「production」の検索結果

MLOps and the production ML lifecycle -- model packaging and serving, CI/CD for ML, experiment tracking, model registries, reproducibility, production monitoring for data and concept drift, retraining pipelines, A/B and shadow deployment, and rollback. Covers batch vs online/real-time inference, REST endpoints, feature stores, data and version pinning, deterministic pipelines, performance-decay detection, and retraining triggers. Use when deploying models to production, serving predictions, monitoring for data or concept drift, setting up ML CI/CD, tracking experiments, managing a model registry, or planning retraining, shadow rollout, and rollback.

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 commit-R protocol: a test-driven rewrite of one compartment of Anabasis v2. Commit what is in flight, remove the compartment's tests in an "R:" commit, write them again from a stated hypothesis so the production code fails on purpose, remove the production files in a second "R:" commit, then write the production code again, rewrite the neighbouring functions the old shape bent, and delete what served only it. Use when the operator says "commit R", "commit-R protocol", "remove and rewrite the tests" or "rewrite from scratch", or asks for a test-first pass that looks for a faster, simpler path to the same outcome.

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。

Search Google Cloud (GKE) server logs of the PROD environment. Use when troubleshooting a production incident that needs the server side of the story — stream stalls, RPC peer changes / reconnects, watchdog closures, mesh/node churn, pod restarts — or when the user says "check PROD logs", "check production server logs", or gives a production timeline from a client console log.

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。

Register trained models in MLflow Model Registry with version control, implement stage transitions (Staging, Production, Archived) with approval workflows, and manage model lineage with comprehensive metadata and deployment tracking. Use when promoting a trained model from experimentation to production, managing multiple model versions across development stages, implementing approval workflows for governance, rolling back to previous versions, or auditing model changes for compliance.

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。

Publish NowStack Mobile production surfaces. Use for Convex prod, web deploy, production env sync, and coordinating iOS/Android release 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。

Audit film, television, and digital content production budgets for above-the-line (ATL) and below-the-line (BTL) cost accuracy, SAG-AFTRA/DGA/WGA/IATSE union rate compliance, fringe and payroll tax calculations, VFX bid variance analysis, post-production cost validation.

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。

Take a chosen visual to finished, production-ready state. Runs cleanup + refinement passes (fix artifacts, balance composition, color-grade, sharpen text, conform to spec/aspect) using image edit/regenerate. Use when the user says "polish this", "finalize", "clean up the image", "production-ready", "fix the artifacts", or has a selected visual that needs finishing. Final stage of the Creative Production set.

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。

Deploy a crouton app to Cloudflare Workers PRODUCTION (<app>.friendlyinter.net). A deliberate, human-initiated action — separate from the default staging /deploy skill. Use ONLY when explicitly asked to ship/release to production. Never run as part of routine work or an automated flow.

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。

Deploy a crouton app to Cloudflare Workers STAGING (auto-provisioning) — the DEFAULT deploy, staging only, never production. Handles the staging bootstrap (auto-creates D1+KV, syncs ids, migrates), wiring CI, routine staging deploys, and Pages→Workers migration. For production use the separate /deploy-production skill. Use when deploying any app in 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。

Use when the user says /deployprod, deploy prod, deploy production, upload production static hosting, or asks to deploy the current app to its production Convex static-hosting target.

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。

本番と同じ失敗を本番前に検出するためのテスト階層設計スキル。 DB方言差、実依存関係、永続化確認、timezone差、packaging整合性、 adversarial regressionをunit/integration/e2e/smoke/packagingに 適切に割り振り、PR CIで最低限の本番同等性を保証する。 Use when designing production-parity tests, closing test blind spots, building smoke suites for real dependencies, verifying persistence beyond UI success, or creating adversarial regression backlogs.

Knowledge base from the DoD Manufacturing Readiness Level (MRL) Deskbook (Version 2022, OSD ManTech / Joint Service–Industry MRL Working Group). Use for assessing and managing manufacturing maturity and producibility risk in defense acquisition: the 10-level MRL scale and its non-linear, target-not-grade nature; the five demonstration environments (laboratory → production-relevant → production-representative → pilot line → production line); the nine manufacturing-risk threads and sub-threads; how MRL targets map onto Adaptive Acquisition Framework milestones (MRL 4/6/7/8/9); conducting an MRL Assessment; building a Manufacturing Maturation Plan; putting MRL maturity into contract language (Section L/M, SOW, AS6500A, DIDs); tailoring criteria for S&T, single-system, ship, sustainment, and industry contexts; and screening operational-technology cybersecurity. Manufacturing-readiness focused — it measures producibility risk and frames mitigation; it is NOT a technology-readiness (TRL) guide, a full systems-engineering process model, a cost-estimating or schedule guide, or a deep cybersecurity audit method (see sibling packs for those).

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 the user says /deployprod, deploy prod, deploy production, upload production static hosting, or asks to deploy the current app to its production Convex static-hosting target.

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 training datasets for LLM specialization from production data, frontier model distillation, and synthetic bootstrapping. Use when formatting production logs into SFT data, distilling from frontier APIs, or preparing data for fine-tuning. Covers JSONL formatting, data quality validation, deduplication, and train/eval splitting.

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 a self-improvement loop for a shipped product's AI agents — production traces feed evals, evals feed improvement proposals (prompts, playbooks, retrieval configs), and a human approval gate promotes changes with rollback. Technique-agnostic: chooses per project between ACE-style evolving playbooks, GEPA/MIPROv2 offline optimization, or simple eval-driven iteration via references/techniques.md. Load when the user asks to make my product's agents self-improving, learn from production traces, add a learning loop, evolve prompts or playbooks safely, promote agent improvements, or GEPA-style optimization. NOT harness-evolution (that improves the coding agent), NOT experimentation (product A/B tests), NOT agent-run-retro (dev-phase manual retros — this skill is the production-scale continuation).

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 the user says /deployprod, deploy prod, deploy production, upload production static hosting, or asks to deploy the current app to its production Convex static-hosting target.

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。

Analyze and fix production errors from Sentry crash reports. Use when investigating AppHang, ANR, crashes, or production errors. Includes complete workflow from JSON analysis to bug fix implementation with evidence-based methodology. Triggers on sentry, crash, AppHang, ANR, error analysis, production error, bug analysis, crash report, freeze, hang, not responding, stacktrace, breadcrumbs, exception.

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 debugging methodology covering root cause analysis, log-based debugging, production debugging, memory leak diagnosis, deadlock detection, performance anomaly investigation, and reproducible bug reproduction. Distilled from real experience at Google, DeepMind, ByteDance, and Huawei. USE WHEN: investigating production incidents, debugging hard-to-reproduce bugs, analyzing crash dumps, finding race conditions, diagnosing memory leaks, debugging performance regressions, or any situation requiring systematic root cause analysis. Triggers on "debugging", "root cause", "bug", "crash", "segfault", "deadlock", "race condition", "memory leak".

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。

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

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 video production plans including shot lists, scripts, B-roll suggestions, music mood, thumbnail concepts, and platform-specific specs for reels, YouTube, and video ads.

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。

バグやテスト失敗、予期しない動作の原因を、再現手順やログ、直近の変更から調べます。仮説の検証から修正後のテストまで、根拠に沿って進めるスキルです。

• 失敗するテストの原因を調べたいとき
• 本番の不具合を追跡したいとき
• ビルドや連携の失敗箇所の調査

本番データベースの構造変更や既存データの移行を計画するスキル。列・索引の追加、段階的な切り替え、復旧手順を整理し、停止時間やロックに配慮した変更を支援します。

• 本番テーブルに列や索引を追加したいとき
• 大量データを分割更新したいとき
• 停止を避けて列を置き換えたいとき

Viteを使う開発で、設定ファイル、環境変数、APIへの接続、プラグイン、ビルド出力を整え、開発サーバーや依存関係の不具合調査を支援するスキル。

• 環境変数と開発用API転送の設定
• 開発サーバーの遅さや更新不良の調査
• 本番用ファイルの最適化と動作確認

Redisを使ったキャッシュ、アクセス回数制限、複数処理の同時実行制御、メッセージ配信を設計し、キーの有効期限や接続管理も見直すスキル。

• APIにキャッシュを追加したいとき
• 利用者別にアクセス回数を制限
• ワーカーのジョブ同時実行を防止