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theme, title, info, author, transition, mdc, lineNumbers, monaco, aspectRatio, canvasWidth, layout, class
| theme | title | info | author | transition | mdc | lineNumbers | monaco | aspectRatio | canvasWidth | layout | class |
|---|---|---|---|---|---|---|---|---|---|---|---|
| seriph | Lesson 08 — How Can an Agent Know What It Needs to Learn? | English video course for AI Agents in Depth | Bojie Li | slide-left | true | false | false | 16/9 | 980 | cover | cover |
Build · Chapter 2 · Context Engineering
How Can an Agent Know What It Needs to Learn?
Skills, progressive disclosure, and on-demand capability
Lesson 08 of 42 · 18 minutes · Dynamic Prompts and Agent Skills; Skills and Tools
layout: center class: text-center
The central question
How can an Agent access hundreds of specialist procedures without carrying all of them in every prompt?
Why this problem matters
Discovery
A thin index tells the Agent what knowledge exists.
Disclosure
Detailed instructions load only after a relevant trigger.
Execution
Bundled scripts make repeated procedures deterministic.
Three ideas to keep in view
Skill metadata
Name and description stay visible
Skill body
Workflow loads only when selected
Resources
References and scripts load only when required
The book's visual model
Skills progressive disclosure
Everything preloaded vs. Progressive disclosure
Everything preloaded
- Large static prompt
- High information competition
- Every task pays the cost
Progressive disclosure
- Thin capability index
- On-demand instructions
- Task-specific context
Skills trade metacognition risk for context efficiency.
A Skill is a navigable package
pptx/
├── SKILL.md # when and how
├── reference.md # details on demand
├── scripts/
│ └── render.py
└── templates/
Test the claim
2-63 min
Generate a deck through progressive Skill loading
Observe: Metadata → Skill body → referenced script → verified artifact
Demo budget: 3 minutes · one contiguous terminal block
class: course-terminal
Live demo
Switching to the terminal
$ uv run python chapter2/agent-skills-ppt/demo.py --offline
Run the command(s), narrate decisions, and point to the observation—not just the output.
What the evidence supports
Finding 1
The Agent initially sees a capability index rather than full instructions.
Finding 2
File reads turn specialist knowledge into explicit trajectory events.
Finding 3
Scripts reduce token use and make artifact creation testable.
layout: center
Where the claim stops
Boundary condition
Progressive disclosure fails when the model does not recognize that a Skill is relevant.
layout: center
Engineering takeaway
Design rule
Keep capability descriptions broad enough for discovery and Skill bodies narrow enough for reliable execution.
Continue the experiment
Official Skill runtime paths
chapter2/agent-skills-ppt/run_official_experiment.py
Skill-enabled trajectory
book-en/images/fig2-12.svg
Skills versus tools
book-en/chapter2.md
layout: center class: text-center
Pause and apply
Your turn
How would you measure false-negative Skill discovery?
layout: center class: text-center
Next · Lesson 09
Long tasks need explicit state and selective forgetting, not only selective loading.
→