Lesson 7 - Capstone: Build, Validate, and Ship
Apply everything you've learned to deliver a production-grade skill. Validate it with tooling, run a cold test, and publish it for your team or the community.
Duration: 2.5-3 hours
Learning Objectives
By the end of this lesson, you will be able to:
- ✓Build a complete skill with instructions, references, and scripts that aligns to your Lesson 1 charter.
- ✓Validate structure using the skills-ref CLI and behavioral quality using smoke tests.
- ✓Conduct a cold test where another person or AI tool uses the skill without coaching.
- ✓Ship the skill via git and establish a maintenance plan.
Videos
Choosing and Scoping Your Capstone
Select a compelling workflow, scope it realistically, and set clear success criteria for the session.
Duration: 6 minutes
Video coming soon
The End-to-End Build Cycle
Walk through the complete build process with checkpoints, demonstrating the finished deploy-checklist as reference.
Duration: 12 minutes
Video coming soon
Maintenance, Composition, and What's Next
Position the capstone as the beginning — cover maintenance, composing skills, and growing a skill library.
Duration: 6 minutes
Video coming soon
Key Concepts
Capstone Project Options
Build Checklist
Capstone Quality Rubric
Validation Pipeline
Course Summary: Your Journey
Common Mistakes & Pitfalls
❌ Skipping the charter and jumping straight to building
Without a clear problem statement and success metric, you'll build something that 'works' but doesn't solve a real pain. Five minutes on the charter saves hours of unfocused work.
❌ Testing with toy inputs instead of real scenarios
A skill that works with 'Hello World' may fail with real data. Use actual tasks from your workflow — that's where the edge cases live.
❌ Testing only once
One test doesn't catch inconsistencies. Three tests with different inputs reveals whether the skill is truly reliable.
❌ Skipping the cold test
If the skill only works because YOU know the missing context, it's not ready to share. Have someone else use it without any coaching.
❌ Overbuilding before shipping
Ship version 1 when it works, not when it's perfect. Real feedback from real usage is more valuable than hypothetical improvements.
❌ Not establishing a maintenance plan
Skills drift as your workflow evolves. Set a reminder to review your skill quarterly. Update it or retire it — stale skills erode trust.
Exercises
Exercise 1: Build Your Capstone Skill
60 minutesUse the build checklist to create a complete skill from scratch (or finish the one you've been building). Follow the 8-step cycle.
Expected Output:
A complete skill directory with SKILL.md, supporting files, and validated structure.
Success Criteria:
- •SKILL.md has valid frontmatter and all four instruction sections populated.
- •At least one supporting file (reference, script, or asset) included and documented.
- •skills-ref validate passes (or manual validation checklist completed).
- •Supporting Files table present in SKILL.md.
Exercise 2: The Cold Test
30 minutesHand your skill to another person (or a different AI tool) with NO explanation. Can they use it successfully from SKILL.md alone?
Expected Output:
Feedback from the tester: what worked, what was confusing, and suggestions.
Success Criteria:
- •Tester completed the core workflow without verbal coaching from you.
- •Collected specific feedback on at least one confusing or missing element.
- •Applied at least one instruction improvement based on feedback.
- •Re-tested after the improvement to confirm the fix works.
Exercise 3: Ship It
15 minutesCommit your skill to a git repository, open a PR or publish it, and write a short announcement describing the skill.
Expected Output:
Your skill live in a repository, accessible to at least one other person.
Success Criteria:
- •Skill committed to git with a descriptive commit message.
- •PR description (or README) includes: problem solved, how to invoke, test evidence.
- •Compatibility field documents tested platforms.
- •Shared with at least one other person (teammate, community, or public repo).
Lesson Reflection
Take a moment to reflect on what you've learned:
- 1. Compare your Lesson 1 scorecard to your capstone result. How much time does the skill save per week?
- 2. What was the most surprising thing you learned about writing instructions for AI?
- 3. During the cold test, what did the tester struggle with? What does that tell you about your instructions?
- 4. What's the next skill you'll build after this capstone?
- 5. If your entire team adopted skill-based workflows, what would change about how you work together?