Explain-First Interrogator Agent Skill

Require the learner to explain a concept in their own words before the AI evaluates or extends it.

Context cost
3.7k tokensestimated from the bundle, loaded when it triggers
Bundle
1 file · 14.8 kBtext throughout, nothing executable
Licence
not declarednone named by its author
Last change
—v1.0
Servers it uses
Noneruns standalone

What it does

Require the learner to explain a concept in their own words before the AI evaluates or extends it. Ensures the AI works from the learner's understanding rather than providing an explanation from scratch.

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.

chidocumentationgatemetacognitionreasoningself-explanation

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.md14.8 kB · preview
--- # AGENT SKILLS STANDARD FIELDS (v2) name: explain-first-interrogator disable-model-invocation: false user-invocable: true effort: medium # DOMAIN 20 FIELDS skill_id: "student-learning/explain-first-interrogator" skill_name: "Explain-First Interrogator" domain: "student-learning" domain_number: 20 version: "1.0" audience: "student" evidence_strength: "strong" evidence_sources: - "Chi et al. (1994) — Eliciting self-explanations improves understanding" - "Chi et al. (1989) — Self-explanations: how students study and use examples in learning to solve problems" - "Bisra et al. (2018) — Inducing self-explanation: a meta-analysis (64 reports, g = 0.55)" - "Hausmann & VanLehn (2007) — Explaining self-explaining: the generation effect in self-explanation" - "arXiv 2604.00142 (2026) — LLM-supported self-explanation improving transfer performance" input_schema: required: - field: "topic_or_problem" type: "string" - field: "context" type: "string" optional: - field: "prior_sessions" type: "array" - field: "developmental_band" type: "string" evidence_captured: cognitive_gate: "self_explanation" student_attempt_required: true confidence_before: false confidence_after: false hint_level_reached: "not_applicable" error_type: "conceptual | procedural | strategic | not_applicable" ai_support_type: "question | warm_start" reflection_captured: true transfer_check: "passed | failed | skipped" unassisted_followup: "not_scheduled" assistance_tag: "scaffolded" chains_well_with: - "student-learning/retrieve-first-gate" - "student-learning/stuck-and-error-diagnosis-coach" - "student-learning/transfer-bridge" - "student-learning/teach-back-evaluator" tags: ["self-explanation", "metacognition", "reasoning", "gate", "Chi"] ---
59# Explain-First Interrogator
60
61## What This Skill Does
62
63Requires the learner to articulate their reasoning, explain a concept in their own words, or walk through their thinking before the AI evaluates, corrects, or extends it. The AI probes the weakest part of the explanation — it never rewrites or replaces it. This operationalises the self-explanation effect: the act of generating an explanation, even an imperfect one, produces deeper learning than reading or hearing a correct explanation (Chi et al., 1994). The skill is designed to make AI-assisted study feel like a Socratic dialogue rather than a lecture.
64
65## Evidence Foundation
66
67Chi et al. (1989) discovered the self-explanation effect by observing students studying worked examples in physics. Students who spontaneously paused to explain to themselves why each step made sense ("good students") dramatically outperformed those who read passively ("poor students") — and the difference was not explained by prior knowledge or study time, but by the quality of cognitive engagement during study. Chi et al. (1994) then demonstrated that this benefit could be induced: students who were prompted to self-explain a biology text significantly outperformed a control group on both immediate comprehension and transfer tests. Bisra et al. (2018) meta-analysed 64 reports and found a mean effect size of g = 0.55 for self-explanation induction across ages, subjects, and contexts — a robust and reproducible finding. Hausmann & VanLehn (2007) established that the benefit comes not just from the content of the explanation but from the act of generation itself: students who generated their own explanations outperformed students who read equivalently correct explanations provided by an instructor, confirming that the generation is the learning. Recent work (arXiv 2604.00142, 2026) shows that LLM-supported self-explanation prompting specifically improves transfer performance — the kind of durable, portable learning that distinguishes genuine understanding from surface familiarity.
68
69## System Prompt
70
71```
72You are a learning coach. Your role is to help {{name_or_"the learner"}} develop genuine understanding of {{topic_or_problem}} — not to explain it for them. The governing principle: ask for the learner's explanation before offering your own. The learner's thinking is the raw material; your job is to probe it, not replace it.
73
74CONTEXT: {{context}}
75TOPIC OR PROBLEM: {{topic_or_problem}}
76DEVELOPMENTAL BAND: {{developmental_band — if not provided, assume secondary school / undergraduate}}
77PRIOR SESSIONS: {{prior_sessions — if not provided, treat this as a first session}}
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md2,193 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.

≈580
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
3,120
on trigger
The instruction body, read only when the skill fires.
1.8%
of a 200k window
Ten skills this size would take about 19% of the window before you open a file.
050k100k150k200k context window

3.7k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Middling. Fine to keep on in a project where you use it weekly, worth unloading in one where you never do.

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

  • SKILL.md14.8 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.

# Explain-First Interrogator · 3.7k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/explain-first-interrogator/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/explain-first-interrogator" ] && { [ ! -e .claude/skills/explain-first-interrogator ] || { mv .claude/skills/explain-first-interrogator "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/explain-first-interrogator" .claude/skills/ && echo "Installed .claude/skills/explain-first-interrogator"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add garethmanning/explain-first-interrogator

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/explain-first-interrogator/ 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/explain-first-interrogator/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/explain-first-interrogator" ] && { [ ! -e ~/.claude/skills/explain-first-interrogator ] || { mv ~/.claude/skills/explain-first-interrogator "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/explain-first-interrogator" ~/.claude/skills/ && echo "Installed ~/.claude/skills/explain-first-interrogator" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/explain-first-interrogator/ 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/explain-first-interrogator

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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