111건 · 관련도순
Simulate quantum dynamics, steady states, and spectra with QuTiP 5. The skill guides solver selection and checks physical assumptions, units, state validity, and numerical convergence so local simulation results can be reviewed with their limitations.
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Review development plans with external AI tools, then revise and review them again while actionable concerns remain. Choose reviewers and a cycle limit to work toward a plan with resolved review findings.
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Build and estimate economic models that represent underlying choices and incentives. Get guidance on demand, dynamic choice, and auction models, select estimation methods, and diagnose numerical problems such as failed convergence or sensitivity to starting values.
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Develop Bayesian models for quantitative social science, from choosing priors to reporting posterior results. Guide MCMC, a sampling method for estimating distributions, check convergence, and assess sensitivity for hierarchical models and small datasets.
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Use this skill during Phase 6 convergence checks. Provides four-dimensional convergence analysis, hallucination detection, and duplicate finding identification.
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Tracks iteration progress toward task completion goals. Monitors quality trends, detects plateauing, and recommends when to stop iterating. Activate on 'convergence tracking', 'iteration progress', 'quality trend', 'stop iterating', 'progress monitoring'. NOT for iteration detection (use dag-iteration-detector) or feedback synthesis (use dag-feedback-synthesizer).
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Monitors a specified object against explicit stopping criteria and distinguishes replica convergence, iterative progress, and workflow terminal state. NOT for execution or setting goals.
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Analyze large matrices before solving linear systems. Check structure and numerical stability, recommend preprocessing and solver settings, estimate selected solution values without a full solve, and assess convergence, memory use, and computational cost.
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Build and evaluate Bayesian models with PyMC and ArviZ, from choosing initial assumptions to checking predictions. The workflow covers sampling diagnostics, model comparison, and reports that explain results with uncertainty intervals.
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Problem-solving strategies for convergence in real analysis
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Think and work like an expert Mathematical Analyst. Use when a task calls for Mathematical Analyst judgment. Reasons from function-space topology, convergence modes, and constant-dependent inequalities (Hölder, Sobolev, Gronwall) through compactness theorems (Rellich-Kondrachov, Banach-Alaoglu), Calderon-Zygmund and Schauder estimates, and Lax-Milgram while treating limit-integral swaps without dominated convergence, boundary-degenerating constants, weak-versus-classical regularity gaps, and concentration-compactness loss as first-class failure modes.
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Run Phase 6 (convergence check) for the active VCSDD feature. Validates four-dimensional convergence: finding diminishment, finding specificity, criteria coverage, and duplicate detection.
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Review what Prisma Next migrations will run on merge or deploy, render the migration graph, resolve concurrent / diamond-convergence conflicts, and configure environment refs for CI. Use for "what migrations are going to run", "what runs on deploy", merge conflict, diamond convergence, concurrent migrations, migration status, ref management, staging, production, MIGRATION.DIVERGED, MIGRATION.NO_MARKER, MIGRATION.MARKER_NOT_IN_HISTORY, prisma migrate status, prisma migrate diff, prisma migrate resolve.
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Synthesizes actionable feedback from validation results, confidence scores, and iteration triggers. Creates structured improvement guidance for re-execution. Activate on 'synthesize feedback', 'improvement suggestions', 'actionable feedback', 'iteration guidance', 'feedback generation'. NOT for iteration detection (use dag-iteration-detector) or convergence tracking (use dag-convergence-monitor).
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Identifies when task outputs require iteration based on quality signals, unmet requirements, or explicit feedback. Triggers appropriate re-execution strategies. Activate on 'needs iteration', 'retry needed', 'not good enough', 'try again', 'refine output'. NOT for feedback generation (use dag-feedback-synthesizer) or convergence tracking (use dag-convergence-monitor).
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Expert-level real analysis knowledge. Use when working with limits, continuity, differentiability, Riemann and Lebesgue integration, sequences and series of functions, metric spaces, measure theory, or functional analysis foundations. Also use when the user mentions 'epsilon-delta', 'uniform continuity', 'uniform convergence', 'Lebesgue integral', 'measure theory', 'sigma-algebra', 'Banach space', 'Hilbert space', 'completeness', 'compactness', 'Heine-Borel', or 'dominated convergence'.
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Identifies when task outputs require iteration based on quality signals, unmet requirements, or explicit feedback. Triggers appropriate re-execution strategies. Activate on 'needs iteration', 'retry needed', 'not good enough', 'try again', 'refine output'. NOT for feedback generation (use dag-feedback-synthesizer) or convergence tracking (use dag-convergence-monitor).
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Synthesizes evidence-linked feedback from validation results, confidence evidence, and iteration triggers. Proposes bounded revision guidance for a stated acceptance failure. Activate on 'synthesize feedback', 'improvement suggestions', 'actionable feedback', 'iteration guidance', 'feedback generation'. NOT for iteration detection (use dag-iteration-detector) or convergence tracking (use dag-convergence-monitor).
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Facilitate idea generation sessions that actually produce diverse, useful options instead of degenerating into the first plausible answer. Use for product ideation, feature exploration, problem-reframing, naming, design alternatives, strategic option-generation, or any task where the failure mode is "we picked the obvious thing too fast." Covers solo and group sessions, divergence/convergence discipline, technique selection (SCAMPER, Crazy Eights, How Might We, 6-3-5 Brainwriting, Reverse Brainstorming, Worst Possible Idea, Random Stimulus), facilitation moves for handling dominant voices and groupthink, and structured convergence to a shortlist with rationale. Outputs a session artifact with options generated, evaluation, shortlist, and the rejected-but-interesting list.
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Multi-dimensional Quality Acceptance and Problem Convergence Engine - Deeply deconstruct requirements, eliminate extreme defects, define absolutely objective acceptance and failure criteria.
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공개 전 글이나 코드를 두 독립 에이전트가 같은 기준으로 심사합니다. 모두 통과할 때까지 수정과 재심사를 반복하고, 반복 한도에 도달하면 미해결 문제를 사람에게 넘깁니다.
프로젝트의 빌드, 타입 검사, 테스트 명령을 실행합니다. 성공 여부와 실패 내용을 보고서와 로그에 저장해 자동 수정 반복 작업의 종료 판단에 활용합니다.
Coordinate gossip protocols, where nodes pass information among peers, for distributed systems. The skill covers update propagation, state synchronization, conflicting changes, failure detection, and tuning communication so nodes eventually reach a consistent state.
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Write Workflow scripts that divide research, reviews, or migrations among agents. The reference explains parallel and staged processing, independent verification, failure handling, budgets, and resuming runs so the script has a clear execution structure.
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