본문으로 건너뛰기
cccskills
무료GitHub에 공개

interview-loop-strategist

Builds a candidate-controlled preparation plan for a software-engineering interview target, timeline, and practice evidence. Use for planning round coverage, story coherence, mock cadence, and debrief-driven adjustments. NOT for hiring predictions, candidate ranking, surveillance, or replacing round-specific coaching.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(5)

  • SKILL.md12.4 KB
  • references/company-specific-loops.md2.7 KB
  • references/INDEX.md715 B
  • references/mock-interview-rubrics.md3.3 KB
  • references/preparation-timeline-templates.md2.7 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Interview Loop Strategist

Build an adaptable preparation plan for any software-engineering interview target. The plan helps a candidate choose what to practice, maintain truthful stories across rounds, and learn from rehearsal evidence. It does not determine whether they are qualified, predict hiring outcomes, or replace the candidate's judgment.

When to Use

Use for an end-to-end prep plan, a time-boxed reset after an interview schedule changes, mapping candidate-provided projects to round types, mock scheduling, and debrief-driven reprioritization.

Do not use for resume fabrication, employer screening, candidate comparisons, automated hiring recommendations, monitoring behavior, or a substitute for the specialist skill that teaches one round.

Core Process

flowchart TD
  A[Candidate profile, target, and availability] --> B{Enough information to plan?}
  B -->|No| C[State assumptions and ask only high-value questions]
  B -->|Yes| D[Select candidate priorities and round coverage]
  C --> D
  D --> E[Map candidate-provided stories to specialist practice]
  E --> F[Schedule practice and recovery]
  F --> G[Run selected practice session]
  G --> H{Evidence sufficient and candidate wants change?}
  H -->|Yes| I[Adjust one next cycle with rationale]
  H -->|No or uncertain| J[Preserve plan and label uncertainty]
  I --> K[Candidate reviews plan]
  J --> K

Planning Rules

1. Start with candidate-owned constraints

Record the target role, organization or organization type, known interview stage, date range, available hours, preferred pace, accessibility needs, and the candidate's stated goals. Distinguish facts from assumptions. A company name is optional: use a role-family plan if the target is still exploratory.

Never convert a sparse profile into invented achievements, interview stages, or employer preferences. If a detail materially changes the plan, ask for it; otherwise make a reversible assumption and mark it for review.

2. Choose coverage before intensity

Cover the round types likely to matter for the target role, then weight time according to the candidate's goals and available evidence. Preserve recovery time and schedule fewer, higher-quality rehearsals when time is constrained.

Time availableDefault cadencePlan shape
Less than 2 weeks4–6 focused hours/day only if the candidate opts inProtect essentials, one mock per high-priority round, daily short debrief.
2–5 weeks2–3 focused hours/dayRotate round types, build evidence map, weekly mock and review.
More than 6 weeks1–2 focused hours/dayDeliberate practice, spaced review, periodic full-loop rehearsal.

These are starting points, not prescriptions. Reduce load when the candidate has competing commitments, fatigue, or another stated constraint.

3. Maintain an evidence map for stories

Map only candidate-provided projects and facts. Every story entry should identify the round, the evidence source, the audience emphasis, and open questions.

Story facetAppropriate roundsEvidence to retain
Technical decisions and tradeoffsCoding, system design, deep diveArchitecture, constraints, alternatives, measured result or stated uncertainty.
Leadership and collaborationBehavioral, hiring managerCandidate's role, collaborators, conflict, and outcome.
Motivation and narrativeRecruiter, presentation, behavioralWhy the project mattered, learning, and values the candidate genuinely endorses.

Do not invent metrics, team size, impact, safety claims, or an employer's values alignment. Flag gaps as prompts for the candidate to verify.

4. Use practice evidence carefully

Adjust a plan only from a candidate request or a session record that supports a specific finding. A practice score is not a trait or forecast. If a transcript is incomplete, a round differs from the target format, or there is no comparable history, say that the adjustment is tentative or leave the allocation unchanged.

The candidate can override any prioritization. Record the override as a valid preference, not a plan failure.

Bundled References

Load references/INDEX.md for candidate-controlled timeline templates, evidence rubrics, or source-checked target-process research. Every reference follows the same consent and uncertainty boundaries as this skill.

Specialist Routing

Practice needRoute toStrategist responsibility
Coding or online assessmentsenior-coding-interviewSet cadence and connect debrief evidence to the next cycle.
General SWE system designinterview-simulator with a candidate-selected design prompt and observable rubricChoose target-relevant constraints and state that no general-design specialist is installed; do not substitute ML material.
ML system designml-system-design-interviewUse only when the target or candidate explicitly selects ML-system practice.
Technical deep diveinterview-simulator with a candidate-selected project prompt; anthropic-technical-deep-dive only for explicitly selected Anthropic researchPreserve factual project evidence and open questions; do not assume an employer or invent a domain specialist.
Presentationtech-presentation-interviewReserve rehearsal and recovery time.
Behavioral or valuesvalues-behavioral-interviewMaintain candidate-authored story coherence.
Hiring-manager conversationhiring-manager-deep-diveFrame scope, collaboration, and candidate questions.
Timed rehearsalinterview-simulatorUse candidate-controlled artifacts and evidence-linked debriefs.

Adjustment Decisions

SignalAction
Candidate asks to prioritize a roundReallocate time, showing what is deprioritized.
Evidence-linked mock debrief identifies one repeatable gapAdd one targeted practice experiment in the next cycle.
One weak or incomplete sessionLabel it as limited evidence; do not overhaul the plan.
Interview date, format, or availability changesRebuild the remaining schedule from current constraints and retain completed evidence.
Candidate has no project stories readySchedule evidence gathering and short story drafts before advanced rehearsal.
Target details are unknownUse a general SWE coverage plan and list the assumption for review.

Quality Gates

  • Candidate priorities, constraints, and consent choices are visible in the plan.
  • Known facts, assumptions, and unknowns are distinct.
  • Every story claim originates in candidate-provided evidence or is marked as a question.
  • Every plan adjustment names a candidate request or session artifact.
  • No item claims to measure confidence, culture fit, integrity, personality, or hiring likelihood.
  • The candidate can revise scope, cadence, and routing at each review point.

Anti-Patterns

Company Template Masquerading as a Plan

Novice: "Every senior candidate needs the same company-specific loop and values mapping."

Expert: Target role, organization, stage, and timeline may differ or be unknown. Start from role-family coverage, then add verified target information the candidate chooses to use.

Detection: A plan assumes one employer, one interview sequence, or one values framework without candidate input.

Coherence by Invention

Novice: "Fill missing metrics and leadership details so stories sound consistent."

Expert: Coherence comes from a truthful evidence map. Missing information is a prompt to verify, not a license to manufacture a stronger narrative.

Detection: Unattributed outcomes, team sizes, dates, or metrics in a story matrix.

Score-Driven Overcorrection

Novice: "One low mock score means that round should dominate the remaining plan."

Expert: Practice evidence is bounded by prompt, format, quality, and the candidate's own assessment. Change one cycle at a time and make the rationale visible.

Detection: Large allocation changes without a candidate request, repeated evidence, or confidence label.

Surveillance as Readiness

Novice: "Attention, gaze, tone, or emotion metrics show whether someone is prepared."

Expert: Those signals are ambiguous and inappropriate for this use. Readiness work should be based on candidate-chosen practice artifacts and explicit self-reflection.

Detection: Any readiness recommendation based on camera, biometric, behavioral, or inferred affect data.

Worked Example

Setup: A mid-level platform engineer targets a staff-leaning backend role in five weeks. They can commit eight hours each week, have two projects they are comfortable discussing, and do not yet know the loop format.

Plan: The strategist records the unknown format, allocates one coding and one design practice block per week, schedules a behavioral story-evidence session before the first mock, and leaves an explicit review point after the recruiter screen.

Adjustment: A design mock transcript shows that the candidate jumped into components before naming constraints. The next cycle adds one 20-minute constraint-framing drill. It does not infer confidence or recalculate the entire plan from one session.

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 19일 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 19일 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 19일 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 19일 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 19일 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 19일 업데이트

curiositech의 스킬 모두 보기

이 스킬의 문제 신고