'interview' 검색 결과

Set up a commercial contracts assistant around your team's review standards and approval rules. The interview creates an editable practice profile and can compare stated negotiation positions with terms found in signed agreements.

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• Setting up a commercial contracts assistant for the first time
• Documenting contract review standards and approval rules
• Comparing stated policies with signed contract terms

Answer Python questions and write code using examples fetched from pythonsheets.com. Find patterns for syntax, asynchronous tasks, networking, databases, machine learning, and distributed computing, or use topic-based questions to prepare for Python interviews.

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• Preparing for Python interviews
• Learning asyncio through examples
• Writing a socket server

Build a threat model that identifies valuable assets, attack entry points, potential threats, and mitigation priorities. Use source code and past vulnerabilities, an interview with the application owner, or a combination of both.

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• Reviewing security risks during system design
• Assessing an inherited codebase without its owner
• Refining a code-based draft with the application owner

Analyze public reviews, app stores, and forums to identify unmet customer needs, competitor weaknesses, and reasons for switching products. Receive short customer quotes with source links, recurring themes, and hypotheses to validate in interviews.

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• Finding recurring complaints about competitors
• Preparing hypotheses before customer interviews
• Gathering quoted evidence for sales comparison guides

Build a company research brief from public sources, covering executive statements, product strategy, organizational practices, and transformation initiatives. Use dated sources to prepare for interviews, understand competitors, assess potential partners, or explore market entry.

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• Preparing for a product management interview
• Researching a competitor's product strategy
• Briefing a team before a potential partner meeting

Guide a product team from a problem hypothesis through customer interviews, research synthesis, and solution experiments. Use six structured phases to gather evidence, compare options, and decide whether to build, change direction, or stop.

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• Validating customer problems before development
• Investigating onboarding drop-off or churn
• Organizing interviews and research synthesis

Work through the move from product manager to Director with questions tailored to your stage. Get practical recommendations for promotion preparation, interviews, early leadership challenges, or persistent difficulties in the role.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Identifying development priorities before a Director promotion
• Preparing experience stories and answers for Director interviews
• Addressing delegation and direction-setting challenges after promotion

Register and search existing Git repositories and worktrees, then choose which projects Ouroboros interviews use as context. You can also generate a configuration containing verified build, test, and other check commands for the evaluation workflow.

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• Selecting existing projects as interview context
• Finding registered repositories by topic
• Rescanning after repositories are added or moved

Turn a vague development idea into clear goals, constraints, and acceptance criteria through a guided interview. It checks unresolved decisions and confirms the agreed goal before moving toward a Seed, the requirements document used by Ouroboros.

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• Clarifying a vague idea before development
• Defining completion criteria for a new feature
• Agreeing on scope and constraints

Develop a research idea through a structured conversation, then turn the answers into a written research specification. Clarify the question, testable hypothesis, data needs, methods for assessing cause and effect, expected findings, and feasibility.

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• Turning an early research idea into a concrete plan
• Exploring a new research direction from scratch
• Identifying data needs and threats to causal interpretation

Combine interview themes and numerical findings into comparison tables and integrated conclusions. The skill helps select an integration approach, explain agreements and contradictions, and assess whether conclusions are supported by both kinds of evidence.

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• Compare survey results with interview themes
• Develop survey items from interview findings
• Explain conflicting qualitative and quantitative findings

Design studies that combine quantitative data, such as surveys, with qualitative evidence, such as interviews. It recommends a suitable design and outlines study phases, participant selection, timing, and ways to combine findings into an integrated interpretation.

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• Plan follow-up interviews to explain survey results
• Develop survey measures from interview findings
• Compare quantitative and qualitative findings

Organize interview transcripts and other qualitative material into codes, or meaning labels, and broader themes. Develop coding guidelines, compare researchers’ coding decisions, and document whether additional data still introduces new findings.

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• Developing a codebook for interview analysis
• Organizing codes into themes
• Checking agreement between researchers

Plan interviews and focus groups for qualitative research, which explores people’s experiences and views. Develop question guides, follow-up prompts, discussion plans, transcription conventions, and participant review procedures suited to the study.

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• Preparing a semi-structured interview guide
• Planning focus group discussions
• Choosing transcription conventions

Analyze interviews, focus groups, and open-ended survey responses using Braun and Clarke's six-phase framework. Label meaningful passages, develop and review themes, and produce an academic Word report with an annotated map showing theme relationships.

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• Identifying themes across interview transcripts
• Analyzing open-ended survey responses
• Choosing an approach before coding qualitative data

Turn a rough app or automation idea into a detailed specification through guided interviews and research. Clarify user journeys, data, security, operations, and competing priorities, then document requirements and acceptance criteria for implementation.

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• Turning a rough app idea into a specification
• Clarifying requirements without technical expertise
• Resolving conflicting requirements and priorities

Turn speech from audio or video recordings into text using OpenAI. Save transcripts as text or JSON, with optional speaker labels and reference recordings to help identify known speakers in meetings or interviews.

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• Transcribe recorded meetings
• Label speakers in interviews
• Extract spoken text from audio or video

[omh] Vague, underspecified request: one-question-at-a-time clarification. Use when the user says: deep-interview, interview me, clarify, feature shaping, ambiguous product request, one question, 要件を詰めて, 曖昧な要求.

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SUPERSEDED by /claudex-loop (formerly /crucible; adds a Phase 0 research/recon pass + rebuilt interview) — prefer that skill unless you explicitly want this one. Two-act plan hardening. ACT 1 (you ↔ Claude) — Claude interviews you relentlessly about a plan or design, one question at a time, recommending an answer for each and exploring the codebase when it can answer itself, until every branch of the decision tree is resolved. ACT 2 (Claude ↔ Codex) — Claude writes the locked plan to PLAN.md and OpenAI Codex adversarially reviews it in a read-only sandbox (VERDICT:APPROVED/REVISE), Claude revises and re-submits to the SAME Codex session until APPROVED or a MAX_ROUNDS cap, then you sign off before any code. Use when the user says "/grill-me-codex", "grill me then have codex review", "grill me and stress-test the plan", "interview me about this plan then get a second model on it", or is about to build something high-stakes (auth, schema, concurrency, migrations, payments) and wants both alignment AND a cross-model sanity check before implementation. Builds on Matt Pocock's grill-me (MIT). For the docs-aware variant use /grill-with-docs-codex; if you already have a plan and want only the Codex review use /codex-review. NOT for reviewing already-written code (use /codex:review) and NOT for trivial changes.

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Build a weekly cadence of customer touchpoints using Opportunity Solution Trees, assumption mapping, and interview snapshots. Use when the user mentions "continuous discovery", "opportunity solution tree", "weekly interviews", "assumption testing", "discovery habits", "product trio", "outcome-based roadmap", "how do I talk to customers regularly", "we keep building things nobody uses", or "connect research to the roadmap". Also trigger when setting up regular customer feedback loops, prioritizing which experiments to run, or tying discovery insights to delivery work. Covers experience mapping, co-creation, and prioritizing opportunities. For interview technique, see mom-test. For team structure, see inspired-product.

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Mock interview preparation tool for residency Match interviews. Generates.

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Curated bank of interview problems organized by company, pattern, and difficulty. Provides problem recommendations, coverage tracking, weak area identification, and premium problem alternatives for FAANG interview preparation.

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Interactive interview that formalizes a fuzzy research idea into a structured spec (RQ, hypotheses, identification, data needs, empirical strategy). Use when user says "interview me", "help me think through this idea", "I have a half-baked idea", "formalize this into a project", "walk me through framing a study". Multi-turn Q&A; saves spec to disk. NOT for lit review (`/lit-review`) or ideation from scratch (`/research-ideation`).

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Use when asked 帮我准备大厂技术面试, 八股文怎么背, 模拟一面二面, 系统设计面试题, 项目深挖怎么答, 手撕算法, or run a mock technical interview for a Chinese internet company. Produces a preparation plan for the interview rounds Chinese tech companies run (笔试, 技术一面二面, 交叉面, HR 面), a 八股 question bank by stack with answer points, a system design walkthrough, a project deep-dive script, and a mock round asked one question at a time with follow-ups (追问) and scores.

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