「ngs」の検索結果

Daily AI music competition — create MIDI songs, vote, and compete for rankings.

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。

Expert-level concrete design covering reinforced concrete beams, columns, slabs, footings, retaining walls, ACI 318 code provisions, and durability.

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。

Expert-level bioinformatics covering sequence analysis, structural bioinformatics, database searching, phylogenetic analysis, NGS data processing, single-cell analysis, and machine learning in biology.

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。

Use when opening edited files in the user's IDE. Reads settings from .agent-settings.yml to determine IDE and whether auto-open is enabled.

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。

[Code Quality] Use when you need to combine /why-review + /fix in a recursive loop — each round runs /why-review to find validated findings then /fix to resolve them, then loops again over the CHANGED target until a fresh full /why-review produces zero findings (nothing left to fix).

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。

[Code Quality] Use when you need to combine /changes-review + /fix in a recursive loop — each round runs /changes-review (report-only) to surface validated findings over a fixed diff scope, then /fix to resolve them, then loops again with a FRESH full /changes-review over the CHANGED diff until one complete pass produces zero validated findings (nothing left to fix).

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。

Use when running a jevaluate rating round, re-rating after a rubric change, changing the jevaluate rubric, or publishing ratings to the jev-mode ratings folder.

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。

Padrões de Retrieval-Augmented Generation incluindo chunking, embeddings, vector stores e otimização de recuperação. Use quando: rag, retrieval augmented, vector search, embeddings, semantic search.

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。

Plataforma de observabilidade para LLM com rastreamento, avaliação e monitoramento. Use ao depurar aplicações LLM, avaliar saídas de modelos em datasets, monitorar sistemas em produção ou construir pipelines de testes sistemáticos para aplicações de IA.

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。

Operações de LLM — RAG, embeddings, bancos de dados vetoriais, fine-tuning, prompt engineering avançado, custos de LLM, avaliações de qualidade e arquiteturas de IA para produção.

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。

Estenda janelas de contexto de modelos transformer usando RoPE, YaRN, ALiBi e técnicas de interpolação de posição. Use ao processar documentos longos (32k-128k+ tokens), estender modelos pré-treinados além dos limites de contexto originais ou implementar codificações posicionais eficientes. Cobre embeddings rotativos, vieses de atenção, métodos de interpolação e estratégias de extrapolação para LLMs.

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。

Use when auditing UI code for interface-guideline compliance (accessibility, forms, animation, images, performance, UX copy) and/or validating a deployed page against Lighthouse 95+ targets. Produces evidence-based findings (file:line + Lighthouse JSON) and turns confirmed pendencies into Draft SPECs via write-specs. Do NOT use to design/build UI from scratch (use design) or to fix findings inline — fixes go through the SPEC pipeline.

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。

Use Lingzao creator-content tools for Xiaohongshu/XHS, Douyin, and WeChat official-account public content. Lingzao supports XHS SEO, Xiaohongshu account analysis, benchmark account discovery, viral note breakdown, keyword research, one-stop content packages from keyword/link/image/inspiration material, title and cover optimization, hand-drawn route-map cards, city food maps, travel itinerary maps, life checklist maps, workflow manual maps, creator content workflow, note search, creator search, profile lookup, recent posts, deep profile copy/subtitle analysis, note/article detail, post comments, article stats, related articles, short-video copy extraction, and prompt-based creator image generation.

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。

Run Ruff checks with optional scope and rule overrides, apply safe and unsafe autofixes iteratively, review each change, and resolve remaining findings with targeted edits or user decisions. Use when cleaning up Ruff lint findings across a Python codebase.

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。

Enforce output quality, evidence verification, and quality gates across security audits. Use this skill when you perform SAST, SCA, threat modeling, or security code reviews. Use this skill to verify code locations and taint traces for all findings. Use this skill to prevent the suppression of valid security findings. Use this skill to evaluate reports against quality thresholds (score ≥ 8/10) before delivery.

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。

Ranks any list of impediments and their countermeasures using a value-stream scoring model (ROI, Cost to Implement, Ease of Deployment, Risk Factor) and a fixed prioritization formula. Use when someone asks to prioritize, rank, sequence, or triage impediments, countermeasures, remediation items, risks, findings, gaps, action items, or backlog entries; or mentions value-stream prioritization, A3 / lean countermeasure ranking, ROI vs. effort scoring, or building a remediation / improvement backlog. Works with GHQR findings, audit results, retrospective action items, risk registers, architecture review gaps, or any free-form `{impediment, countermeasure}` list.

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。

Compress review pressure into the right next action instead of more code by default. Classify findings, reject non-liabilities, and choose proof-only vs minimal-fix vs refactor-kernel, preventing one-patch-per-comment churn. Use after a code review — PR comments, reviewer suggestions, or audit findings — and especially when you are about to reflexively patch every comment.

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。

Save the latest learning as a numbered markdown file under ~/.claude/learnings and update the learnings index with title and tags. Use when the user says "save", asks to record a learning, or wants the current insight stored for later.

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。

The crouton mental model for someone who has never seen this repo — what "layer" means (two different things), the two-halves rule, the three app.config registries, team-scoped multi-tenancy, manifest-driven field types, the numbered invariants with the incidents behind them, and the known-weak points. Use when orienting in this codebase for the first time, when a question is shaped like "how does crouton work / why is it built this way / where does X live", when deciding whether a change belongs in a package or a generated layer, when confused by an extends chain or by croutonApps/croutonCollections/croutonLayoutBlocks, or before proposing any structural change. Trigger phrases: "what is a crouton layer", "package vs generated layer", "how does multi-tenancy work here", "why is there a stub component", "where do field types come from", "what invariants must hold".

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。

Adversarially probe this monorepo for security flaws at the right depth. Steers the red-team subagent — picks quick/standard/deep from the request, spawns it (fanning out for a wide deep sweep), collates findings into a dated report, and files security/sec:* issues for confirmed high/critical findings. Use when asked to "red-team", "try to hack this", "find security holes", "pentest this package/app", or as the brain behind the CI + daily security runs.

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。

exact-13 staging planを独立評価したいとき、4条件とdigestに束縛したplan-findings.jsonを生成したいときに使う。

生成済みプロンプトを C1-C4 と 4 パスで評価したいとき、context:fork で独立評価結果 findings.json を取得したいときに使う。

生成済み plan を4条件と決定論ゲートで評価したいとき、context:fork で独立評価結果 plan-findings.json を取得したいときに使う。

Cloudflare Workersのコードを本番運用品質のベストプラクティスに照らして作成・レビューする。新しいWorkerの実装、Workerコードのレビュー、wrangler.jsoncの設定、streaming・floating promises・global state・secrets・bindings・observabilityなどのanti-pattern確認で使用する。事前学習の記憶よりCloudflare公式ドキュメントからの取得を優先する。