본문으로 건너뛰기
cccskills
무료GitHub에 공개

interview-simulator

Designs candidate-controlled mock interview practice across voice, text, code, and whiteboards. Use for session configuration, observable-evidence feedback, and adaptive practice plans. NOT for hiring decisions, emotion inference, gaze or integrity monitoring, or ranking candidates.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(4)

  • SKILL.md12.1 KB
  • references/INDEX.md550 B
  • references/mobile-app-architecture.md4.6 KB
  • references/whiteboard-engine-setup.md3.6 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Interview Simulator

Run a realistic, candidate-controlled mock interview. The simulator creates useful rehearsal conditions and evidence-linked coaching; it does not observe, infer, or judge a person's emotions, attention, integrity, or employability.

When to Use

Use for a timed coding, design, behavioral, presentation, deep-dive, or hiring-manager rehearsal; for voice or whiteboard practice; and for a debrief that connects feedback to the candidate's own recorded work.

Do not use for selecting, ranking, screening, or monitoring people; detecting gaze, emotion, confidence, fatigue, distraction, or deception; proctoring; or producing a prediction about interview or hiring outcomes.

Core Process

flowchart TD
  A[Candidate selects goal, modality, and consent] --> B[Configure prompt and rubric]
  B --> C[Run practice with candidate controls]
  C --> D[Capture only consented, observable artifacts]
  D --> E{Enough evidence for each rubric finding?}
  E -->|Yes| F[Link finding to session evidence]
  E -->|No| G[Mark feedback unavailable or uncertain]
  F --> H[Candidate-led debrief]
  G --> H
  H --> I[Candidate accepts, edits, or declines next practice step]

1. Establish the practice contract

Before presenting a prompt, confirm:

  • the round type, session goal, duration, modality, and starting difficulty;
  • whether the candidate wants recording, a transcript, or retained whiteboard/code artifacts;
  • any accessibility needs, breaks, pacing, and preferred feedback style; and
  • that all feedback is for practice, not a hiring assessment.

Do not turn on a camera, capture background activity, infer attention, or inspect a second monitor. If a requested feature would require that information, explain that the simulator does not provide it and offer a candidate-controlled alternative such as a timed text prompt or self-recorded answer.

2. Run one bounded round

Use the round routing below to select a prompt and observable rubric. State the timebox and any materials allowed. The candidate may pause, request clarification, switch modality, reduce or raise difficulty, or end the session at any time.

For a difficulty change, ask first. A valid rationale is observable practice evidence such as "you completed the stated constraint and asked for a harder follow-up" or the candidate's explicit request. Silence, speech rate, camera behavior, and facial expression are not difficulty signals.

3. Capture evidence, not impressions

Record only artifacts the candidate elected to retain: prompt versions, response text, code, whiteboard snapshots, transcript segments, elapsed time, and candidate self-notes. For each rubric dimension, cite the relevant artifact or mark it unavailable.

Use language such as "the response named a cache invalidation strategy at 12:40" rather than "you seemed confident." Where artifact coverage is incomplete, say what cannot be assessed.

4. Debrief into an experiment

Produce at most three strengths, three improvement experiments, and one next-session proposal. Each item must include:

FieldRequirement
ObservationA direct pointer to a consented session artifact.
RubricThe round-specific criterion being applied.
ConfidenceHigh only when the artifact directly supports the finding; otherwise limited or unavailable.
ExperimentA candidate-actionable change for the next rehearsal.

The candidate can correct the record, remove retained artifacts where the host product supports removal, and accept, change, or decline the proposed next step.

Round Routing

Selected roundPrompt and rubric source
Codingsenior-coding-interview
General SWE system designCandidate-selected design prompt and agreed observable criteria; no general-design specialist is installed and ML material is not a default.
ML system designml-system-design-interview, only when explicitly selected.
Presentationtech-presentation-interview
Behavioral or valuesvalues-behavioral-interview
Hiring-manager conversationhiring-manager-deep-dive
Technical deep diveCandidate-selected project and factual project evidence; use anthropic-technical-deep-dive only for explicitly selected Anthropic research.

A paired specialist supplies practice material within this session contract. Do not adopt employer predictions, inferred traits, or unverified process claims from specialist material. The candidate approves any target-specific scope and prompt.

Bundled References

Load references/INDEX.md for optional mobile practice or whiteboard implementation guidance. Both guides inherit the session contract, including consent before external processing and candidate approval before plan changes.

Difficulty and Modality Decisions

SituationMove
Candidate requests a harder or easier promptChange difficulty and record the request.
Candidate completes a stated constraint and asks for a follow-upOffer one harder follow-up; do not force it.
Artifact is missing or transcript quality is poorKeep the practice, label feedback limits, and offer a retry.
Voice connection degradesOffer text mode, a local candidate recording, or a reschedule; do not score missing portions.
Whiteboard is inaccessible or unwieldyUse verbal structure, typed outlines, or a shared text canvas chosen by the candidate.
Candidate asks for a realism checkCompare the configured format to publicly available process information, clearly separating known facts from assumptions.

Quality Gates

Mark a session complete only when:

  • The candidate chose or confirmed the session contract.
  • Each retained artifact has an explicit purpose and consent basis.
  • Feedback findings cite observable session evidence or are labeled unavailable.
  • Technical or transcript gaps are visible in session_quality.
  • No feedback claims emotion, attention, integrity, personality, ranking, or a hiring outcome.
  • The candidate has a clear option to revise or decline the next practice step.

Anti-Patterns

Proctoring Disguised as Practice

Novice: "A realistic mock should monitor eyes, tabs, cameras, and second screens."

Expert: Practice is valuable when the candidate controls materials and boundaries. Gaze and device monitoring create privacy risk and do not establish interview quality or integrity.

Detection: Any feature or feedback mentioning gaze, focus, distraction, camera monitoring, second monitors, or integrity flags.

Affect as a Score

Novice: "Hesitation, vocal tone, or facial expression reveal confidence and should adjust difficulty."

Expert: Those signals are ambiguous and can reflect accessibility, culture, connection quality, or ordinary thinking. Adjust only at the candidate's request or from explicit task evidence.

Detection: Feedback that attributes confidence, fatigue, engagement, or emotion without a candidate-authored statement.

Debrief as a Hiring Forecast

Novice: "A mock score predicts whether the candidate will pass."

Expert: A session is a bounded practice sample. Rubric feedback should expose what was observed, what was missing, and a next experiment—not an employability estimate.

Detection: Percentile, pass/fail, ranking, or hiring-probability claims.

Provider Lock-In

Novice: "Voice, transcription, and whiteboard features should be designed around one vendor."

Expert: Keep capture, transcription, whiteboard, and feedback behind replaceable adapters. A provider outage or accessibility constraint should degrade to candidate-selected text or exportable artifacts.

Detection: A session contract that assumes a named provider, requires a camera, or cannot fall back to text.

Worked Example

Setup: A staff backend engineer chooses a 45-minute system-design rehearsal, typed outline plus voice, difficulty 3, and transcript retention. Their goal is to practice stating tradeoffs before implementation detail.

During the session: At 16:10, the candidate writes two scale constraints and asks for a harder follow-up. The simulator offers one constraint about regional failures. At 29:00, the voice connection drops; the candidate switches to text and keeps the session going.

Debrief:

  • Strength: The candidate named latency and consistency constraints before proposing components, with transcript references at 04:22 and 16:10.
  • Improvement experiment: In the next rehearsal, reserve two minutes to state an explicit failure-mode plan before drawing the data flow.
  • Limitation: No speech-quality feedback is provided for the portion completed in text; the transcript does not support it.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Expert in 2000s-era music visualization (Milkdrop, AVS, Geiss) and modern WebGL implementations. Specializes in Butterchurn integration, Web Audio API AnalyserNode FFT data, GLSL shaders for audio-reactive visuals, and psychedelic generative art. Activate on "Milkdrop", "music visualization", "WebGL visualizer", "Butterchurn", "audio reactive", "FFT visualization", "spectrum analyzer". NOT for simple bar charts/waveforms (use basic canvas), video editing, or non-audio visuals.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

curiositech/windags-skills162026년 10월 14일 업데이트

Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

curiositech/windags-skills162026년 10월 14일 업데이트

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

curiositech/windags-skills162026년 10월 14일 업데이트

Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

curiositech/windags-skills162026년 10월 14일 업데이트

Use for insight, reframing, contradiction, impasse, and anomaly-driven problem solving when execution effort no longer helps. NOT for routine optimization, error correction, or well-specified tasks with known solution paths.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

curiositech/windags-skills162026년 10월 14일 업데이트

Apply normative BDI reasoning to agents that must detect norms, choose which commitments to internalize, and resolve conflicts by comparing consequences. Use when obligations, prohibitions, or policies collide in autonomous systems. NOT for simple fixed-priority rules, pure constraint satisfaction, or domains where no real normative conflict exists.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

curiositech/windags-skills162026년 10월 14일 업데이트

curiositech의 스킬 모두 보기

이 스킬의 문제 신고