Menu
OpenAnalyst Challenge Onboarding
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Research-powered creative strategy with visual previews. Use when you need to figure out WHAT to create before creating it. Researches competitors, captures references, generates visual direction previews, and outputs comprehensive briefs. Triggers on: creative strategy, what visuals should I create, creative brief, visual direction. Outputs briefs with actual preview images for feedback.
Execute AI image generation with optimal quality. Use when you need to generate images via Replicate API. Triggers on: generate image, create visual, product shot. Outputs generated images for feedback and iteration.
Specialized skill for AI product photography. Use when you need professional product shots, hero images, lifestyle photography, or e-commerce visuals. Triggers on: product shot, hero image, e-commerce photo. Outputs production-ready product photography.
Specialized skill for AI product video generation. Use when you need product reveals, animated product shots, hero video banners, or e-commerce video content. Triggers on: product video, product reveal, animate product, hero video. Outputs professional product videos.
Specialized skill for social media graphics and thumbnails. Use when you need Instagram posts, YouTube thumbnails, LinkedIn graphics, Twitter images, or ad creatives. Triggers on: social graphic, Instagram post, YouTube thumbnail, LinkedIn image. Outputs platform-optimized graphics.
Specialized skill for AI talking head and lip-sync video generation. Use when you need presenter videos, UGC-style content, or lip-synced avatars. Triggers on: talking head, presenter video, lip sync, UGC video. Outputs professional talking head videos.
Based on SOC occupation classification
| name | challenge-onboarding |
| description | OpenAnalyst Challenge Onboarding |
Dynamic, AI-driven onboarding flow when user creates a new challenge. Questions adapt based on challenge type and user responses.
Based on best practices from The Coaching Tools Company and GoalsWon.
When user clicks "Create Challenge", this skill:
Coach: "What challenge do you want to take on?"
User: "Learn Python"
Coach: "Love it! Let me understand more.
What specifically do you want to achieve with Python?
For example:
- Get a job as a developer
- Automate boring tasks at work
- Build a personal project
- Just curious to learn"
User: "I want to get into data science"
Coach: "Data science with Python - great goal!
Have you done any programming before?"
User: "I know a little JavaScript"
Coach: "Perfect, so not starting from zero. That helps.
Let me ask you something important..."
Coach: "On a scale of 1-10, how committed are you to actually
completing this challenge? Be honest - no judgment."
User: "7"
Coach: "A 7. Okay, what's holding you back from being a 9 or 10?"
User: "I'm worried I won't have enough time with my job"
Coach: "That's a real concern. Let's solve it right now.
Walk me through your typical weekday. When do you wake up,
when do you work, when are you free?"
User: "Wake up 7am, work 9-6, usually free after 8pm"
Coach: "So 8pm to whenever you sleep - that's your window.
How many hours can you realistically commit daily?"
User: "1 hour, maybe 1.5"
Coach: "1 hour is perfect. That's 7 hours per week.
Enough to make real progress.
Now that we've addressed the time concern,
what's your commitment level?"
User: "9"
Coach: "That's what I like to hear. Let's build this plan."
Coach: "Let's get specific. In 30 days, what's the minimum
you'd need to accomplish to feel successful?"
User: "Understand the basics and maybe do a small project"
Coach: "Let me translate that into concrete milestones:
WEEK 1: Python fundamentals (variables, loops, functions)
WEEK 2: Data structures + libraries (NumPy, Pandas)
WEEK 3: Data visualization (Matplotlib, basic analysis)
WEEK 4: Mini-project (analyze a real dataset)
Does that sound right?"
User: "Yes, perfect"
Coach: "Based on your availability:
YOUR SCHEDULE:
โโโโโโโโโโโโโโโโโโโโโโโโโ
Mon: 8:00 PM - 9:00 PM
Tue: 8:00 PM - 9:00 PM
Wed: 8:00 PM - 9:00 PM
Thu: 8:00 PM - 9:00 PM
Fri: 8:00 PM - 9:00 PM
Sat: Flexible (catch-up if needed)
Sun: 8:00 PM - 9:00 PM (week review)
This gives you:
- 6-7 hours/week of learning
- Saturday buffer for life
- Sunday for review and planning
Any adjustments needed?"
User: "Can we skip Friday? I usually have plans"
Coach: "Done. Friday is now your rest day.
Saturday becomes your make-up day if needed."
Coach: "Last step. What happens when you miss a day?
Studies show that having real stakes makes you
3x more likely to succeed. What's your punishment?"
User: "I'll send $50 to my brother"
Coach: "Good. And who's your referee? Someone who will
actually hold you accountable."
User: "My brother"
Coach: "Perfect - he's both the recipient and referee.
He'll get notified when you miss.
๐ YOUR COMMITMENT CONTRACT:
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Challenge: Learn Python for Data Science
Duration: 30 days
Daily: 1 hour (Mon-Thu, Sat-Sun)
Rest: Friday
If I miss: $50 to [Brother]
Referee: [Brother]
Grace period: 24 hours
Type 'I AGREE' to sign this contract."
User: "I AGREE"
Coach: "Contract signed. Your brother has been notified.
Day 1 starts NOW. Your first session is at 8pm tonight.
I'll check in with you then.
Let's do this! ๐"
If score < 8, ask:
If user says "no time":
{
"id": "learn-python-data-science",
"name": "Learn Python for Data Science",
"type": "learning",
"created_at": "2025-12-26T15:00:00Z",
"start_date": "2025-12-26",
"end_date": "2026-01-25",
"duration_days": 30,
"commitment": {
"daily_hours": 1,
"weekly_hours": 6,
"active_days": ["mon", "tue", "wed", "thu", "sat", "sun"],
"rest_days": ["fri"],
"session_time": "20:00"
},
"goals": {
"primary": "Understand Python basics and complete a data science project",
"milestones": [
{"week": 1, "goal": "Python fundamentals"},
{"week": 2, "goal": "NumPy and Pandas"},
{"week": 3, "goal": "Data visualization"},
{"week": 4, "goal": "Mini-project completion"}
]
},
"commitment_score": 9,
"user_concerns": ["time management with job"],
"solutions_discussed": ["8pm-9pm window", "Saturday buffer"]
}
# Weekly Schedule: Learn Python
## Regular Schedule
| Day | Time | Activity |
|-----|------|----------|
| Mon | 8-9 PM | Lesson + practice |
| Tue | 8-9 PM | Lesson + practice |
| Wed | 8-9 PM | Lesson + practice |
| Thu | 8-9 PM | Lesson + practice |
| Fri | - | REST DAY |
| Sat | Flexible | Catch-up/extra practice |
| Sun | 8-9 PM | Week review + next week prep |
## Session Structure (1 hour)
- 0-10 min: Review yesterday
- 10-45 min: New material
- 45-55 min: Practice exercises
- 55-60 min: Log check-in
## Check-in Time
Daily reminder: 9:00 PM
# Milestones: Learn Python
## Week 1: Foundations
- [ ] Variables and data types
- [ ] Conditionals (if/else)
- [ ] Loops (for/while)
- [ ] Functions
- [ ] Mini-exercise: Calculator program
## Week 2: Data Structures
- [ ] Lists and tuples
- [ ] Dictionaries
- [ ] NumPy basics
- [ ] Pandas DataFrames
- [ ] Mini-exercise: Data manipulation
## Week 3: Visualization
- [ ] Matplotlib basics
- [ ] Creating charts
- [ ] Pandas plotting
- [ ] Analyzing a sample dataset
- [ ] Mini-exercise: Visualize data
## Week 4: Project
- [ ] Choose dataset
- [ ] Data cleaning
- [ ] Analysis
- [ ] Visualization
- [ ] Present findings