「intent」の検索結果

Use when [trigger symptoms, user intents, task contexts]. Include Chinese trigger phrases when useful. Do not summarize workflow here.

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。

Create editable charts, diagrams, figures, posters, or slides from intent, data, or references.

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 editing Chinese web fiction, narrative prose, or story drafts that sound AI-generated, overly polished, formulaic, explanatory, or too symmetrical; use to detect AI-writing fingerprints and revise only wording while preserving plot, facts, characters, names, and intent.

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。

GStack spec method for turning fuzzy intent into an executable spec with goals, non-goals, acceptance criteria, risks, and downstream role boundaries.

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。

Smart, granular git commits following monorepo conventions. Analyzes changes, filters to session-relevant files, groups by intent, and uses conventional commit format. Use when committing code changes.

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。

First-principles strategy — strip away conventional assumptions and find the theoretical maximum for your business. Three modes. Diagnostic mode runs a full session (45+ min) to define your theoretical maximum, map the gap honestly, classify constraints, invert for failure modes, set strategic intent, and work backwards to your first intervention. Review mode (15-20 min) checks whether your theoretical maximum has shifted, whether the gap has changed, and whether you're still climbing the right peak. Alert mode defines constraint-shift, peak-shift, and local-peak triggers that tell you when to reassess. Use when someone is stuck optimising incrementally, when conventional wisdom is driving strategy, when a new technology or regulation changes what's physically possible, or when you suspect the business is climbing a local peak. Integrates with company-context.md. Based on practice by Eterdis (eterdis.com).

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 after intent is known to route build, fix, review, debug, docs, or Scrum requests into ordered workflow steps with exit criteria.

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。

Catalog and selection guide for Exasol tools, extensions, connectors, integrations, and architecture patterns. Use when the primary intent is comparison, discovery, support-status research, or architecture selection rather than executing a workflow; hand operational work to the selected dedicated skill. Covers deploy, load, explore, enrich, surface, and scale capability families, including named products with no dedicated skill of their own such as Lakehouse Turbo, the Exasol MCP Server, Governed SQL / Text-to-SQL MCP Server, Agent Control Plane, Extension Manager, Terraform and Ansible tooling, and BI, ETL, and warehouse integrations such as Databricks, SAP, Kafka, Power BI, and Tableau.

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。

Exasol database interaction via the exapump CLI and Exasol SQL. Covers SQL queries and DML/DDL such as `SELECT`, `INSERT`, `UPDATE`, `DELETE`, `MERGE`, `CREATE TABLE`, `ALTER TABLE`, and `DROP TABLE`, `CREATE CONNECTION` connection objects without import or export intent, schema inspection, table design, `exapump sql` and `exapump profile`, query profiling, analytics, and Exasol-specific SQL behavior outside exasol-import and exasol-export.

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。

Premium brand-kit image generation skill for creating high-end brand-guidelines boards, logo systems, identity decks, and visual-world presentations. Trained for minimalist, cinematic, editorial, dark-tech, luxury, cultural, security, gaming, developer-tool, and consumer-app brand systems. Optimized for intentional logo concepting, refined composition, sparse typography, strong symbolic meaning, premium mockups, art-directed imagery, and flexible grid layouts.

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 Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.

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 natural-language crypto/DeFi agents and EVM MCP plugins (Claude Code, Cursor, Codex, Gemini). Aomi turns prompts into wallet-signed txs on Ethereum, Base, Arbitrum, Optimism, Polygon, Linea — non-custodial, fork-simulated. 40+ apps: Uniswap, Aave, Lido, Morpho, GMX, Hyperliquid, Polymarket.

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。

执行显式、编码前的 Product Discovery，把早期或模糊的软件想法转化为用户确认的 Intent、丰富方案卡片、对比矩阵、可编辑画布和可持续 D0 文档基线。仅当用户明确调用 $flight-brainstorm，准备在 Flight Control Run 之前探索空白、稀疏、不确定或方向发生重大变化的项目时使用；禁止因模糊请求或普通开发工作隐式触发。

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。

Author and submit a Narrative workflow from a natural-language intent. Picks the closest example from `assets/examples/`, adapts it to the user's case, walks the YAML against the spec, resolves the data plane, and submits via `narrative_workflows_create` only after the user has approved the rendered spec. Use when: "create a workflow that does X", "schedule a daily refresh of dataset Y", "wrap this NQL as a workflow", "build a pipeline that creates view A then refreshes view B", "submit this workflow YAML", "productionize this query as a recurring job". (narrative-common)

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 starts a non-trivial coding/building task in Claude Code (e.g. "帮我做个 X", "help me build Y", "add feature Z", "搭一个 Z"). Layers an AI-native pedagogical coat over normal coding work — surfaces implicit assumptions in user intent, annotates non-trivial design decisions inline with brief concept-scaffolding for terms the user may not know, and replaces "should I run it?" handoffs with rigorous AI self-verification plus a clean list of what only the user can verify. Skip for pure Q&A, single-line edits, read-only investigation, or refactoring along an existing pattern.

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。

Arlo Belshee의 6단계 네이밍 프로세스와 Clean Code 네이밍 규칙을 적용해, 여러 파일에 흩어진 개별 이름 스멜(타입 반복·-er/-Utils·불용어·축약어 등)을 탐지하고 배치 교체하여 코드 가독성 향상. 단, 하나의 긴 메서드를 통째로 관통(grouping→extract→rename)하는 것은 /intent-revealing-names가 적합. /naming-process로 호출.

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。

지역 변수가 서로 얽혀 Extract Method가 불가능한 거대 메서드를 별도 클래스(Method Object)로 추출. "긴 메서드를 클래스로", "지역 변수 때문에 추출 불가", "method object", "/extract-method-object" 요청 시 사용. 단, 지역 변수 얽힘 없이 잘라낼 수 있는 긴 메서드는 /intent-revealing-names(grouping→extract)가 적합. /extract-method-object [commit-ref]로 호출.

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。

Refactor code by renaming variables, methods, classes, and concepts for clearer intent. Use when asked to improve names, clarify naming, perform a naming pass, or run `/refactor:names`.

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 when asked to review a local diff/change set for correctness, regressions, blast radius, affected flows, and missing tests using the knowledge graph; use `review-pr` for intent/checklist/merge-gate evidence.

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 for pre-PR self-review or pull-request review that must verify the matched intent, allowed scope, required tests/docs, forbidden actions, architecture gates, and merge readiness; not for a general code-quality diff review alone.

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。

Link component, useNavigate, Navigate component, router.navigate, ToOptions/NavigateOptions/LinkOptions, from/to relative navigation, activeOptions/activeProps, preloading (intent/viewport/render), preloadDelay, navigation blocking (useBlocker, Block), createLink, linkOptions helper, scroll restoration, MatchRoute.

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。

Collapse meaningful ambiguity with contrastive examples. Use when a plan/design has several plausible meanings, requirements feel vague, examples would clarify intent faster than grilling, or the user asks to disambiguate, find ambiguity, use behavioral kernels, or ask contrastive questions.

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。

Author a completed-spec fixture — a seeded intent graph plus a believable four-phase interview transcript — for a brunch feature, so it can feed any downstream tooling that consumes a completed spec or dogfood the UI. Use when you need a real completed specification in .brunch/brunch.db for testing or demo: pick a feature from memory/SPEC.md (or a free description), choose a stress profile (coverage|clean), and produce a registered scenario seed + an interview-questions doc that match exactly.

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 starting creative work (features, components, functionality, or behavior changes) to clarify user intent, requirements, and design before implementation.

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。