--- name: interview-loop-strategist-13f296a84c18 description: Orchestrates end-to-end interview preparation for senior ML/AI engineers targeting Anthropic and peer companies. Use for prep timeline generation, story coherence across rounds, mock scheduling, and debrief analysis. Activate on "interview prep", "interview loop", "Anthropic interview", "prep timeline". NOT for resume writing, career narratives, or individual round-type practice. allowed-tools: Read,Write,Edit metadata: gated: true category: Career & Interview pairs-with: - skill: senior-coding-interview reason: Handles coding round preparation and practice - skill: ml-system-design-interview reason: Handles ML system design round deep practice - skill: anthropic-technical-deep-dive reason: Handles Anthropic-specific technical deep dive rounds - skill: tech-presentation-interview reason: Handles technical presentation round preparation - skill: values-behavioral-interview reason: Handles behavioral and values-fit round preparation - skill: hiring-manager-deep-dive reason: Handles hiring manager screen preparation - skill: interview-simulator reason: Runs mock interviews with realistic evaluation - skill: career-biographer reason: Upstream -- extracts career narrative raw material - skill: cv-creator reason: Upstream -- resume must be finalized before loop begins tags: - interview - preparation - strategy - anthropic --- # Interview Loop Strategist End-to-end orchestrator for senior-level AI/ML interview preparation. Coordinates timelines, story coherence across rounds, mock interview cadence, energy management, and post-interview debrief -- routing each round type to its specialist skill. ## When to Use **Use for:** - Building a complete interview prep plan for a specific company - Generating 2-week, 1-month, or 2-month preparation timelines - Ensuring story coherence -- the same project told correctly across behavioral, technical, and HM rounds - Scheduling and tracking mock interview cadence - Energy management strategy for all-day virtual or onsite loops - Post-interview debrief analysis and improvement planning - Coordinating across all 7 round-specific interview skills **NOT for:** - Resume or CV creation (use `cv-creator`) - Career narrative extraction (use `career-biographer`) - Practicing a single round type in isolation (use the round-specific skill directly) - Salary negotiation or offer evaluation - General career counseling --- ## Full Interview Pipeline ```mermaid flowchart TD A[Career Biographer] -->|Extracts narrative| B[CV Creator] B -->|Resume finalized| C[Interview Loop Strategist] C --> D{Company Target Selected} D --> E[Generate Prep Timeline] E --> F[Story Coherence Matrix] F --> G[Round-Specific Prep] G --> G1[Recruiter Screen
Self-prep] G --> G2[CodeSignal / Coding
senior-coding-interview] G --> G3[Hiring Manager Screen
hiring-manager-deep-dive] G --> G4[ML System Design
ml-system-design-interview] G --> G5[Technical Deep Dive
anthropic-technical-deep-dive] G --> G6[Tech Presentation
tech-presentation-interview] G --> G7[Values & Behavioral
values-behavioral-interview] G1 --> H[Mock Interviews
interview-simulator] G2 --> H G3 --> H G4 --> H G5 --> H G6 --> H G7 --> H H --> I[Debrief & Adjust] I -->|Iterate| G I --> J[Interview Day
Energy Protocol] J --> K[Post-Loop Debrief] K --> L{Offer?} L -->|Yes| M[Negotiation Phase] L -->|No| N[Gap Analysis & Retry] N -->|Update plan| E ``` --- ## Skill Routing Table Each round type maps to a specialist skill. The strategist coordinates -- it does not execute round-specific practice. | Round Type | Specialist Skill | Key Focus | |------------|-----------------|-----------| | Recruiter Screen | Self-prep (no skill needed) | Pitch, motivation, logistics, salary range | | Online Assessment / Coding | `senior-coding-interview` | LC hard, system design lite, time management | | Hiring Manager Screen | `hiring-manager-deep-dive` | Leadership, team fit, technical judgment | | ML System Design | `ml-system-design-interview` | End-to-end ML pipelines, tradeoffs, scale | | Technical Deep Dive | `anthropic-technical-deep-dive` | Past work forensics, technical depth, AI safety | | Tech Presentation | `tech-presentation-interview` | 45-min talk, audience calibration, Q&A | | Values / Behavioral | `values-behavioral-interview` | STAR stories, Anthropic values alignment | | Mock Execution | `interview-simulator` | Realistic timed practice with scoring | --- ## Prep Timeline Selection Choose based on time until first round: ```mermaid flowchart LR T{Time Available?} T -->|< 2 weeks| P1[Intensive Plan
|2-5 weeks| P2[Balanced Plan
|6+ weeks| P3[Thorough Plan
R[See references/
preparation-timeline-templates.md] P2 --> R P3 --> R ``` For detailed daily schedules, consult `references/preparation-timeline-templates.md`. --- ## Story Coherence Matrix A senior candidate has 5-8 strong projects. Each project will surface in multiple rounds but must be tailored to the audience and evaluation criteria of that round. ### How to Build the Matrix 1. **List top 5 projects** from career-biographer output (or direct input) 2. **For each project, write 3 versions:** | Version | Round Type | Emphasis | Length | |---------|-----------|----------|--------| | **Technical** | ML Design, Deep Dive | Architecture decisions, tradeoffs, metrics, what you would change | 8-12 min | | **Impact** | Behavioral, HM | Leadership, influence, collaboration, business outcome | 3-5 min (STAR) | | **Narrative** | Presentation, Recruiter | Story arc, why it matters to the world, lessons learned | Variable | 3. **Cross-check for contradictions** -- dates, team sizes, your role, metrics must be identical across versions 4. **Map projects to Anthropic values** -- which project demonstrates which value (safety, honesty, broad benefit)? ### Example Coherence Entry **Project: Real-Time Object Detection Pipeline (2019-2022)** | Round | Version | Key Points | |-------|---------|------------| | ML Design | Technical | YOLOv5 -> custom architecture, 40ms latency constraint, edge deployment, model distillation tradeoffs | | Deep Dive | Technical | Why ResNet backbone over EfficientNet, quantization strategy, failure mode analysis, production monitoring | | Behavioral | Impact | Led 4-person team through 3 pivots, managed stakeholder expectations when accuracy targets slipped, mentored junior engineer who became tech lead | | HM Screen | Impact | Drove 35% revenue increase through automation, navigated org politics to get GPU budget, built cross-functional relationships | | Presentation | Narrative | "From research prototype to production system serving 10M requests/day -- lessons in making ML real" | --- ## Energy Management Protocol All-day interview loops (4-6 hours) are endurance events. Cognitive fatigue causes more failures than knowledge gaps. ### Before Interview Day - Sleep 7-8 hours for 3 consecutive nights prior (not just the night before) - Prepare environment: quiet room, backup internet, charged devices, water, snacks - Do NOT cram the morning of -- review only your story coherence matrix and 1-page cheat sheet - Light exercise the morning of (walk, stretch -- not a hard workout) ### During the Loop | Break Length | Activity | Avoid | |-------------|----------|-------| | 5 min | Stand, stretch, water, deep breaths | Phone, social media, reviewing notes | | 15 min | Walk, snack (protein > sugar), bathroom | Replaying the previous round | | 30+ min (lunch) | Eat a real meal, step outside, reset | Studying for next round | ### Cognitive Sequencing If you can influence round order (sometimes companies ask preference): 1. **Start with your strongest round** -- builds confidence momentum 2. **Put coding early** -- requires peak cognitive freshness 3. **Behavioral/values in the middle** -- these are conversational and let you recover 4. **Presentation whenever you rehearsed it** -- muscle memory carries you 5. **Avoid technical deep dive as last round** -- fatigue makes it easy to ramble --- ## Debrief Framework Run after every mock AND every real interview round. ### Immediate (within 30 minutes) 1. **Dump raw notes** -- what questions were asked, what you said, what you wish you said 2. **Emotional check** -- how did you feel? Confident, uncertain, surprised? 3. **Time check** -- did you run over? Under? Where did you lose time? ### Structured Analysis (within 24 hours) | Dimension | Score (1-5) | Evidence | Action Item | |-----------|-------------|----------|-------------| | Technical accuracy | | | | | Communication clarity | | | | | Time management | | | | | Story coherence | | | | | Energy / confidence | | | | | Question handling | | | | ### Pattern Detection (weekly) - What round types consistently score lowest? - Which stories land well? Which fall flat? - Are you improving on last week's action items? - Adjust prep timeline allocation based on weakness trends For scoring rubrics per round type, consult `references/mock-interview-rubrics.md`. --- ## Anti-Patterns ### Anti-Pattern: Uniform Preparation **Novice**: Spends equal time on every round type -- 2 hours coding, 2 hours design, 2 hours behavioral, repeat. **Expert**: Analyzes personal weaknesses and round weighting. A candidate who aces design but freezes in coding allocates 60% of prep to coding. A candidate whose stories are inconsistent spends dedicated time on the coherence matrix. **Detection**: Prep log shows identical hours across all categories despite known weaknesses. ### Anti-Pattern: Isolation Prep **Novice**: Prepares each round independently. Tells a behavioral story about leading a team of 6 in one round, then says "I was the sole contributor" for the same project in a technical round. **Expert**: Uses the story coherence matrix to thread a consistent narrative across all rounds. Reviews the matrix before every mock. Has a peer check for contradictions. **Detection**: Same project described with conflicting details (team size, timeline, your role, metrics) across different round types. ### Anti-Pattern: Mock Avoidance **Novice**: Reads interview guides, watches YouTube videos, reviews flashcards -- but never actually practices speaking answers aloud under time pressure. **Expert**: Runs at minimum 2 full mock interviews per week in the final month. Records mocks. Reviews recordings. Uses `interview-simulator` for structured scoring. Treats mocks as the primary prep activity, not supplementary. **Detection**: Zero mock session recordings in history. Unable to answer questions within time limits despite "knowing the material." --- ## Anthropic-Specific Notes Anthropic's interview process (as of early 2026) emphasizes: 1. **AI Safety understanding** -- not just technical competence, but genuine engagement with alignment, interpretability, and responsible deployment 2. **Technical depth over breadth** -- they want to see how deep you can go on your own work, not surface-level familiarity with everything 3. **Collaborative problem-solving** -- interviews are designed to feel like working sessions, not interrogations 4. **Intellectual honesty** -- saying "I don't know" or "I was wrong about that" is valued over bluffing 5. **Mission alignment** -- why Anthropic specifically, not just "any AI company" For detailed company-specific loop structures (Anthropic, Google DeepMind, OpenAI, Meta FAIR), consult `references/company-specific-loops.md`. --- ## Process: First Session with a Candidate 1. **Assess current state**: Which company? When is the interview? What's your background? 2. **Review upstream artifacts**: career-biographer output, cv-creator resume, any existing prep 3. **Select timeline template**: 2-week / 1-month / 2-month based on available time 4. **Build story coherence matrix**: Top 5 projects x 3 versions each 5. **Identify weakness areas**: Self-assessment + any prior interview feedback 6. **Generate personalized prep plan**: Daily schedule with skill routing 7. **Schedule first mock**: Within 48 hours of starting prep 8. **Set debrief cadence**: After every mock, weekly pattern review --- ## Reference Files | File | Consult When | |------|-------------| | `references/preparation-timeline-templates.md` | Generating a daily prep schedule for 2-week, 1-month, or 2-month timeline | | `references/mock-interview-rubrics.md` | Scoring mock interviews, self-evaluation, or identifying failure modes per round type | | `references/company-specific-loops.md` | Tailoring prep to a specific company's interview structure (Anthropic, DeepMind, OpenAI, Meta FAIR) | --- Source attribution: curiositech Original project: https://github.com/curiositech/some_claude_skills/blob/f49825290077e5fb46bb3e867dfa8a5b0161152b/.claude/skills/interview-loop-strategist/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2025 Erich Owens Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: curiositech Original project: https://github.com/curiositech/some_claude_skills/blob/f49825290077e5fb46bb3e867dfa8a5b0161152b/.claude/skills/interview-loop-strategist/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering, Human Resources, Product Management Roles: Growth Product Manager, HR Generalist, ML / AI Engineer, Product Manager, Technical Product Manager Specialties: Specialty not specified Tools: Tool not specified Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: dfe870d5cb38dd5c1e941f4291e2bfce08bc578a037f0b8da9a9a2cdf4097aa3