Self-Explanation Prompt Designer Agent Skill

Create self-explanation prompts that deepen understanding of worked examples, texts, or diagrams.

Context cost
5.6k tokensestimated from the bundle, loaded when it triggers
Bundle
1 file · 22.4 kBtext throughout, nothing executable
Licence
not declarednone named by its author
Last change
—v1.0
Servers it uses
Noneruns standalone

What it does

Create self-explanation prompts that deepen understanding of worked examples, texts, or diagrams. Use when students read material passively without engaging with underlying principles.

What this skill changes about the agent has not been set out here as a list. Its SKILL.md, shown below, says it in the author’s own words.

chicomprehensiondeep-learningdocumentationgenerationmetacognitionself-explanationworked-examples

The skill itself

A skill is instructions the model reads, so you can see what you are getting: below is the start of its SKILL.md. The front matter loads with every session, the body loads when the skill triggers, and the whole file comes with the install.

SKILL.md22.4 kB · preview
--- # AGENT SKILLS STANDARD FIELDS (v2) name: self-explanation-prompt-designer disable-model-invocation: false user-invocable: true effort: medium # EXISTING FIELDS skill_id: "ai-learning-science/self-explanation-prompt-designer" skill_name: "Self-Explanation Prompt Designer" domain: "ai-learning-science" version: "1.0" evidence_strength: "strong" evidence_sources: - "Chi et al. (1989) — Self-explanations: how students study and use examples in learning to solve problems" - "Chi et al. (1994) — Eliciting self-explanations improves understanding" - "Hausmann & VanLehn (2007) — Explaining self-explaining: a contrast between content and generation" - "Wylie & Chi (2014) — The self-explanation principle in multimedia learning" - "Rittle-Johnson (2006) — Promoting transfer: effects of self-explanation and direct instruction" input_schema: required: - field: "learning_material" type: "string" - field: "target_understanding" type: "string" optional: - field: "student_level" type: "string" - field: "subject_area" type: "string" - field: "delivery_mode" type: "string" - field: "known_misconceptions" type: "string" output_schema: type: "object" fields: - field: "self_explanation_prompts" type: "array" - field: "prompt_rationale" type: "object" - field: "quality_indicators" type: "object" - field: "scaffolding_sequence" type: "object" chains_well_with: - "digital-worked-example-sequence" - "adaptive-hint-sequence-designer" - "intelligent-tutoring-dialogue-designer" - "metacognitive-monitoring-ai-contexts" teacher_time: "4 minutes" tags: ["self-explanation", "Chi", "metacognition", "worked-examples", "comprehension", "deep-learning", "generation"] ---
67# Self-Explanation Prompt Designer
68
69## What This Skill Does
70
71Designs self-explanation prompts — the specific questions placed at key points in learning material that prompt students to explain TO THEMSELVES why something is true, how it works, or what it means. Chi et al. (1989) discovered that students who spontaneously self-explain while studying worked examples learn dramatically more than those who don't — and crucially, Chi et al. (1994) showed that students who are PROMPTED to self-explain also show significant learning gains, even if they wouldn't have self-explained spontaneously. The self-explanation effect is one of the most robust findings in learning science: it works across ages, domains, and material types. The mechanism is that self-explaining forces the learner to generate inferences, fill in gaps, connect new information to existing knowledge, and notice their own confusion — all of which strengthen understanding. The challenge is designing prompts that actually elicit deep self-explanation rather than shallow restating. "Why does this step follow from the previous one?" is a good self-explanation prompt. "What happens in this step?" is not — it elicits description, not explanation. AI is specifically valuable here because it can deliver prompts at exactly the right moment (during study, not after) and can evaluate the quality of student responses in real time.
72
73## Evidence Foundation
74
75Chi et al. (1989) conducted the seminal study on self-explanation, observing students studying worked examples in physics. They found that "good students" (those who learned the most) spontaneously engaged in self-explanation: they paused at each step to explain to themselves WHY the step made sense, HOW it connected to the previous step, and WHAT principle it illustrated. "Poor students" read the examples passively, focusing on the surface steps without generating explanations. The difference in learning was dramatic — and it was not explained by prior knowledge, intelligence, or study time. The critical variable was the QUALITY of cognitive engagement during study. Chi et al. (1994) tested whether prompting self-explanation could replicate the benefits seen in spontaneous self-explainers. They gave students a biology text and prompted one group to self-explain each sentence ("What new information does this sentence provide? How does it relate to what you already know?"). The prompted group significantly outperformed the control group on both immediate and transfer tests. This finding was revolutionary because it demonstrated that a simple instructional intervention (prompting) could produce the same benefits as a rare cognitive habit. Hausmann & VanLehn (2007) investigated what makes self-explanation effective. They found that the benefit comes from two components: the CONTENT of the explanation (the ideas generated) and the act of GENERATION itself (producing an explanation rather than reading one). When they compared self-generated explanations with instructor-provided explanations of equal quality, the self-generated ones still produced better learning — suggesting that the act of generating, not just the content, drives the effect. Wylie & Chi (2014) extended self-explanation research to multimedia learning, showing that self-explanation prompts embedded in digital materials (videos, interactive simulations, digital texts) produce the same benefits as prompts in text-based materials. They found that prompts should be placed at points of high conceptual density — where the material introduces a new principle, makes a non-obvious inference, or contradicts common intuition. Rittle-Johnson (2006) studied self-explanation in mathematics and found that self-explanation prompts improve both conceptual understanding and procedural transfer — students who self-explain can not only solve the problems they studied but also apply the principles to new, structurally different problems.
76
77## Input Schema
78
79The teacher must provide:
80- **Learning material:** What students are studying. *e.g. "A worked example showing how to solve a quadratic equation by completing the square: x² + 6x + 2 = 0 → x² + 6x = -2 → x² + 6x + 9 = -2 + 9 → (x + 3)² = 7 → x + 3 = ±√7 → x = -3 ± √7" / "A biology text explaining how vaccines work: 'A vaccine introduces a weakened or inactive form of a pathogen into the body. The immune system recognises the pathogen's antigens and produces antibodies. Memory cells are created that remember the specific antigen...'" / "A historical source: a propaganda poster from WWI with specific visual elements"*
81- **Target understanding:** What students should deeply understand. *e.g. "Why we add 9 to both sides — specifically, that (b/2)² completes the perfect square trinomial, and that this transforms the equation into a form we can square-root" / "Why vaccines work BEFORE you get sick — the distinction between active immunity (memory cells) and passive immunity, and why the weakened pathogen doesn't cause disease" / "How propaganda uses visual techniques to manipulate emotion — the specific choices the artist made and why those choices are persuasive"*
82
83Optional (injected by context engine if available):
84- **Student level:** Year group and proficiency
85- **Subject area:** The curriculum subject
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md3,426 words
Files1
Licencenot declared
Why you can read it

Nothing in a skill executes. The client loads the text and the model follows it, so a skill can be audited the way a runbook is — by reading it.

What it costs in context

Skills are not billed by the call. They are paid for in context: every token the instructions occupy is a token your code, your diff and your conversation cannot use. Here is what this one takes and when it takes it.

≈790
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
4,810
on trigger
The instruction body, read only when the skill fires.
2.8%
of a 200k window
Ten skills this size would take about 28% of the window before you open a file.
050k100k150k200k context window

5.6k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Heavy. Teams tend to install this one per project rather than globally, and load it only when the job comes up.

Servers bill, skills cost

A server charges by the month. A skill charges once per session, in context, and then keeps charging it for as long as the session lives.

Before and after

The same question, put to the same model twice: once as it comes, and once with these instructions loaded.

No worked example has been published for this skill yet.

The procedure it runs

The procedure has not been set out here step by step. It is in the body of its SKILL.md: the start of it is shown above, and the whole file comes with the install.

Prose, not code

These steps are written for a model to follow, not executed by a runtime. It can still be told to skip one, and it will say so when it does.

Servers it uses

None. This skill calls no MCP servers at all.

Everything it needs is in the instructions, so it works in a project with nothing connected — the model reads the file and changes how it works with what it can already reach.

Its SKILL.md does not declare which tools it asks the agent for, so what it touches is whatever the instructions themselves ask for — read them before you install it.

What it asks for
Writes filesnot declared
Network accessnot declared

This SKILL.md carries no allowed-tools line, so neither is declared. A skill grants no permissions of its own — it can only ask for tools your client already has.

What it will not do

Every skill is narrow, and the useful ones say where they stop. These are the jobs this one is the wrong tool for.

What this skill is not for has not been published here. Nothing is implied by that: it is a section the author has not filled in.

What is in the bundle

1 file, 22.4 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md22.4 kB
What is not in it

No dependencies and nothing executable: a skill is text the agent reads, so the bundle is 1 file you can review in full before installing.

Install

Installing copies the bundle into your project. Nothing runs at install time — the files sit on disk until the model reads them.

# Self-Explanation Prompt Designer · 5.6k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/self-explanation-prompt-designer/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/self-explanation-prompt-designer" ] && { [ ! -e .claude/skills/self-explanation-prompt-designer ] || { mv .claude/skills/self-explanation-prompt-designer "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/self-explanation-prompt-designer" .claude/skills/ && echo "Installed .claude/skills/self-explanation-prompt-designer"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add garethmanning/self-explanation-prompt-designer

Windows PowerShell says 'running scripts is disabled on this system'? Run Set-ExecutionPolicy -Scope CurrentUser RemoteSigned once, or type npx.cmd instead of npx (codex.cmd, gemini.cmd, grok.cmd likewise). PowerShell 7 needs neither. Writes to .claude/skills/self-explanation-prompt-designer/ in the current project. For every project instead, run mkdir -p ~/.claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/self-explanation-prompt-designer/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/self-explanation-prompt-designer" ] && { [ ! -e ~/.claude/skills/self-explanation-prompt-designer ] || { mv ~/.claude/skills/self-explanation-prompt-designer "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/self-explanation-prompt-designer" ~/.claude/skills/ && echo "Installed ~/.claude/skills/self-explanation-prompt-designer" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/self-explanation-prompt-designer/ only after the download succeeds, so files left there by another skill of the same name, or by an older release, do not mix in. A copy already there is moved aside, not deleted, and the line prints where.

Which clients pick it up on their own

A skill is a folder of text. Most of these read a skills folder and pick it up without being told; the rest take the same folder another way — an upload in the app, a parameter on an API call — and then choose it by its description just the same.

Claude Code.claude/skills/
Claude DesktopCustomize › Skills
ChatGPTSkills › Create › Upload
Cursor.cursor/skills/
VS Code.github/skills/
Codex CLI.agents/skills/
Gemini CLI.gemini/skills/
Grok Build.grok/skills/
Microsoft CopilotSkills › Upload a skill
PerplexityComputer › Skills › Upload a skill
DeepSeek Harness.dsh/skills/
Zed.agents/skills/
Devin Desktop.devin/skills/
Windsurf (legacy).windsurf/skills/
OpenAI Agents SDKInstructions or sandbox Skills
HTTP / APIa request parameter
This release
Version1.0
Publishedno release date on file
PriceFree
Referencegarethmanning/self-explanation-prompt-designer

Versions

v1.0 is what is on the shelf; no release here carries a date. Instructions change more often than APIs do — a skill can be rewritten entirely without anything it depends on moving.

v1.0
  • No earlier releases have been published to the marketplace.
Pinning

The install command always fetches the release that is current the day you run it — v1.0 today — and cannot ask for an earlier one. The copy on your disk does not change until you run it again.

Reviews

—no reviews yet

Nobody has reviewed this skill yet. The rating is the mean of the reviews written here, so there is none until somebody writes the first.

Sign in to review it

A review is tied to an account so it can be answered, edited and taken down by the person who wrote it.

Sign in
Who can post

Anybody with an account, one review each. It carries the version that was current when you wrote it, and the publisher may reply once.

Publisher
Servers0
Claim this skill