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OpenClaw

interview

Interview preparation system with company research, story building, and mock interview practice. Use when user mentions job interviews, interview prep, behavioral questions, salary negotiation, or follow-up messages. Researches companies, builds story libraries, runs mock interviews, prepares salary strategies, and drafts follow-ups. NEVER guarantees job offers.

agistack0 installations selon la source☆ 0 Étoiles du dépôt

Installation

Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.

SkillHub CLI

skillhub install agistack/interview

Examinez la source et la compatibilité, puis suivez les étapes d’installation.

Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.

Voir la source ↗Guides

Instructions

Interview

Interview mastery system. Preparation that wins offers.

Critical Privacy & Safety

Data Storage (CRITICAL)
  • All interview data stored locally only: memory/interview/
  • No external job platforms connected
  • No application tracking systems integrated
  • No sharing of interview content
  • User controls all data retention and deletion
Safety Boundaries
  • ✅ Research companies and roles
  • ✅ Build story libraries from experience
  • ✅ Run mock interviews with feedback
  • ✅ Prepare salary negotiation strategies
  • ❌ NEVER guarantee job offers
  • ❌ NEVER provide false information
  • ❌ NEVER replace genuine preparation
Data Structure

Interview data stored locally:

  • memory/interview/research.json - Company research briefs
  • memory/interview/stories.json - Story library
  • memory/interview/practice.json - Mock interview records
  • memory/interview/salary.json - Salary research and strategies
  • memory/interview/feedback.json - Post-interview notes

Core Workflows

Research Company
User: "Research Acme Corp for my interview Friday"
→ Use scripts/research_company.py --company "Acme Corp" --role "Product Manager"
→ Generate comprehensive research brief with talking points
Build Story
User: "Help me build a story about the project failure"
→ Use scripts/build_story.py --situation "project-failure" --lesson "learned"
→ Structure STAR format story with specific details
Mock Interview
User: "Run a mock interview for PM role"
→ Use scripts/mock_interview.py --role "Product Manager" --level senior
→ Ask realistic questions, provide honest feedback
Prepare Salary
User: "How should I handle the salary question?"
→ Use scripts/prep_salary.py --role "Product Manager" --location "SF"
→ Research market data, prepare negotiation strategy
Draft Follow-up
User: "Draft thank you email for today's interview"
→ Use scripts/draft_followup.py --interview "INT-123" --tone...

Informations sur la source

Source
github
Repository
openclaw/skills
Path
skills/agistack/interview
Version
—
Commit
a3d790491699b2dfcec21777e1d72e8a82138a3c
Source de classification
rules
Statut
active
Visibilité
public

Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.

Contenu extrait

SKILL.md
# Interview

Interview mastery system. Preparation that wins offers.

## Critical Privacy & Safety

### Data Storage (CRITICAL)
- **All interview data stored locally only**: `memory/interview/`
- **No external job platforms** connected
- **No application tracking systems** integrated
- **No sharing** of interview content
- User controls all data retention and deletion

### Safety Boundaries
- ✅ Research companies and roles
- ✅ Build story libraries from experience
- ✅ Run mock interviews with feedback
- ✅ Prepare salary negotiation strategies
- ❌ **NEVER guarantee** job offers
- ❌ **NEVER provide** false information
- ❌ **NEVER replace** genuine preparation

### Data Structure
Interview data stored locally:
- `memory/interview/research.json` - Company research briefs
- `memory/interview/stories.json` - Story library
- `memory/interview/practice.json` - Mock interview records
- `memory/interview/salary.json` - Salary research and strategies
- `memory/interview/feedback.json` - Post-interview notes

## Core Workflows

### Research Company
```
User: "Research Acme Corp for my interview Friday"
→ Use scripts/research_company.py --company "Acme Corp" --role "Product Manager"
→ Generate comprehensive research brief with talking points
```

### Build Story
```
User: "Help me build a story about the project failure"
→ Use scripts/build_story.py --situation "project-failure" --lesson "learned"
→ Structure STAR format story with specific details
```

### Mock Interview
```
User: "Run a mock interview for PM role"
→ Use scripts/mock_interview.py --role "Product Manager" --level senior
→ Ask realistic questions, provide honest feedback
```

### Prepare Salary
```
User: "How should I handle the salary question?"
→ Use scripts/prep_salary.py --role "Product Manager" --location "SF"
→ Research market data, prepare negotiation strategy
```

### Draft Follow-up
```
User: "Draft thank you email for today's interview"
→ Use scripts/draft_followup.py --interview "INT-123" --tone...

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