Digital Worked Example Sequence Agent Skill

Create an interactive digital worked example sequence with fading for online or blended delivery.

Context cost
5.3k tokensestimated from the bundle, loaded when it triggers
Bundle
1 file · 21.1 kBtext throughout, nothing executable
Licence
not declarednone named by its author
Last change
—v1.0
Servers it uses
Noneruns standalone

What it does

Create an interactive digital worked example sequence with fading for online or blended delivery. Use when building e-learning modules, LMS content, or app-based instruction.

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.

cltdigital-learningfadingmultimediarenklself-explanationswellerworked-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.md21.1 kB · preview
--- # AGENT SKILLS STANDARD FIELDS (v2) name: digital-worked-example-sequence disable-model-invocation: false user-invocable: true effort: medium # EXISTING FIELDS skill_id: "ai-learning-science/digital-worked-example-sequence" skill_name: "Digital Worked Example Sequence" domain: "ai-learning-science" version: "1.0" evidence_strength: "strong" evidence_sources: - "Sweller, van Merriënboer & Paas (2019) — Cognitive Architecture and Instructional Design: 20 Years Later" - "Renkl (2014) — Toward an instructionally oriented theory of example-based learning" - "Atkinson, Derry, Renkl & Wortham (2000) — Learning from examples: instructional principles from the worked examples research" - "Renkl, Atkinson & Große (2004) — How fading worked-out solution steps works — a cognitive load perspective" - "Wylie & Chi (2014) — The self-explanation principle in multimedia learning" input_schema: required: - field: "skill_to_teach" type: "string" - field: "target_platform" type: "string" optional: - field: "student_level" type: "string" - field: "subject_area" type: "string" - field: "sequence_length" type: "integer" - field: "interactivity_level" type: "string" - field: "student_data_available" type: "string" output_schema: type: "object" fields: - field: "example_sequence" type: "array" - field: "self_explanation_prompts" type: "array" - field: "fading_schedule" type: "object" - field: "digital_design_specs" type: "object" chains_well_with: - "adaptive-hint-sequence-designer" - "erroneous-example-designer" - "worked-example-to-problem-solving-transition-designer" - "cognitive-load-analyser" teacher_time: "4 minutes" tags: ["worked-examples", "fading", "digital-learning", "CLT", "Sweller", "Renkl", "self-explanation", "multimedia"] ---
70# Digital Worked Example Sequence
71
72## What This Skill Does
73
74Designs a worked example sequence optimised for digital delivery — incorporating self-explanation prompts, a systematic fading schedule, and interactivity design that exploits what digital environments offer beyond paper. The sequence moves students from studying complete worked examples (where every step is shown and explained) through faded examples (where progressively more steps are removed for the student to complete) to independent problem-solving. The critical insight from Renkl (2014) is that worked examples only produce learning when students actively process them — passive reading of worked examples is barely better than no examples at all. Digital delivery creates unique opportunities (self-explanation prompts at each step, immediate feedback on faded steps, adaptive pacing based on performance) and unique risks (split attention between screen elements, cognitive overload from multimedia, temptation to click through without thinking). The output includes the complete example sequence, embedded self-explanation prompts, a precise fading schedule, and digital design specifications. AI is specifically valuable here because designing an effective digital worked example sequence requires coordinating content design (the mathematical or procedural steps), cognitive design (fading schedule, self-explanation points), and interface design (how steps are revealed, where prompts appear, how feedback is given) — three design dimensions that must be aligned.
75
76## Evidence Foundation
77
78Sweller, van Merriënboer & Paas (2019) updated cognitive load theory for contemporary digital learning contexts, identifying new sources of extraneous load specific to digital environments: transient information (content that appears and disappears), split attention between multiple screen areas, and redundancy in multimedia presentations. They emphasised that digital worked examples must manage these load sources through careful design. Renkl (2014) synthesised 25 years of worked example research into instructional principles: examples should be structured (steps clearly delineated), self-explanation should be prompted (not left to chance), fading should be systematic (one step at a time, starting with the most recently learned), and the transition to independent practice should be gradual. Atkinson et al. (2000) established foundational principles: worked examples are most effective for NOVICE learners (experts suffer the "expertise reversal effect" — examples become redundant and counterproductive), examples should alternate with practice problems rather than being presented in blocks, and the key mechanism is schema acquisition (building mental templates for problem types). Renkl, Atkinson & Große (2004) demonstrated that systematic fading — removing solution steps one at a time — was significantly more effective than abrupt transitions from full examples to full problems. Wylie & Chi (2014) showed that self-explanation prompts embedded within multimedia worked examples dramatically improved learning compared to worked examples without prompts, because they forced active processing of each step.
79
80## Input Schema
81
82The teacher must provide:
83- **Skill to teach:** The specific procedure. *e.g. "Solving simultaneous equations by elimination" / "Constructing a balanced argument paragraph" / "Converting between units of measurement" / "Writing a recursive function in Python"*
84- **Target platform:** Where it will be delivered. *e.g. "Google Slides with embedded quizzes" / "Custom web app with step-by-step reveal" / "Interactive PDF" / "Learning management system (Canvas/Moodle)"*
85
86Optional (injected by context engine if available):
87- **Student level:** Year group and proficiency
88- **Subject area:** Curriculum subject
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md3,131 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.

≈770
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
4,505
on trigger
The instruction body, read only when the skill fires.
2.6%
of a 200k window
Ten skills this size would take about 26% of the window before you open a file.
050k100k150k200k context window

5.3k 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, 21.1 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md21.1 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.

# Digital Worked Example Sequence · 5.3k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/digital-worked-example-sequence-2/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/digital-worked-example-sequence-2" ] && { [ ! -e .claude/skills/digital-worked-example-sequence-2 ] || { mv .claude/skills/digital-worked-example-sequence-2 "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/digital-worked-example-sequence-2" .claude/skills/ && echo "Installed .claude/skills/digital-worked-example-sequence-2"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add garethmanning/digital-worked-example-sequence-2

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/digital-worked-example-sequence-2/ 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/digital-worked-example-sequence-2/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/digital-worked-example-sequence-2" ] && { [ ! -e ~/.claude/skills/digital-worked-example-sequence-2 ] || { mv ~/.claude/skills/digital-worked-example-sequence-2 "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/digital-worked-example-sequence-2" ~/.claude/skills/ && echo "Installed ~/.claude/skills/digital-worked-example-sequence-2" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/digital-worked-example-sequence-2/ 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. It appears as digital-worked-example-sequence in Gemini CLI, Codex, Copilot and Grok (its own name); Claude Code lists it by the folder name, digital-worked-example-sequence-2.

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/digital-worked-example-sequence-2

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.

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