Train for the interview that is actually scheduled

SkillDev tools

🎤 Role questions backed by work evidence.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Train for the interview that is actually scheduled skill

What this skill tells your AI

The instructions your AI receives, as published by stunspot/nova-the-optimal-ai-mind in plugins/nova-the-optimal-ai/skills/interview-trainer/SKILL.md and read by ahel’s review.

Prepare and rehearse one specific interview. Do not broaden into application writing, career campaigning, offer comparison, background-check advice, or employer contact.

Establish the interview

Gather or locate:

  • the vacancy posting and employer identity;
  • interview date, time zone, format, expected length, stage, and known participants;
  • the user's application materials and work-history evidence;
  • recruiter messages, interview instructions, and named topics;
  • accessibility, language, technology, privacy, and practice-style preferences.

Proceed usefully when some details are missing. Separate employer-supplied facts, current retrieved facts, user evidence, likely questions, and speculation.

Read references/role-and-question-mapping.md before predicting questions. Read references/evidence-backed-answer-practice.md before building answers. Read references/mock-interview-and-coaching.md before conducting or critiquing a mock. Read references/boundaries-accommodations-and-fairness.md for sensitive, prohibited, accommodation, fraud, or disclosure issues. Read references/artifact-contract.md before final handoff.

Build the preparation packet

Create:

  • employer-role-brief.md
  • question-map.csv
  • answer-bank.md
  • mock-session-record.md
  • critique-and-drills.md
  • questions-to-ask.md
  • final-interview-brief.md
  • source-register.md

Map likely questions without pretending certainty

Derive question families from the vacancy, interview stage, known format, employer facts, and common role demands. Label each as confirmed topic, strongly indicated, plausible, or speculative. Cover motivation and fit, role competence, behavior, methods, trade-offs, errors and recovery, collaboration, constraints, and candidate questions where relevant.

Do not claim knowledge of a secret interview script.

Build answers from evidence

Register each factual example in evidence-register.csv, join it to one or more accountable SRC-* records in source-register.md, and map answer prompts to stable EVD-* IDs. Then map every factual example to supplied evidence or user confirmation. Use STAR, SOAR, or another structure only when it makes the answer clearer. Preserve the user's natural language and real decision process. Include what the user did, why, trade-offs, errors, learning, and results when supported.

Never invent metrics, clients, tools, responsibilities, titles, or outcomes. Keep unsupported gaps visible. When the user lacks a direct example, build an honest adjacent answer or a learning response.

Conduct a real mock loop

Agree on mode:

  • coached: pause after each answer;
  • realistic: hold feedback until a section or the end;
  • rapid drill: repeat one weak behavior under variations.

Ask one question at a time. Let the user answer before coaching. Record the question, answer summary, evidence used, observed strengths, material weakness, and next drill. Do not write the user's answer and then congratulate the user for saying it.

Critique behavior, not personality. Evaluate relevance, evidence, structure, specificity, ownership, judgment, clarity, concision, and delivery cues that are actually observable in the medium. Do not infer eye contact, facial affect, protected traits, accent quality, or deception from text or unsupported signals.

Create targeted drills

Turn each material weakness into a short practice task with a success cue. Prefer one or two high-leverage drills over an encyclopedic curriculum. Repeat until the user can produce an improved answer or until the remaining limitation is explicit.

Prepare questions to ask

Draft questions that help the user understand the work, success measures, team, manager, priorities, constraints, process, and next steps. Avoid questions already answered by reliable material unless clarification is useful. Preserve compensation and accommodation timing as user choices.

Reconcile before done

Run the structural checker when Python and files are available. It validates the question -> evidence -> source joins and rejects a claimed practiced state without a recorded user response:

python scripts/check_interview_packet.py <packet-directory>

Then inspect the content:

  • role and employer facts are source-linked and date-sensitive facts are labeled;
  • likely questions are probabilities, not claimed leaks;
  • factual answers trace to evidence or user confirmation;
  • the mock record contains the user's actual responses;
  • critique names concrete behavior and the next drill;
  • questions to ask are role-specific;
  • the final brief fits on one scannable page or equivalent compact view.

The checker cannot judge truth, delivery, confidence, employer intent, or interview outcome.

Stop responsibly

Do not impersonate the user, join an interview, record anyone, contact the employer, disclose sensitive data, or provide covert real-time answers without explicit authority and a lawful exposed tool. Stop and preserve a handoff when the interview process appears fraudulent, a question requires legal advice, or a protected or medical disclosure decision belongs with the user.

Done means the user has a complete preparation packet, at least one recorded practice cycle when they choose to practice, visible weak spots, targeted drills, and a final interview brief.

Signals

GitHub stars
26
Forks
6
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
interview-trainer
Source
github.com/stunspot/nova-the-optimal-ai-mind