「before」の検索結果

Use when cataloging or annotating visible construction in photographs or scan RGB, especially before registration or while the base model may be replaced.

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 reviewing a project's architecture for design pattern applicability -- either validating a design document before implementation, or analyzing an existing codebase for pattern improvement opportunities.

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。

Apply when concurrent actors might write to the same file, branch, key, or state object. Eliminate the sharing first; serialize structurally only when one shared writer is a real invariant.

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 before review when the user asks to strip, remove, or clean up comments in code.

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。

Apply when facing a novel UI interaction or architectural decision with no precedent in the codebase. Build 2-3 competing prototypes and compare side by side before committing.

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。

Explain how a subsystem works, trace a runtime flow, or determine where code and ownership belong before making a change.

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。

Apply when sequencing an addition, refactor, or rewrite. Remove dead weight, redundant validators, and stub references first, then build on the simpler base.

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。

Apply to multi-step work (sweeps, migrations, runs of similar edits) and to how you stack commits and PRs. Break work into small units that each end in a verifiable state, check each before the next, and order delivery so the sequence proves itself to a reviewer.

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。

Find what a change could break somewhere else before it ships, beyond the diff, and prove the one fact it's safe because of by running real code instead of writing it up. Use for 'blast radius of X', 'what could this break', or reviewing a small diff you don't trust.

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。

Apply before writing logic: choosing core types and data structures, sequencing scaffold-vs-feature work, asking what concurrent actors share. Get the data structures right so downstream code becomes obvious.

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。

Apply after completing a task, before declaring done. Verify against the real artifact (run the feature, read the actual value, inspect the diff), not a proxy, self-report, or 'it compiles.'

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。

Reconstruct your recent working context from your own chat history, live state, and the shared record (user reports, prior fixes, incidents), then hand back a tight current-state brief. Use for 'recall my work on X', 'catch me up', 'what have I been working on', 'where did I leave off', before starting or resuming work.

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。

Sketch types, signatures, and module structure before code, then stay in the loop while implementation fills in. Use for /architect, 'architect this', 'design this', or non-trivial work where jumping to code would lock in the wrong shape.

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。

Self-verification loop. After completing a task, re-read the original request, list each requirement, verify whether it was met, and fix gaps before stopping.

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 an Estuary derivation for stateless filtering, field selection, and per-document field transformation. Use for WHERE clauses, projecting fields, computing new fields from existing ones, or data cleansing. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this (renaming alone is usually a projection, not a derivation). Use when user says "filter-transform derivation", "derivation to filter by status", "transformation with WHERE clause", "derive computed fields", "cleanse data with a derivation", or "stateless derivation".

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。

Write Estuary derivations in Python — async-generator transforms, Pydantic-typed source/output, optional persistent state, and dependency installation via uv. Use for ML/embeddings, async API calls, or any transform where SQL and TypeScript are awkward. Derivations add complexity and cost (and Python derivations require a private/BYOC data plane) — confirm the user wants a Python derivation before reaching for this. Use when user says "python derivation", "write a derivation in python", "embed with a python derivation", "ML derivation", "call an API from a derivation", or anything mentioning model2vec / huggingface / pydantic in a derivation context.

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 an Estuary derivation that joins two or more collections on a shared key, merging their fields into a single enriched collection. Use for data enrichment, denormalisation, building wide tables for BI, and real-time CDC joins. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this (a destination-side SQL join or BI tool view is often simpler). Use when user says "join-collections derivation", "derivation to join two collections", "transformation to enrich with customer data", "derive a wide table", "lookup-join derivation", or "build a join derivation".

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 an Estuary derivation with custom SQLite state (internal tables) to make per-document decisions based on accumulated history — account balances, inventory levels, approval workflows, deduplication. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this. Use when user says "stateful derivation", "derivation to track balance", "transformation to approve based on state", "derive an approval workflow", "deduplication derivation", or "build a stateful derivation".

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 an Estuary derivation that flattens a nested array into one output document per array element. Use for normalising nested JSON, exploding line items, unpacking tags, or producing one-row-per-item tables from arrays. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this (some destinations can `UNNEST` server-side without one). Use when user says "flatten-array derivation", "derivation to explode an array", "transformation to unnest", "derive one row per item", or "flatten nested data with a derivation".

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 an Estuary derivation that continuously aggregates metrics (daily totals, running counts, min/max, lifetime revenue) using reduction annotations. Use for real-time dashboards, continuous materialised views, and replacing batch ETL aggregations with streaming. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this. Use when user says "aggregation derivation", "derivation to sum daily totals", "running-count derivation", "transformation for min/max per group", "derive lifetime metrics", "aggregate-by-date derivation", or "real-time-dashboard derivation".

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 an Estuary derivation that maintains a sliding time window of events (e.g. "transfers in the last 24h", "failed logins in the last 5 minutes") using internal SQLite state and readDelay for time-based expiration. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this. Use when user says "windowing derivation", "derivation for last-24h events", "rolling-window derivation", "sliding-window transformation", "fraud-detection derivation", "rate-limit derivation", or "time-window derivation".

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。

사회복지 기관(복지관, 생활시설, 센터, 비영리단체)의 실무자가 업무용 앱을 만들거나, 업무를 자동화하거나, 엑셀·양식을 전산화하고 싶다고 할 때 코드를 만들기 전에 업무의 필요성, 개인정보 등급, 사람에 관한 기록의 윤리, 플랫폼 경로, 운영 조건을 판단하고 DECISION.md로 정리하는 절차. "프로그램일지 앱", "이용자 명부", "출석 체크", "사례관리 기록", "후원자 관리", "차량 운행일지", "복지 앱", "바이브코딩", "희망이음 자동 입력", "에이전트 브라우저로 동기화" 같은 말이 나오거나 사회복지 현장의 행정 업무를 자동화하려 할 때 사용한다. Use before writing any code when a Korean social-welfare practitioner wants to build an app or automate a workflow. Not for general coding tasks.

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。

Reproduce, isolate, and diagnose a specific software failure before changing production behaviour, then propose the smallest safe fix with regression protection. Use whenever the user reports a bug, crash, error, stack trace, flaky test, incorrect output, "works on my machine," intermittent failure, regression after a deploy, or any behaviour that diverges from expectation and the cause is not obvious. Prefer this over jumping straight to a fix. Part of the audit suite; inherits the shared constitution, while its own job is proving causality.

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。

Creates and reviews agent skills safely by enforcing deletion-prevention harness rules: no fixed absolute temp paths, no cwd-dependent cleanup, no wildcard deletes, no workspace-external file operations, no silent cleanup failures, and mandatory temp-root validation before any destructive command. Use when creating a new skill, updating an existing SKILL.md, or reviewing prompts/runbooks that include temp files, extraction, cleanup, PowerShell, bash, rm, del, Remove-Item, or archive expansion.

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。