991건 · 관련도순
Polishes academic English paragraph by paragraph while preserving the author's meaning and technical content. Reviews grammar, word choice, voice, logical flow, and sentence structure, providing specific revision reasons alongside complete polished paragraphs.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Turn an accepted feature proposal into an implementation specification grounded in existing code. Define scope, explain design decisions, track unresolved questions, and document migration steps and tests, with implementation tasks added when the work is approved for building.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Turn a technical choice into a structured debate between different AI model families. Assign explicit positions, launch the discussion, and summarize agreement, disagreement, and a recommendation supported by the strongest argument that survives the debate.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Create PowerPoint presentations for talks, pitches, and reports, edit existing slides, or read and summarize a deck. The skill guides color choices, typography, varied layouts, and content checks to give each slide a clear message.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Review existing charts for clear communication, appropriate chart types, readable labels, scales, and color choices. Get concrete suggestions for simplifying layouts, improving accessibility, and making the visualization suit its intended audience.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Turn a feature specification into a technical implementation plan. Organizes technology choices, research decisions, data models, and interface agreements into project files, while checking that the design follows the project's documented principles.
한국어 요약은 준비 중입니다. 영어 요약을 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.
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.
Assesses RNA-seq data quality specifically for alternative splicing analysis. QC layers include experimental design audit (library prep, read length, depth, replicates), STAR 2-pass cohort-style alignment, junction saturation curves and discovery plateau detection, novel-vs-known junction ratio diagnostics, junction-overhang distribution, splice-site strength scoring (MaxEntScan intrinsic + SpliceAI context-aware), strandedness verification, GENCODE basic vs comprehensive choice, and rRNA contamination screening. Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice — failures silently bias PSI estimates and inflate novel-junction false positives. Use when evaluating data suitability for splicing analysis, troubleshooting low event detection, or designing sequencing experiments where AS is a primary endpoint.
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.
Faithfully append a human modeler's choice and rationale to one canonical per-subquestion JSONL decision ledger. Use after a choice card is answered or when migrating legacy decision artifacts; never originate or improve the decision.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate scaffolds that gradually increase student choice, voice, and ownership within a learning task. Use when students depend heavily on teacher direction and need to develop autonomy.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generates standalone interactive HTML "deal cards" that translate complex regulations into negotiation-ready reference tools, systematically distinguishing mandatory obligations from negotiable implementation choices. Use when the user needs an interactive regulatory guide for (1) contract negotiation support, (2) client education or internal training, (3) regulatory briefings for commercial stakeholders, or (4) structured comparison between required and flexible compliance paths. Primary focus on EU digital regulation (Data Act, AI Act, CRA, DORA, NIS2, GDPR) but the structural pattern transfers to any regulation where separating hard obligations from implementation choice is the point. Supports bilingual output where the jurisdiction calls for it.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert blueprint for visual novels (Doki Doki Literature Club, Phoenix Wright, Steins;Gate) focusing on branching narratives, dialogue systems, choice consequences, rollback mechanics, and persistent flags. Use when building story-driven, choice-based, or dating sim games. Keywords visual novel, dialogue system, branching narrative, typewriter effect, rollback, bbcode, RichTextLabel.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert patterns for branching dialogue systems including dialogue graphs (Resource-based), character portraits, player choices, conditional dialogue (flags/quests), typewriter effects, localization support, and voice acting integration. Use for narrative games, RPGs, or visual novels. Trigger keywords: DialogueLine, DialogueChoice, DialogueGraph, dialogue_manager, typewriter_effect, branching_dialogue, dialogue_flags, localization, voice_acting.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Route a Bevy 0.20 educational loop. Use when prompts and choices are components, lesson totals are one resource, and grading must run after selection in one chain. Keywords: Bevy, Educational, LessonProgress, Choice, chain, genre.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert blueprint for visual novels (Doki Doki Literature Club, Phoenix Wright, Steins;Gate) focusing on branching narratives, dialogue systems, choice consequences, rollback mechanics, and persistent flags. Use when building story-driven, choice-based, or dating sim games. Keywords visual novel, dialogue system, branching narrative, typewriter effect, rollback, bbcode, RichTextLabel.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Expert patterns for branching dialogue systems including dialogue graphs (Resource-based), character portraits, player choices, conditional dialogue (flags/quests), typewriter effects, localization support, and voice acting integration. Use for narrative games, RPGs, or visual novels. Trigger keywords: DialogueLine, DialogueChoice, DialogueGraph, dialogue_manager, typewriter_effect, branching_dialogue, dialogue_flags, localization, voice_acting.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Deliberate-choice procedure for a medium-to-high-stakes engineering fork — when the first plausible solution (the instinct, the default next-token pull) would be costly to get wrong. Fires AUTONOMOUSLY: invoke proactively whenever a fork fits, never wait to be asked — mechanical triggers include a fix failing for the 2nd-3rd time, adding a dependency, schema/migration design, deleting or deprecating things others depend on, changing a public API, choosing an architecture or stack. Refuse the premature collapse: hold real options open (urge · contrarian · synthesis · out-of-box · intuitive dots · precedent · first-principles), ground each branch in at least one fact from outside the model (codebase, docs, benchmark, spike), future-model consequences (blast radius, reversibility, maintenance, pre-mortem), collapse by deliberate choice, then try to refute the winner before acting. Log the decision and rejected branches in docs/decision_logs/. Not for routine calls — a decision worth more than one forward pass.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guide a user from an unclear idea to an actionable plan through the agent harness's native ask tool and clickable choices. Use when the user wants a guided requirements interview, step-by-step questions, help deciding what to build, or a button-led path from goals to scope, solution, and technology choices. Do not turn an ordinary implementation request or a single clarification into an interview.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Extracts and records architectural decisions from diffs, conversation context, and explicit choices. Supports automatic extraction from staged changes, deduplication against prior decisions, and persistent ADR-format logging with spec traceability. Use after a significant design or architecture choice, before committing a diff that embeds implicit decisions, or when the same question keeps resurfacing.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.