'quality' 검색 결과

4人の異なる専門家ペルソナ（20年ベテランエンジニア、TDDエキスパート、Clean Codeエキスパート、バグハンター）で ソースコードを批判的にレビューするスキル。設計判断、テスト容易性、可読性、バグ検出の観点から コードの問題を検出し、改善提案を行う。Python/JavaScriptに対しては追加のチェックポイントを適用。 バグハンターは状態遷移、例外パス、依存関係、非同期競合に特化し、本番環境での問題を事前に検出する。 Use when reviewing source code from multiple expert perspectives to find design flaws, testability issues, code quality problems, and runtime bugs. Triggers: "critical code review", "multi-persona code review", "expert code review", "code quality assessment", "find bugs".

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Run an extremely strict maintainability review for abstraction quality, giant files, and spaghetti-condition growth. Use for a thermo-nuclear code quality review, thermonuclear review, deep code quality audit, or especially harsh maintainability review.

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Documentation quality standards and writing principles. Use when establishing formatting rules, reviewing doc quality metrics, creating writing guidelines, or enforcing consistent documentation style across a project.

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two-stage review (spec compliance first, then code quality), refactoring, and quality improvement. Use when reviewing code, eliminating code smells, reducing technical debt, refactoring methods, running self-critique loops, or improving maintainability and readability.

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Systematic design quality evaluation. Hierarchy, type, color, space, craft, system. Use when evaluating whether a design is ready to ship, running quality audits, or setting quality standards.

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Run or maintain the full tdmcp repo quality-audit team: command sweeps, all package/Makefile/CI gates, security review, usability/flow review, refactor/test-gap analysis, coverage hardening, QA, and follow-up fix waves. Use whenever the user asks for a complete audit, improve repo/code quality, test all commands, find security/usability failures, refactor debt, add missing tests, re-run the audit, continue a previous quality wave, or verify the repo is ready.

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Quality control for pooled CRISPR screens covering library representation, Gini index, log-skew, replicate Pearson and Spearman concordance, essentialome precision-recall AUC against CEGv2 (Hart 2017), Cas9 cut-toxicity diagnostics, copy-number amplicon detection (Aguirre 2016 / Munoz 2016), bottleneck propagation through plasmid pool, infection, selection, and endpoint stages, MOI verification, and DepMap-style screen-quality scoring. Use when assessing screen quality before hit calling, deciding whether to repeat or rescue a screen, diagnosing low-confidence hits, choosing between MAGeCK / BAGEL2 / Chronos based on quality grade, picking a normalization strategy from QC signatures, or evaluating whether an in-vivo screen retained adequate library complexity.

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Quality gates determinísticos para PHP e Laravel. Use quando o usuário pedir "quality gate PHP", "quality gate Laravel", "PHPStan", "Larastan", "Pest", "PHPUnit", "PHPMD", "Composer Audit", cobertura PHP ou baseline PHP.

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Quality gates determinísticos para PostgreSQL. Use quando o usuário pedir "quality gate PostgreSQL", "quality gate Postgres", migrations ou schema drift PostgreSQL, constraints ou índices PostgreSQL, EXPLAIN, plano de consulta ou baseline para PostgreSQL.

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Run an extremely strict maintainability review for abstraction quality, giant files, and spaghetti-condition growth. Use for a thermo-nuclear code quality review, thermonuclear review, deep code quality audit, or especially harsh maintainability review.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Quality control for pooled CRISPR screens covering library representation, Gini index, log-skew, replicate Pearson and Spearman concordance, essentialome precision-recall AUC against CEGv2 (Hart 2017), Cas9 cut-toxicity diagnostics, copy-number amplicon detection (Aguirre 2016 / Munoz 2016), bottleneck propagation through plasmid pool, infection, selection, and endpoint stages, MOI verification, and DepMap-style screen-quality scoring. Use when assessing screen quality before hit calling, deciding whether to repeat or rescue a screen, diagnosing low-confidence hits, choosing between MAGeCK / BAGEL2 / Chronos based on quality grade, picking a normalization strategy from QC signatures, or evaluating whether an in-vivo screen retained adequate library complexity.

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Great Expectations, dbt tests, anomaly detection, and data contracts for data quality. Activate on: data quality, data validation, Great Expectations, data contract, anomaly detection, SLA, freshness check, schema validation. NOT for: dbt model structure (use dbt-analytics-engineer), schema evolution (use schema-evolution-manager).

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Produces a narrow-interval quality signal for a WinDAGs skill that a third party can verify without inspecting model weights or method internals. The mechanism is attribution-kNN over a tamper-evident outcome log (task→skills→accept/reject store), combined with a conformal-prediction calibration certificate that provides a formal coverage guarantee. Thompson/Beta sampling is explicitly rejected as a category error: it produces a selection signal for stationary i.i.d. rewards, not an attestation signal for context-dependent LLM output quality. The resulting bundle — outcome-log Merkle root + conformal threshold + optional TEE guardrail signature — constitutes a "narrow-interval trust" credential: verifiable, bounded, and internals-opaque.

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Code quality standards for OneKey. Use when fixing lint warnings, running pre-commit tasks, handling unused variables, writing comments, or ensuring code quality. All comments must be in English. Triggers on lint, linting, eslint, oxlint, tsc, type check, unused variable, comment, documentation, spellcheck, code quality, pre-commit, yarn lint.

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Enforces minimum quality thresholds in CI including code coverage, linting, type checking, and security scanning. Provides required checks, PR rules, and automated enforcement. Use for "quality gates", "CI checks", "code quality", or "PR requirements".

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Claude Code에서 파일을 편집할 때 Plankton으로 자동 서식 정리와 코드 검사를 실행하도록 설정합니다. 남은 위반의 자동 수정과 재검사, 검사 규칙 보호도 다룹니다.

• 편집할 때마다 서식 정리와 코드 검사를 할 때
• 남은 검사 위반의 자동 수정을 설정할 때
• 검사 규칙이 완화되는 것을 막을 때

예측 시장과 거래 에이전트의 흐름을 약관, 데이터 품질, 비밀 정보, 개인정보, 거래 실행 관점에서 점검하고 문제점과 필요한 대책을 정리합니다.

• 거래 서비스 인증 추가 전 검토
• 자산 정보와 API 키 처리 점검
• 시장 데이터 품질과 출처 표시 확인

Claude Code가 작업을 마칠 때 학습 기록 수정 시각과 디스크 여유 공간을 확인해 조건에 따라 완료를 막습니다. 대화에서 절차 생략을 암시하는 표현도 경고합니다.

• 복잡한 편집 후 학습 기록을 확인할 때
• 디스크 공간 부족 시 완료를 막을 때
• 확인 생략을 암시하는 표현 탐지

Create HTML slides about choosing and connecting AI models in OpenDesign using your own API key. Use a terminal-style template to explain costs, quality, and model routing decisions, with speaker notes and presentation controls.

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• Explaining model choices to engineers and IT teams
• Comparing model costs, quality, and routing decisions in slides
• Preparing a terminal-style presentation with speaker notes

Restore image quality using restoration models hosted by fal.ai. The skill covers reducing blur and noise, fixing faces, and restoring old document images, with the full workflow available through the upstream skill bundle.

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• Reducing blur in images
• Removing image noise
• Fixing facial image quality

Download individual YouTube videos with a chosen quality, file format, and destination folder. Save video as MP4, WebM, or MKV, or download only the audio as MP3 using the included Python script.

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• Saving a video at a chosen resolution
• Downloading only the audio as MP3
• Choosing a video format and destination folder

Generate RuVLLM settings for local language model inference and create MicroLoRA or SONA instances. Adjust their small internal weights using quality scores as feedback, with instances held in server memory for subsequent adaptation calls.

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• Configure local language model inference
• Prepare inference tools for an air-gapped environment
• Adjust MicroLoRA using quality feedback

Review Front-End Checklist rule files and improve their instructions, explanations, and code examples. Use the project's quality scorer and structure validator to identify gaps, replace vague prompts, and organize each rule into a usable reference for developers and agents.

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• Expanding incomplete Front-End Checklist rules
• Making AI audit and repair prompts more specific
• Checking rule structure and quality before a PR

Evaluate 3D point clouds and movement trajectories against reference data with CloudAnalyzer. Prepare inputs, measure differences, apply quality thresholds, inspect results visually, and track baseline history for mapping, localization, and perception workflows.

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• Compare a generated point cloud with reference data
• Measure errors in estimated movement trajectories
• Check ground segmentation against quality thresholds