Elaborative Interrogation Prompt Generator Agent Skill

Generate elaborative interrogation prompts that deepen encoding through targeted why and how questions.

Context cost
4.5k tokensestimated from the bundle, loaded when it triggers
Bundle
1 file · 18.1 kBtext throughout, nothing executable
Licence
not declarednone named by its author
Last change
—v1.0
Servers it uses
Noneruns standalone

What it does

Generate elaborative interrogation prompts that deepen encoding through targeted why and how questions. Use when students memorise without understanding or need deeper processing of content.

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.

comprehensiondeep-processingdocumentationelaborationencodingpromptquestioning

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.md18.1 kB · preview
--- # AGENT SKILLS STANDARD FIELDS (v2) name: elaborative-interrogation-generator disable-model-invocation: false user-invocable: true effort: medium # EXISTING FIELDS skill_id: "memory-learning-science/elaborative-interrogation-generator" skill_name: "Elaborative Interrogation Prompt Generator" domain: "memory-learning-science" version: "1.0" evidence_strength: "strong" evidence_sources: - "Pressley et al. (1992) — Elaborative interrogation facilitates acquisition of confusing facts" - "Woloshyn et al. (1994) — Use of elaborative interrogation to help students acquire information consistent with prior knowledge" - "Dunlosky et al. (2013) — Elaborative interrogation rated moderate-utility learning strategy" - "McDaniel & Donnelly (1996) — Learning with analogy and elaborative interrogation" - "Ozgungor & Guthrie (2004) — Interactions among elaborative interrogation, knowledge, and interest in the process of constructing knowledge from text" input_schema: required: - field: "topic" type: "string" - field: "student_level" type: "string" - field: "prompt_count" type: "integer" optional: - field: "content_text" type: "string" - field: "prior_knowledge" type: "string" - field: "student_profiles" type: "array" - field: "learning_objectives" type: "string" output_schema: type: "object" fields: - field: "elaborative_prompts" type: "array" - field: "implementation_guide" type: "string" - field: "connection_prompts" type: "array" chains_well_with: - "retrieval-practice-generator" - "self-regulation-scaffold-generator" - "metacognitive-prompt-library" - "reading-comprehension-strategy-selector" teacher_time: "3 minutes" tags: ["elaboration", "encoding", "comprehension", "deep-processing", "questioning"] ---
67# Elaborative Interrogation Prompt Generator
68
69## What This Skill Does
70
71Generates a set of "why?" and "how does this connect?" prompts designed to deepen encoding by forcing students to generate explanations that link new information to existing knowledge. Unlike comprehension questions (which check understanding), elaborative interrogation prompts require students to explain *why* a fact is true or *how* it relates to something they already know — the act of generating the explanation strengthens the memory trace. AI is specifically valuable here because effective elaborative prompts must be pitched at the precise intersection of what students are learning and what they already know — too disconnected from prior knowledge and students can't generate explanations; too obvious and there's no elaboration needed.
72
73## Evidence Foundation
74
75Pressley et al. (1992) demonstrated that answering "why?" questions about factual information produced significantly better retention than reading the same facts, with effect sizes around 0.59. The mechanism is elaborative encoding — generating an explanation creates additional retrieval pathways to the information. Woloshyn et al. (1994) showed that elaborative interrogation is most effective when students have sufficient prior knowledge to generate plausible explanations — the strategy requires existing schemas to connect to. Dunlosky et al. (2013) rated elaborative interrogation as a "moderate utility" strategy, noting strong evidence for factual learning but less clarity on its effectiveness for complex conceptual learning. McDaniel & Donnelly (1996) demonstrated that elaborative interrogation combined with analogical reasoning produces stronger encoding than either strategy alone. Ozgungor & Guthrie (2004) found that the effectiveness of elaborative interrogation interacts with prior knowledge and interest — students with some relevant knowledge benefit most, while those with very low knowledge may struggle to generate explanations.
76
77## Input Schema
78
79The teacher must provide:
80- **Topic:** The concept or content students are learning. *e.g. "Properties of metals and non-metals" / "Causes of the French Revolution" / "The structure of a sonnet"*
81- **Student level:** Year group and prior knowledge. *e.g. "Year 9 Chemistry, mid-ability, have covered atomic structure"*
82- **Prompt count:** Number of prompts needed. *e.g. 6*
83
84Optional (injected by context engine if available):
85- **Content text:** The specific text, passage, or resource students are working with
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md2,561 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.

≈720
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
3,805
on trigger
The instruction body, read only when the skill fires.
2.3%
of a 200k window
Ten skills this size would take about 23% of the window before you open a file.
050k100k150k200k context window

4.5k 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, 18.1 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md18.1 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.

# Elaborative Interrogation Prompt Generator · 4.5k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/elaborative-interrogation-prompt-generator-2/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/elaborative-interrogation-prompt-generator-2" ] && { [ ! -e .claude/skills/elaborative-interrogation-prompt-generator-2 ] || { mv .claude/skills/elaborative-interrogation-prompt-generator-2 "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/elaborative-interrogation-prompt-generator-2" .claude/skills/ && echo "Installed .claude/skills/elaborative-interrogation-prompt-generator-2"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add garethmanning/elaborative-interrogation-prompt-generator-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/elaborative-interrogation-prompt-generator-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/elaborative-interrogation-prompt-generator-2/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/elaborative-interrogation-prompt-generator-2" ] && { [ ! -e ~/.claude/skills/elaborative-interrogation-prompt-generator-2 ] || { mv ~/.claude/skills/elaborative-interrogation-prompt-generator-2 "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/elaborative-interrogation-prompt-generator-2" ~/.claude/skills/ && echo "Installed ~/.claude/skills/elaborative-interrogation-prompt-generator-2" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/elaborative-interrogation-prompt-generator-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 elaborative-interrogation-generator in Gemini CLI, Codex, Copilot and Grok (its own name); Claude Code lists it by the folder name, elaborative-interrogation-prompt-generator-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/elaborative-interrogation-prompt-generator-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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