「dsl」の検索結果

Kotlinのコード作成・レビュー・改修を支援します。nullの扱い、不変データ、非同期処理、型安全な設定記述を見直し、読みやすく保守しやすい構成に整えます。

• Kotlinのnull処理を点検したいとき
• 可変データや深い入れ子を整理したいとき
• コルーチンとFlowの非同期処理設計

Design a generative recipe DSL so agents emit small validated JSON documents and ONE audited interpreter renders them all — data, not code. Covers the N-artifacts threshold for choosing a DSL over bespoke code, designing roles as style slots so a single recipe renders unchanged in every output style, the validator gate (compile + bounds + real render in all styles, plus a --fast mode for corpus-wide sweeps), hand-authored seed recipes, and the emergent conventions that keep a corpus of hundreds consistent without per-item steering. Use when many similar artifacts (icons, status badges, diagram nodes, test fixtures) must be generated by agents, when per-item bespoke code is multiplying unreviewable code paths, or when a whole corpus must be re-rendered cheaply into new styles.

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。

Kotlinでデータベースの検索・更新処理を実装するためのパターン集です。JetBrains Exposedのクエリから接続管理、構造変更、テストまで扱います。

• データベースの検索・更新を実装したいとき
• データベース接続の再利用を設定したいとき
• データベース構造の変更管理

Sync the local LikeC4 syntax guide with the upstream LikeC4 skills repository (github.com/likec4/likec4/tree/main/skills/likec4-dsl). Use when the user asks to update, sync, or check the LikeC4 syntax guide, when upstream LikeC4 DSL changes are suspected, or when mentioning likec4 syntax sync, DSL drift, or syntax guide update.

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。

Authoring and managing Agentforce agent definitions using the declarative Agent Script DSL (.agent files) and associated metadata types, including the Agent Script language for the new Agentforce Builder (hybrid-reasoning -> logic vs | prompt instructions, @actions/@subagent/@variables references, subagents). Use when creating agents in source control, writing or debugging Agent Script, debugging agent metadata, or understanding the metadata lifecycle of GenAiPlugin/GenAiPlanner/BotVersion types. Triggers: 'how do I deploy an Agentforce agent using source control', 'convert an agent topic to a subagent', 'what metadata types make up an Agentforce agent', 'agent test run command failing in CI pipeline', 'GenAiPlugin vs GenAiPlanner metadata relationship', 'Agent Script logic vs prompt instructions'. NOT for Apex-based agent actions — use agentforce/custom-agent-actions-apex. NOT for clicking the agent together in the Builder UI — use agentforce/agentforce-agent-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。

Authoring and managing Agentforce agent definitions using the declarative Agent Script DSL (.agent files) and associated metadata types. Use when creating agents in source control, debugging agent metadata, or understanding the metadata lifecycle of GenAiPlugin/GenAiPlanner/BotVersion types. Triggers: 'how do I deploy an Agentforce agent using source control', 'what metadata types make up an Agentforce agent', 'agent test run command failing in CI pipeline', 'GenAiPlugin vs GenAiPlanner metadata relationship'. NOT for Apex-based agent actions (use custom-agent-actions-apex). NOT for UI-based agent creation (use agentforce-agent-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。

Reverse JavaScript-based custom DSL/VM interpreters, non-standard WASM-like runtimes, and risk-control engines. Use when analyzing IIFE or switch-based opcode dispatchers, extracting instruction tables, recovering bytecode semantics, capturing VM state at runtime, or reconstructing execution 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。

The complete slide DSL manual — the canvas and its background, the ten element types field by field with units, defaults and legal values, where each type keeps its words, which field the renderer actually paints when two disagree, exactly what happens to the HTML you put in `content`, and the structure schema that is now the ONLY thing a write is checked against. Nothing filters your markup and nothing normalises your values, so this manual is the whole of what makes a page render correctly. Load it before patching a field you have not patched before, when a patch comes back rejected, when a patch lands but nothing moves on screen, or when you need to know where a colour, a font, a table cell or a code line really lives. This is a field reference and not a procedure — `page-clone` and `pro-editing` decide which pages to touch, `slide-craft` decides what a good page looks like, and this one says what the JSON means.

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 map for reading and editing an OpenMAIC stage document with read_stage, patch_stage, and grep_stage. Load it before patching a structure you have not patched before, when patch_stage rejects an operation, or whenever the path from a stage, outline, scene, content object, or action to the field you need is uncertain. It routes to field-level references for quizzes, interactive widgets, actions, and PBL projects; the installed slide-dsl skill remains the complete slide canvas manual.

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。

Provides idiomatic Kotlin implementation patterns including coroutine concurrency, Flow stream handling, multiplatform architecture, Compose UI construction, Ktor server setup, and type-safe DSL design. Use when building Kotlin applications requiring coroutines, multiplatform development, or Android with Compose. Invoke for Flow API, KMP projects, Ktor servers, DSL design, sealed classes, suspend function, Android Kotlin, Kotlin Multiplatform.

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。

Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step Stata pipeline an applied economist runs on every paper — (1) data import & cleaning (use/import, destring, misstable, duplicates, merge assert), (2) variable construction (gen/egen/winsor2/xtile/xtset with L./F./D.), (3) descriptive statistics & Table 1 (tabstat/balancetable/asdoc), (4) classical diagnostic tests (sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/hausman/estat overid), (5) baseline modeling (reg/xtreg/reghdfe/ivreg2/ivregress/csdid/did_imputation/eventstudyinteract/sdid/rdrobust/synth/psmatch2/teffects/heckman/qreg/ppmlhdfe), (6) robustness battery (bacondecomp/honestdid/rwolf/ritest/wildbootstrap/oster), (7) further analysis (subgroup/triple-diff/interactions/medsem/marginsplot/binscatter by group), (8) publication-ready tables & figures (esttab/outreg2/estout/coefplot/marginsplot/rdplot/twoway combined). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `teffects ipw` / `teffects ipwra` / `teffects aipw` / `eltmle`, Mendelian randomization via `mrrobust` (IVW / Egger / weighted median) and `mregger` / `mrpresso`, KM / Cox / AFT / RMST survival via `sts` / `stcox` / `streg` / `strmst2`, E-value sensitivity via `evalue` (Linden-Mathur), principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `ddml` / `pdslasso`, S/T/X/R/DR meta-learners via `crforest` and `ddml interactive`, causal forest via `crforest` / `cforest`, BART/BCF via `bart` / `bartCause`-style externals, CATE distribution + policy tree via `crforest`, off-policy evaluation, conformal causal externals, fairness audit, DAG learning via `pcalg` / external Python callouts). Use when the user asks for a complete Stata empirical analysis, wants a reproducible .do-file pipeline, needs a Stata counterpart to the Python StatsPAI / Full-empirical-analysis-skill, or names a specific Stata step in isolation ("run reghdfe with two-way clustering", "csdid event study", "winsor2 at 1%", "esttab to LaTeX", "coefplot with CI", "ivreg2 weak-IV test", "synth_runner placebos", "teffects psmatch balance check"). Mode A triggers on "target trial emulation Stata", "teffects ipw aipw", "eltmle", "mrrobust", "mregger weighted median", "stcox AFT survival", "strmst2", "evalue Stata", "STROBE Stata", "公共健康 Stata", "流行病学 Stata". Mode B triggers on "ddml Stata", "pdslasso", "crforest causal forest Stata", "policy tree Stata", "因果机器学习 Stata".

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。

Guide for creating and writing Expo native modules and views using the Expo Modules API (Swift, Kotlin, TypeScript). Covers module definition DSL, native views, shared objects, config plugins, lifecycle hooks, autolinking, and type system. Use when building or modifying native modules for Expo. Not for migrating an existing Swift module from the definition DSL to the Expo Modules API 2.0 macros; use expo-migrate-module (from the expo-experiments plugin) for that.

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。

DSL kernels, callables, structs, buffers, atomics, control flow, and dispatch.

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。

Reverse JavaScript-based custom DSL/VM interpreters, non-standard WASM-like runtimes, and risk-control engines. Use when analyzing IIFE or switch-based opcode dispatchers, extracting instruction tables, recovering bytecode semantics, capturing VM state at runtime, or reconstructing execution 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。

Implement 10 advanced AI agent node types for any DSL/runtime system — plan compiler, code interpreter, critique loops, intelligent routing, multi-agent ensemble, persistent memory, external API tools, iterative loops, data transforms, and parallel execution. Use this skill whenever the user wants to add agent capabilities to a workflow engine, build an AI orchestration framework, implement Karpathy-style AI upgrades, add new node types to a DSL runtime, or create autonomous agent pipelines. Also trigger when the user mentions AetherLang, flow engines, node types, agent frameworks, or wants to upgrade their AI system with capabilities like self-programming, reflection, routing, ensemble, memory, or tool use.

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 en Elasticsearch (indexing, mappings, aggregations, Kibana, DSL queries)

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。

test ツールと CircuitTest Python DSL の書き方。test を実行する前に start_session でセッションを開始し、その session_id を指定する。回路の自動テストを作成・実行する前に必ず読む。

過去の成功知見から抽出したレッドストーン回路設計の普遍トラップ集。回路設計・テストコード記述前に必ず読むこと。フレームワーク/DSLの誤用で試行回数を枯渇させるのを防ぐ。

Authors reproducible Nextflow DSL2 pipelines built on reactive dataflow, where processes communicate only through channels and execution order is not guaranteed. Use when deciding channel/dataflow (Nextflow) vs rule-based (Snakemake) authoring; wiring queue vs value channels and fixing shared-reference exhaustion with .first(); composing DSL2 modules and subworkflows with take/main/emit; selecting container/conda profiles and pinning images by digest for portability across local/SLURM/LSF/AWS Batch/Google Batch/Kubernetes executors; diagnosing why -resume misses the cache (nondeterministic input order, mtime on network filesystems, mutable :latest tags) with cache 'lenient' and -dump-hashes; managing work/ vs publishDir and dynamic retry escalation; and choosing whether to adopt an nf-core community pipeline or author from scratch.

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。

NVIDIA CUDA GPU 並列コンピューティングリファレンス。 CUDA C++ / CUDA Python, kernel, nvcc, Unified Memory, CUDA Graphs, Cooperative Groups, Driver API, マルチ GPU。 PTX ISA 命令セット, state space, MMA 命令。 Blackwell チューニング, Streaming Multiprocessor, NVLink。 CUTLASS / CuTe DSL / CuTe C++ GEMM, Tensor Core, tcgen05, Operator API, nsight-compute, compute-sanitizer。

Android アプリ開発 (Kotlin) の Gradle ビルドシステムリファレンス。 Android Gradle Plugin (AGP), Kotlin DSL (build.gradle.kts), version catalog, KSP, R8/ProGuard, App Bundle, baseline profile。

コルーチン、null安全、DSLビルダーを活用した、堅牢で効率的かつ保守可能なKotlinアプリケーション構築のためのイディオマティックなKotlinパターン、ベストプラクティス、規約。

Ktorサーバーパターン — ルーティングDSL、プラグイン、認証、Koin DI、kotlinx.serialization、WebSocket、testApplicationテスト。

JetBrains Exposed ORMパターン — DSLクエリ、DAOパターン、トランザクション、HikariCPコネクションプーリング、Flywayマイグレーション、リポジトリパターン。