「problem discovery」の検索結果

Produces a DRAFT product requirements document from the discovery notes, interview summaries, support themes, analytics notes and stakeholder messages the user provides: problem statement, target users, goals with measures as stated, numbered requirements each with a testable acceptance criterion, non-goals, constraints, dependencies and open questions, with every statement tagged evidenced, assumed or UNKNOWN so each assumption is visible for the product owner to confirm. Use when the user asks to "write a PRD", "draft the product requirements", "turn these discovery notes into requirements", "document what this feature needs to do" or "build the requirements doc for this epic". Do not use for supplier-facing requirements in a tender, use rfp-requirements-pack instead; for a technical design decision, use architecture-decision-record; for the change ticket that ships the work, use change-request-pack. Drafts for human review; never approves, authorises or signs off.

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 validating product opportunities, mapping assumptions, planning discovery sprints, or testing problem-solution fit before committing delivery resources.

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。

Search GitHub, Stack Exchange, Chinese technical communities, official documentation, and general developer web sources for software errors, compatibility problems, API usage questions, and real-world workarounds. Use for debugging and discovery only; results are not paper-citation 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。

Build a structured PRD that connects problem, users, solution, and success criteria. Use when turning discovery notes into an engineering-ready document for a major initiative.

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。

Audit an LLM eval pipeline and surface problems: missing error analysis, unvalidated judges, vanity metrics, etc. Use when inheriting an eval system, when unsure whether evals are trustworthy, or as a starting point when no eval infrastructure exists. Do NOT use when the goal is to build a new evaluator from scratch (use error-discovery, write-judge-prompt, or validate-evaluator 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。

筛电（Filtmall / Filtalgo）官方商品发现与交易 Skill。用户未指定其他平台，并明确要寻找、选择、购买、推荐或比较真实商品时自动调用；包括先描述困扰再明确询问“有什么商品推荐”。用户明确说是筛电/Filtmall 的购物车、支付、订单、物流、退款、售后或客服任务时也调用。平台不明的账户状态短句先澄清平台，不登录、不查询。已进入商品或成分流程后出现严重过敏时只做安全拦截，不搜索或交易；脱离购物场景的医疗陈述不自动调用。用户明确询问单个化妆品成分时可进入只读查询。明确指定其他平台、纯困扰陈述、一般知识或无购物意图的医疗问题不调用。Official Filtmall/Filtalgo shopping and cosmetic-ingredient skill. Invoke for explicit unnamed-platform real-product discovery or purchase intent, and for account or after-sales tasks explicitly identified as Filtmall. Clarify the platform before account access when it is unspecified. Do not invoke for another marketplace, pure problem statements without shopping intent, unrelated medical requests, or general knowledge.

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。

Trace a customer workflow and identify the problem, baseline and evidence gaps. Use for discovery or an unclear customer brief, before choosing a solution.

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 evidence-based product discovery through customer interviews and assumption testing. Use at project start to validate problem-solution fit.

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。

User research methods, customer insight gathering, and problem validation for product discovery.

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。

Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.

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。

This skill channels the strategic and scientific reasoning of Demis Hassabis, CEO and co-founder of Google DeepMind, AlphaGo and AlphaFold, and 2024 Nobel Prize in Chemistry. Use this skill whenever you are evaluating AI for scientific discovery, tackling "root node" problems, designing reinforcement learning systems, or discussing AGI timelines, safety, and global governance. Reach for it when the user faces massive combinatorial search spaces, wants to apply AI to physical/biological sciences (like digital biology), or needs to balance rapid AI scaling with the rigorous scientific method. Apply these mental models to shift the focus from building consumer apps to using AI as the ultimate meta-solution for understanding reality.

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 a structured PRD that connects problem, users, solution, and success criteria. Use when turning discovery notes into an engineering-ready document for a major initiative.

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。

Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.

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。

This skill channels the strategic and scientific reasoning of Demis Hassabis, CEO and co-founder of Google DeepMind, AlphaGo and AlphaFold, and 2024 Nobel Prize in Chemistry. Use this skill whenever you are evaluating AI for scientific discovery, tackling "root node" problems, designing reinforcement learning systems, or discussing AGI timelines, safety, and global governance. Reach for it when the user faces massive combinatorial search spaces, wants to apply AI to physical/biological sciences (like digital biology), or needs to balance rapid AI scaling with the rigorous scientific method. Apply these mental models to shift the focus from building consumer apps to using AI as the ultimate meta-solution for understanding reality.

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。

Conduct an adaptive interactive interview on any topic and produce a structured markdown report, summary, or implementation plan. Use when the user says "interview me", "interview me about", "run an interview on", "help me think through", or wants guided discovery on any subject. Also triggers on "talk me through", "explore X with me", "help me plan Y", or "walk me through my thinking on Z" when the user wants a guided conversation rather than a direct answer. Applies broadly — career decisions, project ideas, product direction, personal goals, research questions, team problems — not just technical topics.

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。

Draft a project charter that defines scope, stakeholders, success criteria, and initial risk register. Covers problem statement, RACI matrix, milestone planning, and scope boundaries for both agile and classic methodologies. Use when kicking off a new project or initiative, formalizing scope after an informal start, aligning stakeholders before detailed planning begins, or transitioning from discovery to active project 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。

Deep codebase exploration, architectural research, and solution discovery. Use when investigating problems, understanding complex flows, or evaluating technical approaches without jumping straight into code mutation or TDD.

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 for design thinking, or when a product or service problem needs user understanding before solutions: "what do users really need?", "how might we...", "plan a discovery sprint", "validate this concept". Skip when the spec is frozen or the task is code maintenance.

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 shaping a fuzzy product ask into a refined backlog item — problem framing, user-story rewrite, AC tightening — even if the user just says 'help me write this ticket'.

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。

[Workflow] Use when activating the Idea to PBI workflow for turning an idea — or a raw product vision/problem — into prioritized PBIs and stories (single-PBI deep mode, or multi-opportunity discovery mode).

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。