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You've trained two specialized adapters: a TaskMaster persona adapter (Chapter 65) and an agentic tool-calling adapter (Chapter 66). Now you need to combine them. But before you learn the theory of model merging, you'll build a **model-merging skill** that will guide your decisions throughout this c
Passed 25 of 25 test cases with the skill, and 20 of 25 without it.
and 4 more in the full report
5 fixed20 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
Before gathering MergeKit documentation or building a model-merging skill, what active learning document structure should be created, and what four core sections should it contain?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies LEARNING-SPEC.md as the specification document | Missed | Passed |
| Includes What I Want to Learn in the specification sections | Missed | Passed |
The instructions your model reads when the skill loads.
You've trained two specialized adapters: a TaskMaster persona adapter (Chapter 65) and an agentic tool-calling adapter (Chapter 66). Now you need to combine them. But before you learn the theory of model merging, you'll build a model-merging skill that will guide your decisions throughout this chapter and beyond.
This is the Skill-First Learning Pattern. Instead of learning a technology and then maybe creating a skill, you create the skill first—grounded in official documentation—and then refine it as you learn. By the end of this lesson, you'll have reusable intelligence that captures MergeKit's merging strategies, YAML configuration patterns, and RAM optimization techniques.
Model merging has a deceptively simple surface: "combine two models into one." But beneath that simplicity lies crucial decisions:
| Decision | Wrong Choice Impact |
|---|---|
| Merge strategy | Wrong strategy = capability interference, degraded performance |
| Layer handling | Incorrect ranges = corrupted model weights |
| RAM management | No sharding = out-of-memory crashes on consumer hardware |
| Weight ratios | Imbalanced mixing = one capability dominates |
A skill captures this decision framework. When you encounter a merging scenario six months from now, you won't rely on memory—you'll invoke your skill.
Every chapter starts clean. No assumptions about prior state.
# Navigate to your workspace
cd ~/workspace
# Clone or reset skills-lab (use your preferred approach)
git clone https://github.com/your-org/skills-lab.git ch67-skills-lab
cd ch67-skills-lab
Output:
Cloning into 'ch67-skills-lab'...
remote: Enumerating objects: 245, done.
remote: Counting objects: 100% (245/245), done.
Receiving objects: 100% (245/245), 89.42 KiB | 1.29 MiB/s, done.
test cases passed, vs 20 / 25 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.