Interview Preparation
REQUIRED BACKGROUND: Use career-state-protocol. Use the current T### Target Model from jd-company-research and stories from story-bank-builder.
Goal
Build a stage-specific preparation pack that aligns what the target is likely to test with what the candidate can prove and say naturally.
Mandatory inputs
Collect these before a final prep pack:
- company, role, and actual JD or Target Model;
- candidate Career Profile, project inventory, or resume;
- submitted resume version when one exists;
- interview stage, format, date, and language;
- known interviewer roles and prior-round feedback when available.
Ask the highest-value missing item one question at a time. Under time pressure, produce a clearly provisional priority brief while collecting the remaining inputs.
Workflow
- Refresh time-sensitive target facts if the existing research may be stale.
- Read the
T### capability model and infer the purpose of this stage.
- Run consistency checks between the submitted resume and Career State.
- Build a question map from target evidence, candidate gaps, resume claims, prior feedback, interviewer function, and stage.
- Build a story map with primary story, backup story, claim support, and follow-up risk.
- Route weak project evidence to
project-deep-dive and missing coverage to story-bank-builder.
- Prepare answer theses, not invented scripts, for questions with supported evidence.
- Add skeptical follow-ups for ownership, attribution, tradeoffs, AI depth, failure, adoption, and scale as relevant.
- Create a training plan ordered by expected interview impact and remaining time.
Do not generate a generic question dump before building the target and story maps. Read references/prep-pack.md and references/question-mapping.md.
Stage behavior
- Recruiter or first screen: positioning, timeline, motivation, communication, and basic fit.
- Hiring manager: ownership, judgment, collaboration, impact, and role-specific depth.
- Project or technical discussion: architecture, product logic, alternatives, evaluation, failure modes, and defensible implementation detail.
- Case or open problem: assumptions, structure, decisions, metrics, risk, and adaptation.
- Final or panel: unresolved concerns, consistency, maturity, values in action, and questions for the team.
These are starting hypotheses. The actual JD, company research, interviewer, and prior feedback take priority.
Output rules
Make every prepared claim traceable to allowed C### items. Label predictions as inference. Match the requested interview language. End with one concrete next drill, usually the weakest high-priority question or riskiest flagship story.
1---2name: interview-prep3description: Use when preparing for a specific interview opportunity, round, interviewer, panel, case, project deep dive, technical discussion, behavioral interview, or hiring-manager conversation.4---56# Interview Preparation78**REQUIRED BACKGROUND:** Use `career-state-protocol`. Use the current `T###` Target Model from `jd-company-research` and stories from `story-bank-builder`.910## Goal1112Build a stage-specific preparation pack that aligns what the target is likely to test with what the candidate can prove and say naturally.1314## Mandatory inputs1516Collect these before a final prep pack:1718- company, role, and actual JD or Target Model;19- candidate Career Profile, project inventory, or resume;20- submitted resume version when one exists;21- interview stage, format, date, and language;22- known interviewer roles and prior-round feedback when available.2324Ask the highest-value missing item one question at a time. Under time pressure, produce a clearly provisional priority brief while collecting the remaining inputs.2526## Workflow27281. Refresh time-sensitive target facts if the existing research may be stale.292. Read the `T###` capability model and infer the purpose of this stage.303. Run consistency checks between the submitted resume and Career State.314. Build a question map from target evidence, candidate gaps, resume claims, prior feedback, interviewer function, and stage.325. Build a story map with primary story, backup story, claim support, and follow-up risk.336. Route weak project evidence to `project-deep-dive` and missing coverage to `story-bank-builder`.347. Prepare answer theses, not invented scripts, for questions with supported evidence.358. Add skeptical follow-ups for ownership, attribution, tradeoffs, AI depth, failure, adoption, and scale as relevant.369. Create a training plan ordered by expected interview impact and remaining time.3738Do not generate a generic question dump before building the target and story maps. Read `references/prep-pack.md` and `references/question-mapping.md`.3940## Stage behavior4142- Recruiter or first screen: positioning, timeline, motivation, communication, and basic fit.43- Hiring manager: ownership, judgment, collaboration, impact, and role-specific depth.44- Project or technical discussion: architecture, product logic, alternatives, evaluation, failure modes, and defensible implementation detail.45- Case or open problem: assumptions, structure, decisions, metrics, risk, and adaptation.46- Final or panel: unresolved concerns, consistency, maturity, values in action, and questions for the team.4748These are starting hypotheses. The actual JD, company research, interviewer, and prior feedback take priority.4950## Output rules5152Make every prepared claim traceable to allowed `C###` items. Label predictions as inference. Match the requested interview language. End with one concrete next drill, usually the weakest high-priority question or riskiest flagship story.
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