either for one session or through a saved setting. Explicit model choices remain in effect, while architecture and security review tasks use a separate economy selection.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Prepare scientific figures for papers, reports, and saved deliverables by checking data fidelity, chart choice, labels, axes, and color consistency. Verify exported figures for overlapping text and readability while keeping fonts, palettes, and framing configurable.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Create a concise exam review from supplied notes or PDFs and a syllabus. Get topic definitions, key points, answer examples or multiple-choice cues, and practice questions, with priorities adjusted to the time available.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
This skill covers structural econometric models. Use when the user is building, estimating, or debugging structural models — including BLP demand estimation, dynamic discrete choice, auction models, or any workflow involving moment conditions, nested fixed-point algorithms, or MPEC formulations. Triggers on "structural model", "moment conditions", "NFXP", "MPEC", "BLP", "random coefficients", "dynamic discrete choice", "CCP", "Rust model", "auction estimation", "GMM objective", "inner loop", "contraction mapping", or convergence/starting value problems in optimization-based estimation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Authors and edits AWS Step Functions state machines: writes Amazon States Language (ASL) in JSONata, and chooses and structures state types (Task, Choice, Map, Parallel, Pass, Wait, Succeed, Fail). Covers ASL syntax, JSONata data transformation and variables, Retry/Catch error handling, service integrations (.sync, waitForTaskToken callbacks), Distributed Map for large-scale S3/CSV processing, saga/compensation patterns, Standard vs Express workflow choice, TestState API unit testing, and migrating state machines from JSONPath to JSONata. Use when the user is building, authoring, debugging, or migrating a Step Functions state machine or ASL definition, or orchestrating multi-step workflows with branching, retries, or human-approval callbacks, even if they don't say 'Step Functions.' Do NOT use for general Lambda function code, API Gateway, EventBridge wiring, or SAM/CDK application packaging.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build branching dialogue and narrative — a node/choice graph with conditions, variables, and localization hooks — and choose between authoring tools Ink and Yarn Spinner or a custom data-driven runner. Engine-neutral. Use when the user mentions dialogue system, branching dialogue, conversation tree, choices, Ink (.ink), Yarn Spinner (.yarn), or NPC dialogue.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Behavioral guardrails for Codex coding work based on common user complaints. Use when Codex is asked to implement, modify, debug, review, test, or operate on a codebase and should avoid unsafe scope expansion, stale edits, fake completion, brittle edits, shallow debugging, superficial patch-on fixes, one-off special-case code, short-term design choices that hurt maintainability, blindly following incorrect user assumptions, poor dependency choices, generic product or UI output, over-mocked tests, noisy approvals, verbose status reports, or leaking internal reasoning into project artifacts or user-facing UI.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
ALWAYS invoke this skill before asking the user any technical question or offering options, and whenever they ask to be asked in plain words - "ask simple", "ask me simply", "ask me in plain words" - in any language. ALWAYS invoke it too when the user asks for your view on a technical choice: whether it is worth doing, more than the problem needs, or replaceable by something simpler, and which option you would take. Invoke it even when the code makes the answer look obvious: the checks are what make the answer more than a guess. Rewrites the question in plain words and always ends with one marked recommendation. A structural choice first passes six checks, shown as a table: effort now, simpler substitute, extra work later, lock-in, over-engineering, easy to undo. Doing nothing is always weighed.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Detects A/B chromatin compartments from balanced Hi-C contact matrices via eigenvector decomposition of the distance-normalized, Pearson-correlated cis matrix with cooltools (eigs_cis), then orients (phases) the compartment eigenvector against a GC or gene-density track so the active (A) sign is not arbitrary. Covers the eigenvector-is-a-choice problem (per-arm view_df to remove the centromere gradient; picking the eigenvector by max correlation with activity, not by eigenvalue), GC phasing with bioframe.frac_gc, resolution choice (100kb-1Mb), saddle plots and saddle_strength for compartmentalization strength, the cohesin-loss-strengthens-compartments result, subcompartments (SNIPER/Calder/dcHiC), and cross-condition compartment switching. Use when calling A/B compartments, computing E1/eigenvectors, phasing the eigenvector, building saddle plots, choosing a compartment resolution, quantifying compartment strength, or comparing compartmentalization across conditions.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Identifies super-enhancers from H3K27ac, MED1, or BRD4 ChIP-seq using ROSE, ROSE2, LILY, HOMER -style super, and ENCODE dELS cross-referencing. Handles peak stitching parameters, ranking choices, hockey-stick inflection, marker choice (H3K27ac vs MED1/BRD4), and cross-condition comparison with spike-in normalization. Constructs core regulatory circuitry (Saint-Andre 2016) from SE-encoded TFs. Use when identifying cell-identity / cancer-associated regulatory domains, comparing super-enhancers between conditions, identifying master transcription factor networks, or predicting BET-inhibitor responsiveness.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.