「fea」の検索結果

Use when creating, assigning, or checking custom permissions to control feature access beyond CRUD and FLS. Trigger keywords: 'custom permission', 'FeatureManagement.checkPermission', '$Permission global variable', 'feature gate', 'named access grant', 'beta feature flag', 'SetupEntityAccess', 'requiredPermission', 'who has this custom permission'. NOT for gating Apex service code and its tests — use apex/apex-custom-permissions-check. NOT for permission set design — use admin/permission-set-architecture.

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。

End-to-end Android indie idea workflow — feature-build phase only (no monetization). Phased — discover (if no idea) → generate 2 candidates → refine scope (offline/online, sharing, MVP vs full, core-value features) → risk/reward/tradeoff analysis when scoping down → emit idea brief. Use for greenfield app concepts, new feature ideation, idea discovery, or pre-design discovery before writing a design document. Triggers — new app idea, feature concept, indie dev brainstorm, validate idea, refine idea, what should I build, find an app idea, idea pipeline, I have no idea, help me find an app idea, scope down, MVP scoping, core value features.

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。

Survey the whole tdmcp project and produce a prioritized list of NEW features it could implement — across artist controls, CLI/DX, AI/LLM integration, and TouchDesigner depth. Use whenever the user wants to brainstorm, discover, survey, list, or audit what features/tools/effects/controls/commands/prompts/capabilities tdmcp *could* add, asks 'what could we build / what's missing / what are the opportunities / ideas for the project', or wants a feature backlog or gap analysis. Also for follow-ups: re-run, refresh, update, re-survey, deepen, or re-prioritize the backlog, or survey just one surface (e.g. 'CLI ideas only'). This is the IDEATION harness — it produces a list to decide from; it does NOT build. When the user has already chosen a feature and wants it implemented/shipped/added, use tdmcp-pipeline instead.

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。

When the user wants to get featured on the App Store or understand the editorial process. Also use when the user mentions "get featured", "App Store editorial", "App of the Day", "Today tab", "Apple featuring", or "how to get Apple to feature my app". For launch strategy, see app-launch. For ASO optimization, see aso-audit.

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。

抽象的な要件・事業側の要求を深掘りし、既存実装との整合を確認して実装マスタとシステム要件書を作成する。「こういう機能を作りたい」「この要求を満たす機能を設計して」等の抽象要件の提示時、既存機能の拡張や Phase 分割の要件定義開始時、/design-feature 実行時に使用。境界: 要件確定後の実装計画書・PR 分割と実装進行は plan-feature-prs。

Use when the user wants to evolve the features of a PRD roadmap with user stories — 'refinar as histórias da feature', 'escrever histórias de usuário', 'critérios de aceite', 'regras de negócio da feature', 'deixar a feature pronta', 'user stories for FT01' — one feature at a time, interviewing the human until each story meets the Definition of Ready: persona, value, INVEST, CA (Gherkin, happy and error paths) and RN from the PRD. Do NOT use to create the roadmap (prd-roadmap) or to change requirements without stories (prd-refine).

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 wants a small, well-bounded change done now without a plan file — a bugfix, config change, rename, doc update or simple refactor touching 1–5 files — 'corrigir rapidinho', 'quick fix no X', 'renomear Y'. Presents a mini-plan, implements after approval, verifies and commits. Do NOT use for more than 5 files, architectural decisions, migrations or new UI flows (use feat-feature / feat-backend / feat-frontend).

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。

Run a structured discovery interview and produce a complete, developer-ready Product Requirements Document. Load when the user asks to write a PRD, create product requirements, document a feature, define user stories with acceptance criteria, or turn a rough idea into a formal product requirements document. Also triggers on "document this feature", "write requirements for", "create a one-pager", "turn this into a PRD", "I need a PRD for", or any request to produce a structured product document for stakeholder alignment or engineering handoff. Supports Full PRD, Lean PRD, and One-Pager formats. Note: for executable feature specifications (FRs, NFRs, ACs as Given/When/Then consumable by AI coding agents), route to `feature-spec` instead — PRDs frame the product, feature-specs encode the implementable contract.

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 a feature branch from an Azure DevOps ticket. Fetches ticket title, slugifies, creates `feature/<id>-<slug>`, and optionally transitions ticket to Active. Trigger when user says "feature branch", "create branch", "/dev-azdo:feature-branch", or provides a ticket id/URL to start work on.

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。

開発前に製品の狙いを検証し、企画の実行判断、利用開始時のつまずき、機能の優先順位を整理します。根拠と次の行動を含む企画書やロードマップにまとめます。

• 曖昧なアイデアを企画書にしたいとき
• 開発する機能の優先順位付け
• 公開前の導入体験の点検

Coordinate parallel feature development with file ownership strategies, conflict avoidance rules, and integration patterns for multi-agent implementation. Use this skill when decomposing a large feature into independent work streams, when two or more agents need to implement different layers of the same system simultaneously, when establishing file ownership to prevent merge conflicts in a shared codebase, when designing interface contracts so parallel implementers can build against each other's APIs before they are ready, or when deciding whether to use vertical slices versus horizontal layers for a full-stack feature.

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。

Scaffold the co-located feature structure in frontend/src. Use when creating a new page, feature, view (tab), or component folder, when a feature needs a shell with tabs, or when moving existing code out of src/container into src/pages. Generates the full folder tree (components/hooks/store/types/utils/constants/__tests__/README) and registers the page's routes with one command.

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。

Conducts structured requirements workshops to produce feature specifications, user stories, EARS-format functional requirements, acceptance criteria, and implementation checklists. Use when defining new features, gathering requirements, or writing specifications. Invoke for feature definition, requirements gathering, user stories, EARS format specs, PRDs, acceptance criteria, or requirement matrices.

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。

Multi-pass deep code review for large patches (1000+ LOC) that maximizes real bug detection. Orchestrates targeted-review, shallow-review, and deep-review in parallel across all HIGH and MEDIUM risk files and commits: understands the feature holistically, splits by file and commit, runs deep review on every HIGH/MEDIUM file, targeted review across the same scope, and per-commit shallow review, followed by cross-cut consistency checks. Use when: reviewing a large patch (feature branch, multi-commit, or single large diff, 1000+ LOC), doing a thorough pre-merge review, or when shallower reviews miss bugs due to patch size. Triggers on: "review this branch", "review these commits", "review this feature", "mega review", "thorough review", "full review of X commits", or when the user specifies a commit range or feature for review.

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。

Spec-driven feature work that freezes obligations instead of the plan: one human-reviewed plan with EARS criteria, path, entities, interface and one-way doors, then proof-backed checks, then build, then an independent Verifier. Use when the user says "tlc-spec-lean", "plan feature", "specify feature", "write the checks", "build this plan", or "verify work". Do NOT use for standalone design documents unattached to a feature, architecture decomposition analysis, or work that already has a task list or checklist to execute.

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。

[omh] LLM-powered feature to build: LLM app development: prepare a build handoff for an LLM-powered feature with a pinned provider boundary, schema-first outputs, versioned prompt files, grounded retrieval, and an eval suite as a shipped deliverable. Use when the user says: llm-app-dev, llm app development, llm application development, build an llm app, build an llm feature, llm feature development, build a rag pipeline, rag pipeline.

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 cryptocurrency market sentiment using Fear & Greed Index, news analysis, and market momentum. Use when gauging overall market mood, checking if markets are fearful or greedy, or analyzing sentiment for specific coins. Trigger with phrases like "analyze crypto sentiment", "check market mood", "is the market fearful", "sentiment for Bitcoin", or "Fear and Greed index".

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。

When the user wants to get press coverage, media mentions, or editorial features for their app — including writing press releases, pitching journalists, getting on "best apps" lists, or building an app press kit. Use when the user mentions "press", "PR", "media coverage", "TechCrunch", "journalist", "press release", "app press kit", "get featured in media", "editorial coverage", "review from a blogger", or "app launch announcement". For Apple editorial featuring, see app-store-featured. For launch strategy, see app-launch.

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 complete phenotype-scoring bioinformatics research designs for any disease context and any user-defined phenotype, pathway, process, signature, or molecular program. Use when a study centers on gene-set or feature-set definition, intersection with DEGs or candidate features, phenotype scoring, feature selection, diagnostic or stratification assessment, immune or cellular-resolution interpretation, network analysis, and optional orthogonal validation. Covers five study patterns (signature discovery, phenotype scoring, feature selection, immune/cellular interpretation, multi-layer validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.

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。

Break large features into independently verifiable, human-reviewable slices under a feature directory in specs/. Use for risky or multi-step feature work that needs upfront questioning, API seams, browser-playable checkpoints, HTML visualizations, screenshot gates, staged implementation plans, recursive fog-of-war reslicing, or proactive research into reference implementations/best practices before slicing. Pairs with your project's verification harness and screenshot gates (the browser checkpoints), [refactor-clean](../refactor-clean/SKILL.md) (review the materialized spec so the plan describes one-owner architecture, not the feature bolted on), [screenshot-critique](../../visual/screenshot-critique/SKILL.md) and [compare-screenshots](../../visual/compare-screenshots/SKILL.md) (the visual gates), and a code-review pass (audit each slice before it lands).

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。

When the user wants to understand or optimize for SERP feature types (PAA, sitelinks, rich results, AI Overviews). Also use when the user mentions "SERP," "SERP features," "search result features," "People Also Ask," "PAA," "sitelinks," "knowledge panel," "local pack," "rich results," "zero-click," "SERP types," "AI Overviews," "Bing Copilot," or "Yandex AI." For JSON-LD and rich result implementation, use schema-markup. For organic strategy and roadmap, use seo-strategy.

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。

Organize Flutter apps with modular feature-based clean architecture. Use when creating features under lib/features/ with domain, data, and presentation layers. Do not use for test-only, BlocBuilder, navigation, or spinner requests.

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。

Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).

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。

Decide which AI features to build, delay, shelve or kill, and whether each one gets better as models improve. Produces an AI Bet Memo (feature-by-feature verdicts, retest triggers, scaffolding ledger, moat and safety check) from a roadmap or idea list. Use for 'AI strategy', 'AI roadmap', 'what should we build with AI', 'will the next model make this obsolete', 'build vs wait for the model', 'will OpenAI/Anthropic build this', 'agent or chatbot', 'is this feature worth shipping'. Draws on 118 Lenny's Podcast guests incl. Nick Turley, Kevin Weil and Dianne Penn.

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。