interview

name	interview
description	Generate company-specific interview intelligence including process overview, audience-mapped questions, story bank mapping, and technical prep checklist. Triggers on interview prep, interview prep, interview for, prep for interview.

Interview — Company-Specific Interview Intelligence

Read ~/.openclaw/workspace/skills/career-ops/references/scoring-system.md for archetype detection.

Data Paths

• CV: ~/.openclaw/workspace/career-ops-data/cv.md
• Profile: ~/.openclaw/workspace/career-ops-data/config/profile.yml
• Reports: ~/.openclaw/workspace/career-ops-data/reports/
• Story bank: ~/.openclaw/workspace/career-ops-data/interview-prep/story-bank.md
• Output: ~/.openclaw/workspace/career-ops-data/interview-prep/{company-slug}-{role-slug}.md

Workflow

Step 1 — Research

Run WebSearch queries grouped by audience:

Recruiter / HR screen:

• "{company} {role} salary" site:levels.fyi and site:glassdoor.com/Salary
• "{company} interview process site:glassdoor.com"
• "{company} site:teamblind.com" comp negotiation OR offer
• "{company} careers" + "{company} benefits"

Hiring manager / leadership:

• "{company} engineering blog" and "{company} {team} blog"
• "{company}" news OR launch OR roadmap
• "{company} {role} interview process"

Peer / technical panel:

• "{company} {role} interview questions site:glassdoor.com"
• "{company} {role} interview site:leetcode.com/discuss"
• "{company} interview process site:teamblind.com"

NEVER invent interview questions. Inferred questions must be tagged [inferred from JD]. Cite all sources.

Step 2 — Process Overview

Summary: rounds, timeline, difficulty rating, positive experience rate, known quirks. If data insufficient, write "unknown" rather than guessing.

Step 2.5 — Audience Map

Classify each round: recruiter-screen, hiring-manager, peer-tech, panel-mixed.

Step 3 — Round-by-Round Breakdown

For each round: duration, conducted by, what they evaluate, reported questions with sources, how to prepare.

Step 4 — Likely Questions (per audience)

Group questions by audience. Draft candidate-specific answers using CV and profile. Each audience gets tailored prep:

• Recruiter: CV walkthrough, comp, why this company, logistics
• Hiring Manager: motivation + scope fit, 90-day plan, leadership questions
• Peer-tech: technical depth, domain-specific, reverse questions
• Panel-mixed: framing for all audiences, hand-off discipline

Step 5 — Story Bank Mapping

Map stories from story-bank.md to each audience. Identify gaps and suggest experiences from CV that could become STAR+R stories.

Step 6 — Technical Prep Checklist

Max 10 items, prioritized by frequency and relevance.

Step 7 — Company Signals (per audience)

What to say, do, and avoid — segmented by who's listening. Different framing for recruiter, HM, peer, and mixed panel.

Output

Save to interview-prep/{company-slug}-{role-slug}.md with header: Company, Role, URL, Legitimacy tier, Report link, Date, Sources count, Audiences covered.

Post-Research

1. Ask if user wants to draft stories for gaps
2. Suggest running research mode if company research was thin
3. If interview date known, suggest setting a review reminder