「dr」の検索結果

自动驾驶 (Autonomous Driving) Master OS — automated mastery of Autonomous Driving: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on Autonomous Driving problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「自动驾驶」「智能驾驶」「智驾」「辅助驾驶」「领航辅助」

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。

数据工程 — 数据平台从业者的认知操作系统, 覆盖把数据从源系统搬运成可靠 / 可查询 / 可信赖形态供分析 / ML / 数据产品消费的全生命周期 (生成 → 摄取 → 存储 → 转换 → 服务 + 安全/数据管理/DataOps/数据架构/编排/软件工程 六条暗流, Reis & Housley 框架): 摄取与集成 (批 + CDC 变更数据捕获 Debezium + EL 工具 Fivetran/Airbyte/Meltano/dlt + Kafka Connect + schema drift) / 存储与文件表格式 (对象存储数据湖 + 列存 Parquet/ORC/Arrow/Avro + 开放表格式 Apache Iceberg/Delta Lake/Apache Hudi + lakehouse + 分区/compaction) / 转换与建模 (ELT dbt/SQLMesh + Spark + 维度建模 Kimball + Inmon + Data Vault + 大宽表 OBT + 渐变维 SCD + 增量模型 + 语义/指标层) / 编排与工作流 (Apache Airflow/Dagster/Prefect/Mage/Kestra/Apache DolphinScheduler + DAG + 幂等 + 回填 backfill + 数据资产调度) / 批流与实时 (Apache Kafka/Apache Flink/Spark Structured Streaming/Kinesis/Pulsar/Redpanda + Lambda vs Kappa + watermark/窗口/exactly-once + 流式 SQL Materialize/RisingWave + 实时 OLAP ClickHouse/Apache Druid/Apache Pinot/StarRocks/Apache Doris) / 数仓与查询引擎 (Snowflake/BigQuery/Redshift/Databricks SQL/Trino/Presto/DuckDB/Polars + 存算分离 + MPP) / 数据质量测试与可观测性 (dbt tests/Great Expectations/Soda + 数据契约 + Monte Carlo data downtime + 新鲜度/量/schema 异常检测) / 数据治理编目与血缘 (DataHub/Amundsen/OpenMetadata/Unity Catalog + 列级血缘 + PII 分类 + 访问控制 + GDPR) / DataOps 与可靠性 (数据 CI/CD + 转换版本控制 + 环境隔离 + 幂等重处理 + 数据 SLA/SLO + 计算存储 FinOps) / 数据架构范式 (现代数据栈 + lakehouse + data mesh + data fabric + 去中心化 vs 中心化所有权) / 分析工程角色 (dbt 时代连接数据工程与分析的桥) — 不含 数据科学/ML 建模本身 (是下游消费者) / BI 仪表盘制作 (serving 下游) / 数据分析报表为终点 / 'data engineer = 跑 Hadoop 的' 过时窄化 / 通用后端应用开发 (平行学科) (Data Engineering — the cognitive operating system of practitioners who design, build, and operate the data platform: moving data from source systems into reliable, queryable, trustworthy form for analytics / ML / products, covering (a) the data engineering lifecycle (generation → ingestion → storage → transformation → serving, with the undercurrents security / data management / DataOps / data architecture / orchestration / software engineering — Reis & Housley framing), (b) ingestion & integration (batch + CDC change-data-capture with Debezium, EL tools Fivetran / Airbyte / Meltano / dlt, Kafka Connect, API + file + database sources, schema drift handling), (c) storage & file/table formats (object storage data lakes, columnar formats Parquet / ORC / Arrow / Avro, open table formats Apache Iceberg / Delta Lake / Apache Hudi, lakehouse architecture, partitioning / compaction / Z-ordering), (d) transformation & modeling (ELT with dbt / SQLMesh, Spark, dimensional modeling Kimball, Inmon CIF, Data Vault, One Big Table / wide tables, normalization vs denormalization, slowly changing dimensions, incremental models, the semantic / metrics layer), (e) orchestration & workflow (Apache Airflow, Dagster, Prefect, Mage, Kestra, Apache DolphinScheduler, DAGs, idempotency, backfills, data-aware / asset-based scheduling), (f) batch vs streaming & real-time (Apache Kafka, Apache Flink, Spark Structured Streaming, Kinesis / Pulsar / Redpanda, the Lambda vs Kappa debate, watermarks / windowing / exactly-once, streaming SQL Materialize / RisingWave, real-time OLAP ClickHouse / Apache Druid / Apache Pinot / StarRocks / Apache Doris), (g) warehouses & query engines (Snowflake, BigQuery, Redshift, Databricks SQL, Trino / Presto, DuckDB, Polars, decoupled storage & compute, MPP), (h) data quality, testing & observability (dbt tests, Great Expectations, Soda, data contracts, Monte Carlo / data downtime, freshness / volume / schema anomaly detection, unit / integration testing of pipelines), (i) data governance, catalog & lineage (DataHub, Amundsen, OpenMetadata, Unity Catalog, column-level lineage, PII / data classification, access control, GDPR / data privacy), (j) DataOps & reliability (CI/CD for data, version control of transformations, environments, idempotent reprocessing, SLAs / SLOs for data, cost / FinOps for compute & storage), (k) data architecture paradigms (modern data stack, data lakehouse, data mesh, data fabric, decentralized vs centralized ownership), (l) the analytics engineering role (the dbt-era bridge between data engineering and analysis); N

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。

Runs a long swarm of hundreds to thousands of cheap headless coding workers that drive a large failing-test backlog to green. Failures are clustered by signature, each worker fixes one cluster in its own git worktree, a serial no-regression merge gate lands the fixes, the loop resumes across restarts for hours, and it federates across sibling sessions over git branches when one container's CPU is not enough. Use for "fix these thousands of failing tests with a Haiku swarm", "drive the test suite to green with hundreds of agents", "build this compiler or interpreter against a conformance suite", "launch, relaunch or check in on the swarm loop", "the swarm is stuck, idle or rejecting everything", "parallel agents fixing tests keep hitting merge conflicts and regressions", "add sibling containers to the swarm", or "compare models on the tail the swarm could not fix". Ships the harness, a per-project TOML config, a fake-worker selftest, check-in signals and the 2026-10-12 haiku-cc failure catalogue.

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。

Draws images, PDF/SVG vector files and animation frames with pycairo, and supplies what cairo lacks: shaped text for Arabic, Indic and other scripts, text along curves, blur/glow/drop shadows, path booleans, stroke-to-outline, and Gouraud-shaded 3D. Use when writing pycairo or cairo drawing code, generative art, custom diagrams or posters rendered in Python, or when cairo output needs non-Latin text, soft effects, merged shapes or shaded surfaces.

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。

Load at the start of any task whose deliverable is prose another person will read — a PR description, commit message, README, doc, postmortem, blog post, issue or review comment, report, release note or essay — before drafting it, whether the result is pushed, posted, published or saved to a file, without being asked. Draft, then run this pass on the draft before handing it over. Also use when someone says "de-claude", "de-slop", "humanize this", "this reads like AI", "make it sound human", or asks for a voice, tone or register edit. Rewrites the constructions that mark prose as model-written (staged reveals, verdict headers, aphoristic closers, "it's not X, it's Y", em-dash drama, forced triads, flat-certainty adverbs) into plain technical prose and checks the rewrite kept every claim. Not for fiction, poetry, code, or quoted text; for a full adversarial review of a deliverable use challenging.

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。

Inspect ADR health read-only; optionally select one ADR with --adr N.

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。

TRIGGER when: user asks to draw, illustrate, or visualize any process, structure, or concept — "画个流程图", "draw an architecture diagram", "帮我画个对比图", "visualize this process", "make a diagram for this doc", or mentions 流程图/架构图/示意图/对比图/时序图/泳道图/时间线. Also trigger when user provides a document/article and asks for illustrations or diagrams. DO NOT trigger for: chart/plot requests (bar charts, line graphs — those need a charting library, not draw.io), or artistic image requests (logos, mascots, drawings of objects/people — those need an image tool).

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。

Draft a reply or a value-add quote tweet for a specific X (Twitter) tweet from its URL. Use to reply in a thread, answer a creator, or quote-tweet with added value. Parses the tweet URL, reads pasted context, decides reply vs quote tweet, and drafts 1-3 variants in your voice. X has no LinkedIn-style 2-level thread flattening, so a reply is just a tweet. Publora has no reply endpoint, so the draft is returned as a copy-paste block to post yourself. Not for top-level posts (use x-post-writer).

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。

Plan, draft, audit, and publish posts and threads for X (Twitter). Use when the user wants to write a single tweet or an auto-numbered thread, build a long-form tweetstorm, remove AI tells from a draft, reverse-engineer the hook from a viral tweet, draft a reply or quote tweet, or plan a week of X content. Tweets and threads publish via the Publora API, which auto-splits long content into a numbered thread. User provides notes or a tweet URL, the skill drafts, the user approves, then it publishes.

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 a newly mined VoiceDrop article needs automated post-processing — invoked headless by the 5-minute poller (launchd com.jianshuo.voicedrop-postprocess) with the article stem as argument. Triggers — "/wjs-voicedrop-post-processing <stem>".

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。

Generate professional diagrams as valid draw.io XML — flowcharts, architecture, C4 models, ER diagrams, sequence diagrams, mind maps, and swimlanes. Don't use for Excalidraw or Mermaid output, hand-drawn sketch styles, or slide decks/presentations.

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。

Suede Labs Android app factory: plan, build, test, and release a production-grade native Android app from a product idea through Google Play. Use for requests to create, ship, submit, monetize, or modernize an Android app. Covers Kotlin and Jetpack Compose architecture, API-level policy verification, accessibility, privacy and Data Safety, Play Billing, Play Integrity, account deletion, testing, performance, store assets, signing, staged rollout, and a release evidence gate. NOT FOR: iOS work (private Suede Labs companion, not in this pack: ios-app-factory), a review-only pass on an existing app (use suede-code-review), or live Play listing and keyword audits on a shipped app (use suede-aso).

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。

Manage Google Workspace via the gws CLI — Drive, Gmail, Sheets, Docs, Slides, People, Chat, Meet, Forms, and cross-service workflows. Use when asked to send email, read/write spreadsheets, upload files to Drive, manage contacts, create presentations, send Chat messages, create docs, or any Google Workspace operation. Also triggers on "gws", "Google Drive", "Google Sheets", "Gmail", "Google Docs", "Google Slides", "Google Chat", "Google Meet", "Google Forms", "check my inbox", "upload to Drive", "read spreadsheet", "send an email", "create a doc", "send a chat message", "create a presentation".

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 and debug SceneKit scenes where one 3D object (a product, badge, coin, wheel) performs on a transparent stage inside a SwiftUI app - studio lighting, real shadows, baked keyframe choreography, hand-rolled physics, gestures and haptics, multi-scene sequencing. Use when working with SCNView or SceneView in SwiftUI, UIViewRepresentable 3D scenes, product or hero-object animation, roll or spin entrances, a first-frame hitch when a scene appears, shadows missing or wrong, metal rendering black, choreographed 3D motion that must stay interruptible, a continuous vapor stream (vent air, steam, mist) drawn as a shader-driven sheet, a liquid-metal or jelly blob (noise-deformed surface with mirror reflections and tap ripples), chrome or gold that looks cartoon-flat instead of real, HDRI image-based lighting, free trackball rotation of an actor, deterministic screenshot testing of shader-driven scenes, cutting an actor in two with a finger swipe (runtime mesh slicing with sealed cross-sections and a falling piece), a die-struck relief object (badge, coin, medallion) built from a heightfield with multiple finishes on one mesh, an actor crumbling into debris that falls and rests on the floor, reproducing a real object's motion from photos or video, or keyframed motion that stutters at its own keyframes. Not for RealityKit, ARKit, visionOS, or full game worlds.

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。

Draft a Guardrails PR from its complete final diff and carry out authorized CI and review follow-up, including stacked PRs and takeovers.

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。

Light Blender scenes professionally — three-point setups, HDRI environments, studio/cinematic/dramatic configurations, light groups, color temperature, soft vs hard shadows. Use whenever the user asks to "light the scene", "set up lighting", "make it look cinematic / dramatic / studio / outdoor / sunset", "add a key light", "use HDRI", or any lighting-related request. Make sure to use this skill even if the user does not say "light" — also covers "make it look professional", "studio shot", "moody atmosphere", "golden hour", "rim light". Pairs with blender-materials (lighting reveals materials) and blender-cameras (lighting + composition together = shot).

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。

Resolve TikTok video URLs to return no-watermark/watermarked download addresses, playback addresses, and cover image addresses for saving promotional video assets or offline analysis. Trigger when users mention TikTok video download, TikTok watermark-free download, TikTok video save, download TikTok promotional video, TikTok no watermark video, TikTok video download, download TikTok video, no watermark TikTok video, save TikTok video, TikTok video link resolution. Even if the user does not explicitly mention "Nexscope", trigger this skill whenever their need involves extracting downloadable/playable video addresses from a TikTok video link.

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。

教師なし分析と EDA 前処理（欠測 / 欠損 / NaN / 補完 / imputation / 外れ値処理、クラスタリング / セグメンテーション / k-means / 階層クラスタリング / GMM / DBSCAN、次元削減 / PCA / 主成分分析 / 因子分析 / UMAP / t-SNE、異常検知 / anomaly detection / 不正検知）を実行したら必ずセットで出す図と値のルーター。fillna, dropna, fill_null, SimpleImputer, IterativeImputer, missingno, KMeans, AgglomerativeClustering, DBSCAN, GaussianMixture, silhouette_score, dendrogram, PCA, FactorAnalysis, calculate_kmo, TSNE, umap.UMAP, IsolationForest, LocalOutlierFactor, pyod がコードに現れたとき、またはユーザーが「欠損を埋めて」「クラスタリングして」「主成分で 要約して」「異常を検知して」「データをきれいにして」と言ったときに使う。安定性・k の根拠・平行分析・ベースラインに 言及がなくても適用する。SKILL.md のルーティング表で手法を特定し、対応する references/<手法>.md を読んでから実行する。 教師あり予測は predictive-modeling-diagnostics、回帰・検定は statistical-inference-diagnostics を使う。

Use when users request Android competitor research, systematic feature or interface mapping, evidence screenshots, interaction coverage, comparison-ready product dossiers, or Android system-boundary verification without APK decompilation.

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。

Detects scope creep by quantifying drift percentage. Auto-triggered by L1 orchestrators when files exceed the original plan. Compares git changes against plan, classifies drift into 4 tiers: ON_TRACK, MINOR_DRIFT, SIGNIFICANT_DRIFT, OUT_OF_CONTROL.

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。

Drive one AI agent from another through kolu's terminals: spawn a Claude Code / Codex / opencode session in a PTY, prompt it, watch the screen, read the reply, prompt again. PRIMARY PATH: the kolu MCP tools (`lifecycle_create`/`lifecycle_sendInput`, `wait_outputSettled`/ `wait_agentState`, `screen_text`, the `terminals` resource). FALLBACK: the `kolu` CLI — same verbs, same discipline. Triggers on "drive another agent", "agent drives agent", "make Claude drive Codex", or wiring a loop where one coding agent supervises another.

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。

字面の同一性ではなく意図（目的）の同一性に基づいてコードの共通化を判断するスキル。 DRY原則の誤適用（字面が同じだが意図が異なるコードの共通化）を検出し、正しい共通化判定を 支援する。コードレビュー、リファクタリング、新規実装時に重複コードの扱いを判断する場合に使用。 対象言語: 言語非依存（Rust, Java, TypeScript, Go, Python, Kotlin, Scala等すべて）。 トリガー：「重複コードを共通化したい」「DRYに従いたい」「似たコードがある」 「この2つの関数をまとめたい」「コードの重複を減らしたい」「共通化すべきか」 「リファクタリングで共通関数にしたい」といった重複コード・共通化関連リクエストで起動。

Audit guardrail coverage — bypass vectors, false positive rates, policy gap analysis, red-team scenarios. Use when asked to "audit our AI guardrails", "can our filters be bypassed", or "check guardrail false positives".

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 drift alerts and escalation — thresholds, runbooks, and retrain triggers. Use when asked to "alert on model drift", "when should we retrain", or "write a drift escalation runbook".

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。