'refinement' 검색 결과

Generate systematic refinement steps from high-level specifications to concrete implementations in Isabelle/HOL or Coq, preserving correctness obligations at each step. Use when working with formal verification, program refinement, proof development, or when translating abstract specifications into executable code while maintaining formal guarantees. Supports data refinement (abstract types → concrete structures), algorithmic refinement (specifications → algorithms), and stepwise refinement with proof obligations.

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Improve existing code through repeated cycles of testing, implementation, and refactoring. The skill covers performance bottlenecks, error recovery, code coverage, and documentation, helping developers refine behavior and structure while keeping tests passing.

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• Refactoring existing code while keeping tests passing
• Adding tests before implementing expected behavior
• Investigating slow database access and cache usage

Validate particle data and run RELION refinement for single-particle cryo-electron microscopy. Check independently refined half maps, calculate diagnostic map correlations, and perform soft-mask postprocessing while preserving execution logs and restart information.

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• Check particle metadata and image-stack consistency
• Run refinement on a selected particle population
• Inspect agreement between independent half maps

Review agent work histories to identify recurring failures, repeated discoveries, and stalled tasks. Improve the prompts, specialist agents, skills, tools, and memory that guide future work, then submit those changes separately for review.

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• Addressing recurring command or tool failures
• Recording findings that agents repeatedly rediscover
• Turning repeated procedures into skills or tools

Design guide + benchmarked pitfalls for building Monte-Carlo / Simulated Annealing refinement layers on top of greedy hex-grid layouts for code visualization (packages/gui/src/store/hexLayout.ts, sandbox/hex-sandbox, future Rust ports). Use when: (1) planning to add MC/SA on top of an existing hex-grid pack, (2) deciding whether a simpler SA would match a hierarchical MC with force-directed + chain drag, (3) budgeting wall-clock for million-node cold-start, (4) debugging "blob turns Swiss-cheese" or "folders stop nucleating" during MC refinement, (5) choosing between folder cohesion and raw Σ-link-length as cost function. Contains four hard-won, quantified lessons and anti-patterns that would take a full day of experiments to rediscover.

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새 기능과 복잡한 구현을 명세, 의사 코드, 아키텍처 설계, 개선, 완료 단계로 나누어 진행합니다. 요구 사항과 인수 기준을 정리하고 기존 지식도 활용합니다.

• 새 기능의 요구 사항 정리
• 복잡한 구현의 진행 방식 정리
• 시스템 아키텍처 변경 계획

Simulate a uniform gas mixture in a closed, insulated reactor at constant volume or pressure. Calculate ignition delay from the peak temperature rise rate, export temperature and species histories, and check conservation and numerical stability.

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• Calculate ignition delay at constant volume or pressure
• Inspect temperature and species histories
• Compare ignition delays across reaction mechanisms

Convert agreed goals, constraints, and success criteria into a Seed specification an agent can execute and verify. A quality review checks consistency and testable outcomes, with any suggested refinements left for the user to approve.

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• Formalize requirements agreed in conversation
• Create a specification after a requirements interview
• Check acceptance criteria for measurable outcomes

Run repeated rounds of code fixes, performance improvements, or other refinements with defined goals and progress tracking. Evaluate each round, stop when appropriate, and optionally use numeric measurements to keep improvements or roll back regressions.

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• Repeat fixes and tests until all tests pass
• Improve response time toward a target
• Refine error messages against clarity criteria

Turn written instructions into infographics for statistics, comparisons, timelines, and more. Choose layouts and color palettes, optionally research supporting facts, and receive versioned images with automated quality reviews and refinement when scores fall below the chosen threshold.

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• Turning survey results into a statistical infographic
• Creating a side-by-side product comparison
• Visualizing company history or project milestones

Run the measurement and ceremony layer of a delivery team: the metric set, estimation and capacity, the five sprint ceremonies, and the sprint report and quarterly rollup. Use this skill whenever the user is acting as PMO or scrum lead, planning a sprint, running standup, refinement, review, or retro, measuring velocity, cycle time, or throughput, checking capacity, or reporting on how a team is doing. Triggers on PMO, sprint planning, plan the sprint, standup, refinement, backlog grooming, sprint review, retro, velocity, capacity, cycle time, lead time, throughput, WIP, burndown, carryover, sprint report, story points to hours, how is the team doing, delivery metrics, agent lanes. Also triggers when the user asks how much work fits in a sprint or whether a team is on track, even if they never say sprint or PMO.

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Polishes working code through successive quality passes in fresh subagents. Use after tests pass when code needs multi-dimension refinement before release.

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Run a self-refinement loop when a Blender result is subpar, user expectations are not met, validation fails, or repeated issues reveal missing skill depth. The loop diagnoses the gap, decides whether to repair the artifact or acquire/refine a generic skill, sanitizes lessons for reusable publication, updates docs/versioning, validates, and prepares stage/commit/push when explicitly requested.

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Extract maximum value from existing code — performance optimization, API surface refinement, and dead weight elimination. The art of turning base code into gold through systematic identification and amplification of value-bearing patterns. Use when optimizing a working but sluggish codebase, refining an API surface that has accumulated cruft, reducing bundle size or memory footprint, or preparing code for open-source release — when code works correctly but doesn't shine and needs polish rather than a full rewrite.

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Premium UI/UX design audit and refinement skill. Conducts systematic visual audits of existing apps and produces phased, implementation-ready design plans. Use this skill whenever the user asks to audit a UI, improve an app's visual design, make an interface feel more polished or premium, review design consistency, fix visual hierarchy, or refine spacing/typography/color. Also trigger when the user says "design review", "make it look better", "UI polish", "visual refinement", "design pass", "audit the design", or references making an app feel more professional. This skill is purely visual — it does not touch functionality, logic, or features. It elevates what exists.

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Normative sufficiency conditions for Initial Boundary Conditions (IBCs) and the SOP for the cheap-tier boundary refinement loop (/boundary). Trigger when: - Crafting, auditing, or refining a prompt/IBC destined for an expensive (architect-class) model or an autonomous worker dispatch. - Evaluating whether a task frame is sufficient to bound an agent walk. - Prompt contains: /boundary, IBC, initial boundary condition, boundary contract, sufficiency conditions, worker prompt, prompt refinement.

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SOP for the automated refinement loop (/refine) workflow. Trigger when: - Exhaustively auditing and optimizing pre-existing codebase artifacts. - Applying iterative contraction mapping to reduce code entropy. - Prompt contains: /refine, refinement loop, contraction mapping, fixed-point sweep.

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API와 개발 도구 계획을 개발자의 관점에서 대화하며 검토합니다. 경쟁 제품과의 차이와 도입 과정의 장애물을 정리하고 목적에 맞게 개선점을 살펴봅니다.

• API나 SDK 계획 재검토
• 개발자의 초기 도입 장애물 확인
• 경쟁 제품과 개발자 경험 비교

구현된 UI와 디자인 요구 사항의 시각적 차이를 줄이도록 평가와 조정, 마무리를 반복하는 작업을 안내합니다. 별도로 설치하는 원본 작업 흐름을 찾는 입구로 사용합니다.

• 구현 화면과 디자인 요구 사항을 비교합니다
• 시각적 차이를 반복해서 조정합니다
• 디자인에서 구현까지의 검토 단계를 계획합니다

요구 사항 정리부터 설계, 테스트를 먼저 쓰는 구현, 검토, 배포까지 개발을 단계별로 진행하고 여러 AI 에이전트가 작업과 지식을 나누는 절차를 다룹니다.

• 새 기능 계획 및 구현
• 기존 코드 수정과 테스트 추가
• 코드 보안 및 성능 검토

구현된 기능을 명세와 대조하고 코드 검토, 테스트 추가, 성능 검증을 진행합니다. 요구 사항과 코드·테스트의 대응표 및 문서를 정리해 배포 준비 상태를 확인합니다.

• 구현이 명세를 충족하는지 확인
• 미검증 요구 사항의 테스트 추가
• 요구 사항·코드·테스트 대응표 작성

요구 사항 정리부터 설계, 테스트 우선 구현, 검토, 배포까지 단계별로 진행합니다. Claude Flow로 여러 에이전트의 역할을 나누고 개발 전반을 관리합니다.

• 요구 사항부터 새 기능 개발
• 테스트와 함께 기존 코드 개선
• 버그 조사와 재발 방지 테스트 추가

Combine laboratory animal welfare measurements into reference-relative RELSA severity scores. Analyze individual trajectories, explore forecasts for specified observation times, and derive candidate score zones, with explicit checks on normalization, reference selection, prediction intervals, and evaluation limits.

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• Combine animal welfare measurements into daily scores
• Compare group severity using a shared reference
• Explore severity forecasts at specified observation times

Turn descriptions of scientific components and relationships into PNG diagram drafts with Nano Banana 2. Gemini 3.7 Flash reviews each draft and may request one revision, while saved image versions and review logs support manual checking.

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• Drafting neural network architecture diagrams
• Illustrating biological pathways
• Creating study flowcharts