「engineer」の検索結果

抽出済みJSONファイル（馬・血統・レース出走データ）をSQLiteデータベースにインポートする。「DBインポート」「データベースに保存」「JSONをDBに登録」といった依頼で発動する。

有馬記念分析システムの命名ドメイン適合性レビュー。関数名・変数名・型名が競馬ドメインの意図（馬・騎手・調教師・血統・レース・出走・着順・上がり3F・馬場・会場・枠順・ローテ）を表現しているか、汎用名・技術接尾辞・命名規則の逸脱（snake_case 混入・アンダースコア付きメソッド名）・同一概念に別名が混在していないか（venue と course、同名異義の型）を検証。Use when user says '命名レビュー', 'review naming', '名前チェック', or 'ドメイン命名確認'.

JRA公式サイトから特定レースの出馬表HTMLを取得して馬データを抽出する。「データ取得」「JRAからデータを取ってきて」「出馬表を取得して」といった依頼で発動する。

agent 向けテキスト指示（skill / slash command / task プロンプト / コード生成プロンプト）を、バイアスを排した実行者に動かしてもらい、両面（実行者の自己申告 + 指示側メトリクス）で評価して反復改善する手法。改善が頭打ちになるまで回す。Use when user says 'プロンプトを改善して', 'スキルをチューニング', 'empirical-prompt-tuning', or after creating/heavily revising a skill or prompt.

有馬記念分析システムで利用可能なスキル一覧と使い方、スコア配分、基本ワークフローを表示する。「ヘルプ」「使い方を教えて」「何ができるの」といった依頼で発動する。

有馬記念分析システム（Bun + TypeScript CLI）のコード品質・コードスタイルレビュー。機械検証の通過（bun run c / bun run lint / bun test）、非推奨 API の使用、死にコード・消し残し、マジックナンバーと固定語彙の constants 集約、コメントの why 品質（足し算側）、既定値・フォールバックの規律、非同期とエラーハンドリング（握り潰し catch・await 漏れ）、関数構成とファイル構成（メイン関数先頭・400行）、制御フロー（条件連鎖・ガード節の羅列）、コレクション操作（reduce・entries・長チェーン）、否定条件 + else、オーケストレーションの平坦化、型安全（any・as）、import の一貫性、CLI 出力（console）の置き場所を検証。Use when user says 'コードレビュー', 'review code', '規約チェック', 'スタイルチェック', or '死にコード確認'.

Analyze binaries or protocols you own (or have license to inspect) to improve security, compatibility, or incident response. Not for cracking, DRM bypass, or malware creation.

日本語の概要は準備中です。原文の説明を表示しています。

Design, implement, and review web interface layout, component states, feedback, and motion within the current CodeConductor workflow. Use for concrete web UI work or requested UI audits, not backend-only tasks or native mobile apps.

日本語の概要は準備中です。原文の説明を表示しています。

Web Performance Engineering — analyzes Core Web Vitals using the PageSpeed Insights API (PSI v5) and applies the 80/20 principle: identify the 20% of changes that produce 80% of the performance gain. Produces a prioritized markdown report with quantified estimates and framework-specific code. Trigger: When auditing page speed, analyzing Core Web Vitals, or optimizing web performance for any URL.

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Designs cytosine (CBE, C-to-T) and adenine (ABE, A-to-G) base-editor guides by positioning the target base at the activity-peak of the editing window (protospacer positions ~5-7, PAM-distal numbering), minimizing bystander edits for product purity, reading dinucleotide context (APOBEC1 TC favored / GC disfavored), and selecting the editor variant (BE4max, ABEmax, ABE8e, YE1/SECURE, TadCBE, CGBE, SpG/SpRY-BE). Covers knockout by premature stop (CRISPR-STOP/iSTOP) and splice-site disruption, the three off-target classes (Cas-dependent, Cas-independent DNA, RNA), outcome prediction (BE-Hive/DeepBE), and the base-vs-prime-vs-HDR decision. Use when installing a transition mutation without a double-strand break, knocking out a gene without indels, or choosing CBE vs ABE. Generic guide scoring, prime editing, and HDR donors are separate skills.

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Nominates and assesses CRISPR off-target sites genome-wide. Enumerates candidate sites by mismatch and bulge tolerance with Cas-OFFinder/CRISPRitz, ranks them with the published CFD score (SpCas9-only, relative ranker) or MIT/CRISTA/energy models, runs variant-aware screening against gnomAD/individual genomes (CRISPRme), and frames the empirical genome-wide discovery assays (GUIDE-seq, CIRCLE-seq, CHANGE-seq, DISCOVER-seq, Digenome-seq) and high-fidelity nuclease choice (HiFi Cas9, Sniper-Cas9, eSpCas9, SpCas9-HF1). Use when assessing guide RNA specificity, choosing among candidate guides, screening a therapeutic guide against population variation, or planning empirical off-target validation. Distinguishes predicted vs detected vs validated. On-target activity scoring and deaminase (Cas-independent) base/prime-editor off-targets are separate skills.

日本語の概要は準備中です。原文の説明を表示しています。

Designs pegRNAs and nicking guides for prime editing (PE) -- choosing the nick/strand, tuning the primer-binding site (PBS) and reverse-transcription template (RTT) as a per-locus panel, selecting the PE system (PE2/PE3/PE3b/PE4/PE5/PEmax/PE7), adding MMR-evading and PAM-disrupting silent edits, appending epegRNA 3' motifs (tevopreQ1/mpknot), and ranking with PRIDICT/DeepPrime. Covers twinPE/PASTE for large insertions and the prime-vs-base-editing decision. Use when designing a scarless point mutation, small insertion/deletion, or any of the 12 base conversions without a double-strand break, when efficiency is low and MMR inhibition or pegRNA stabilization is needed, or when routing a large insertion to an integrase method. Generic guide scoring and base editing are separate skills.

日本語の概要は準備中です。原文の説明を表示しています。

Designs donor/repair templates for precise CRISPR knock-ins -- choosing the format (ssODN, long-ssDNA/Easi-CRISPR, dsDNA/plasmid, AAV6), sizing homology arms, placing the cut within ~10 bp of the edit, and adding a mandatory codon-checked blocking (PAM/seed) mutation so the edited allele is not re-cut. Frames the HDR-vs-NHEJ-vs-MMEJ pathway competition, the MMEJ (PITCh) and homology-independent (HITI/HMEJ) alternatives for post-mitotic cells, ssODN strand/asymmetry choice, phosphorothioate end-protection, and ranked HDR enhancers. Use when designing a donor for a point mutation, epitope/fluorophore tag, allele replacement, or knock-in, or when HDR efficiency is low. Guide design and base/prime editing are separate skills.

日本語の概要は準備中です。原文の説明を表示しています。

Designs and ranks guide RNAs (sgRNAs) for CRISPR-Cas9/Cas12a gene knockout by scanning a target for PAM sites (NGG SpCas9, NNGRRT SaCas9, TTTV Cas12a, NG SpCas9-NG, near-PAMless SpRY), enumerating candidate spacers, applying hard filters (Pol-III TTTT terminator, 5' G, GC), ranking on-target activity with the context-appropriate model (Rule Set 2/Azimuth for U6/lentiviral, CRISPRscan for T7/embryo, DeepHF for high-fidelity variants, DeepCpf1 for Cas12a), and predicting the indel/frameshift outcome (Bae out-of-frame score, inDelphi, FORECasT, Lindel). Use when selecting sgRNAs to knock out a gene, choosing a nuclease/PAM for a constrained locus, picking which exon to target, or shortlisting guides before an off-target check. Off-target specificity, base/prime editing, and HDR donors are separate skills.

日本語の概要は準備中です。原文の説明を表示しています。

Build a differential-ready consensus peakset from per-replicate ATAC-seq peaks using iterative overlap removal, fixed-width re-centering, and majority-rule overlap. Use when generating a stable peak coordinate system for downstream differential accessibility, ML feature engineering, cross-sample comparison, or fixed-width peak counts; covers Corces 2018 iterative overlap (501 bp), DiffBind summit re-centering, and ENCODE consistency rules.

日本語の概要は準備中です。原文の説明を表示しています。

Generates Spring Boot 3.x configurations, creates REST controllers, implements Spring Security 6 authentication flows, sets up Spring Data JPA repositories, and configures reactive WebFlux endpoints. Use when building Spring Boot 3.x applications, microservices, or reactive Java applications; invoke for Spring Data JPA, Spring Security 6, WebFlux, Spring Cloud integration, Java REST API design, or Microservices Java architecture.

日本語の概要は準備中です。原文の説明を表示しています。

This skill enforces Sentry's blog writing standards across every post — whether you're helping an engineer write their first blog post or a marketer draft a product announcement.

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You are a senior backend engineer operating production-grade services under strict architectural and reliability constraints. Use when routes, controllers, services, repositories, express middleware, or prisma database access.

日本語の概要は準備中です。原文の説明を表示しています。

Senior embedded software engineer specializing in firmware and driver development for ARM Cortex-M microcontrollers (Teensy, STM32, nRF52, SAMD).

日本語の概要は準備中です。原文の説明を表示しています。

Core UI/UX engineering skill for building highly interactive, spatial, weightless, and glassmorphism-based web interfaces using GSAP and 3D CSS.

日本語の概要は準備中です。原文の説明を表示しています。

Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

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Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

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Forces the laziest solution that actually works, simplest, shortest, most minimal. Channels a senior dev who has been paged at 3am for a factory class that wrapped one function: question whether the task needs to exist at all, reach for the standard library before custom code, native platform features before dependencies, one line before fifty. Supports intensity levels: lite, full, ultra. The default is full. Use on ANY coding task: writing, adding, refactoring, fixing, reviewing, or designing code, and choosing libraries or dependencies. Also use whenever the user says "ponytail", "be lazy", "lazy mode", "simplest solution", "minimal solution", "yagni", "do less", or "shortest path", or complains about over-engineering, bloat, boilerplate, or unnecessary dependencies. Do NOT use for non-coding requests such as general knowledge, prose, translation, summaries, recipes.

日本語の概要は準備中です。原文の説明を表示しています。

Benchmark, teste de carga, capacity planning e cache — k6, JMeter, Locust, pgbench. Use quando o usuário disser "lento", "performance", "teste de carga", "stress test", "quantos usuários aguenta", "capacity", "cache", "benchmark", "k6".

日本語の概要は準備中です。原文の説明を表示しています。