「pta」の検索結果

Exercise the delivered customer journey using real runtime or browser evidence. Use for functional acceptance testing after implementation, including failure paths.

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。

Sequence an understood outcome into verifiable delivery slices with dependencies, ownership and acceptance checks. Use for delivery planning, estimation or migration 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。

Prepare a sponsor update separating promised outcomes, measured results and customer acceptance. Use for progress readouts or defending a delivery claim.

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。

Translate ideas, feature requests, or vague concepts into specific, actionable dev briefs. Use this skill whenever the user has an idea they want to build, a feature to spec out, a bug to file, a project to scope, or needs to convert a half-formed idea into a clear implementation brief. Triggers on I want to add, we should build, can we make, what is the plan for, how do we implement, dev brief, feature spec, PRD, user story, acceptance criteria, scope this, prioritize. Also triggers when the user has a list of things they want to build and needs help converting them into well-formed 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。

Grade a ForgeCAD or CAD-as-code model against a requirement, brief, prompt, reference, or acceptance criteria with evidence and a 0-10 score.

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.

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。

Agile product ownership for backlog management and sprint execution. Use for writing user stories and acceptance criteria, planning sprints, estimating story points, breaking down epics, or prioritizing a backlog.

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 development from an executable specification with traceable requirement IDs and merge-time coverage gates. Use when starting a greenfield feature, reviving a stale spec, or gating merges on requirement coverage.

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。

Talent acquisition across recruiting strategy, sourcing, interviews, and hiring analytics. Use when writing job descriptions, designing interview scorecards, analyzing hiring funnels, or improving offer acceptance rates.

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 Terraform Provider resources and data sources using the Plugin Framework: CRUD operations, schema design, plan modifiers and validators, not-found handling, waiters for eventually consistent APIs, import support, resource design principles, and required acceptance test coverage. Use when adding or changing a resource or data source, deciding whether an API concept should be a resource, wiring a resource to the provider's configured client, handling drift or resource-not-found, or reviewing a resource implementation before submission.

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La compétence Normalisation juridique FR nettoie un document Word rédigé en français juridique. Elle distingue deux régimes : les corrections déterministes (apostrophes courbes, guillemets français, espaces insécables, insécables des références — art. 1240, n° 21-12.345, 50 % —, ordinaux 1ère → 1re, ligatures œ, la loi dispose et non stipule, anglicismes-calques), appliquées directement ; et les réécritures de jugement (virgule avant « et », ternaires creux, formules emphatiques, harmonisations), posées en révisions Word soumises à votre acceptation. Chaque changement est consigné et restitué en tableau récapitulatif : on revient en arrière mot à mot — « défais 3, 7-10 ». La normalisation des citations reste purement formelle. Fonctionne en Cowork ou sur fichier Word.

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Takes a skill — a .skill or .zip archive, a bare SKILL.md, a folder, or just a raw idea — and produces a package ready for public distribution on lawve.ai, plus the exact entries for the submission form. Runs a gate first: skills with no plausible legal use, unsafe code, or licensing bars are declined with reasons, and general-purpose skills that could serve legal work are offered a legal adaptation instead. Then applies the standard compliance transforms — structure, metadata, attribution, dependency ladders, orphaned-file repair, limitations disclosure — validates the result, and delivers a zip. Raw ideas are gated first, built (via skill-creator where available), then adapted. Use for "make this Lawve-compliant," "prep this skill for Lawve," "package for lawve.ai," "is this suitable for Lawve," "get my skill ready to publish," or any request to ready a skill for the Lawve catalogue.

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Audits legal AI outputs and workflows for honest compression: unnecessary complexity, false simplicity, excessive caveats, hidden uncertainty and poor cost per legally acceptable output.

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。

Turn a novel into a fully playable game on the selected target platform. Orchestrates the whole adaptation pipeline — requirements intake, gameable deconstruction, concept selection, world and visual design, target-runtime build, and evidence-based QA — for a novel in any language. Use for novel to game, story to game, book to game, adapt this novel into a game, turn this book into a playable prototype, make an interactive story or text adventure from this novel. NovelToGame 总入口。把任意语言的原始小说、拆文库或 oh-story 写作工程转成有原著依据、可在目标平台完整游玩的游戏，编排游戏化拆解、概念选择、游戏与视觉设计、目标运行环境构建和证据化质量验证。用于小说转游戏、把这本书做成游戏、把小说改成互动小说 / 互动叙事 / 文字冒险游戏等需求。

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Adapt a historical primary source for classroom use — modifying complexity and length while preserving features for sourcing, close reading, and corroboration. Use when a primary source is too complex for the target age group.

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。

Adapt a classroom task for specific learner needs while preserving the core learning objective intact. Use when differentiating for SEND, EAL, gifted, ADHD, dyslexia, or anxiety.

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 a fact-checking protocol for AI-generated text, extending SIFT with AI-specific adaptations for hallucination detection. Use when students need to verify AI claims and citations.

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 patterns for migrating 2D games to 3D including node type conversions, camera systems (third-person, first-person, orbit), physics layer migration, sprite-to-model art pipeline, and control scheme adaptations. Use when porting 2D projects to 3D or adding 3D elements. Trigger keywords: CharacterBody2D to CharacterBody3D, Area2D to Area3D, Camera2D to Camera3D, Vector2 to Vector3, collision_layer migration, sprite to MeshInstance3D, 2D to 3D conversion.

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 handling multiple resolutions — stretch modes, aspect ratios, DPI scaling, and mobile/desktop adaptation

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 comprehensive Product Requirements Document from product plan. Creates PRD.md with features, user stories, acceptance criteria, and success metrics. Use when creating product requirements.

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。

Localization and internationalization strategy for iOS/macOS apps. Covers market prioritization, language tier recommendations, minimum viable localization levels, translation workflows, cultural adaptation, localized ASO, and testing. Use when planning localization, expanding to new markets, deciding which languages to support, or planning translation workflow.

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This package implements a suite of methods to preprocess data from PTR-TOF-MS instruments (HDF5 format) and generates the 'sample by features' table of peak intensities in addition to the sample and feature metadata (as a single ExpressionSet object for subsequent statistical analysis). This package also permit usefull tools for cohorts management as analyzing data progressively, visualization tools and quality control. The steps include calibration, expiration detection, peak detection and qua

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。

Lead substantial Codex builds with one configured implementation worker and lead acceptance. Use when the user requests codex-on-crack, names a model to delegate work to, asks to run subagents through Claude, or requests a lead/worker split. Keep host-only work direct; honor explicit external model choices. Workers must not invoke this orchestrator.

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。

Expand a 1-4 sentence product brief into a full spec with a design language, acceptance surface, and an ordered feature list.

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。