techwavedev Interview Coach
Overview
A persistent, adaptive coaching system for the full job search lifecycle.
Not a question bank — an opinionated system that tracks your patterns,
scores your answers, and gets sharper the more you use it. State persists
in coaching_state.md across sessions so you always pick up where you left off.
Install
npx skills add dbhat93/job-search-os
Then type /coach → kickoff.
When to Use This Skill
- Use when starting a job search and need a structured system
- Use when preparing for a specific interview (company research, mock, hype)
- Use when you want to analyze a past interview transcript
- Use when negotiating an offer or handling comp questions on recruiter screens
- Use when building or maintaining a storybank of interview-ready stories
What It Covers
- JD decoding — six lenses, fit verdict, recruiter questions to ask
- Resume + LinkedIn — ATS audit, bullet rewrites, platform-native optimization
- Mock interviews — behavioral, system design, case, panel, technical formats
- Transcript analysis — paste from Otter/Zoom/Grain, auto-detected format
- Storybank — STAR stories with earned secrets, retrieval drills, portfolio optimization
- Comp + negotiation — pre-offer scripting, offer analysis, exact negotiation scripts
- 23 total commands across the full search lifecycle
Examples
Example 1: Start your job search
/coach
kickoff
The coach asks for your resume, target role, and timeline — then builds your profile and gives you a prioritized action plan.
Example 2: Prep for a specific company
/coach
prep Stripe Senior PM
Runs company research, generates a role-specific prep brief, and queues up mock interview questions tailored to Stripe's process.
Example 3: Analyze an interview transcript
/coach
analyze
Paste a raw transcript from Otter, Zoom, or any tool. The coach auto-detects the format, scores each answer across five dimensions, and gives you a drill plan targeting your specific gaps.
Example 4: Handle a comp question
/coach
salary
Coaches you through the recruiter screen "what are your salary expectations?" moment with a defensible range and exact scripts.
Source
https://github.com/dbhat93/job-search-os
AGI Framework Integration
Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills
Memory-First Protocol
Retrieve prior decisions and patterns to avoid re-discovering solutions. Cache results for instant retrieval in future sessions.
# Check for prior development context before starting
python3 execution/memory_manager.py auto --query "prior work and patterns related to Interview Coach"
Storing Results
After completing work, store development decisions for future sessions:
python3 execution/memory_manager.py store \
--content "Completed task with key insights documented for future reference" \
--type decision --project <project> \
--tags interview-coach default
Multi-Agent Collaboration
Share outcomes with other agents so the team stays aligned and avoids duplicate work.
python3 execution/cross_agent_context.py store \
--agent "<your-agent>" \
--action "Task completed — results documented and shared with team" \
--project <project>