Cognitive Tutoring Architecture Designer Agent Skill

Map knowledge components and skill hierarchies for a cognitive tutoring system or adaptive learning platform.

Context cost
6.1k tokensestimated from the bundle, loaded when it triggers
Bundle
1 file · 24.5 kBtext throughout, nothing executable
Licence
not declarednone named by its author
Last change
—v1.0
Servers it uses
Noneruns standalone

What it does

Map knowledge components and skill hierarchies for a cognitive tutoring system or adaptive learning platform. Use when designing intelligent tutoring software or skill-based mastery systems.

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.

act-radaptiveandersoncognitive-tutorcorbettitsknowledge-tracingmastery

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.md24.5 kB · preview
--- # AGENT SKILLS STANDARD FIELDS (v2) name: cognitive-tutoring-architecture-designer disable-model-invocation: true user-invocable: true effort: medium # EXISTING FIELDS skill_id: "ai-learning-science/cognitive-tutoring-architecture-designer" skill_name: "Cognitive Tutoring Architecture Designer" domain: "ai-learning-science" version: "1.0" evidence_strength: "strong" evidence_sources: - "Anderson et al. (1995) — Cognitive tutors: lessons learned (ACT-R theory applied to education)" - "Corbett & Anderson (1995) — Knowledge tracing: modeling the acquisition of procedural knowledge" - "Koedinger & Aleven (2007) — Exploring the assistance dilemma in experiments with cognitive tutors" - "Ritter et al. (2007) — Cognitive Tutor: applied research in mathematics education" - "Pane et al. (2014) — Effectiveness of cognitive tutor algebra I at scale (RAND evaluation)" input_schema: required: - field: "skill_domain" type: "string" - field: "knowledge_components" type: "string" optional: - field: "student_level" type: "string" - field: "subject_area" type: "string" - field: "common_errors" type: "string" - field: "mastery_threshold" type: "string" - field: "system_context" type: "string" output_schema: type: "object" fields: - field: "cognitive_model" type: "object" - field: "knowledge_tracing_design" type: "object" - field: "problem_selection_logic" type: "object" - field: "feedback_architecture" type: "object" chains_well_with: - "adaptive-hint-sequence-designer" - "intelligent-tutoring-dialogue-designer" - "formative-assessment-loop-designer" - "worked-example-to-problem-solving-transition-designer" - "technological-pedagogical-content-knowledge-developer" teacher_time: "6 minutes" tags: ["cognitive-tutor", "ACT-R", "Anderson", "knowledge-tracing", "Corbett", "mastery", "ITS", "adaptive"] ---
71# Cognitive Tutoring Architecture Designer
72
73## What This Skill Does
74
75Designs the cognitive architecture for a tutoring system — the knowledge component model, knowledge tracing algorithm, problem selection logic, and feedback architecture that together create an adaptive learning experience. This skill is based on the Cognitive Tutor architecture developed by Anderson et al. (1995) at Carnegie Mellon University, which remains one of the most empirically validated approaches to intelligent tutoring. The Cognitive Tutor approach is built on ACT-R theory: the idea that expertise in any domain can be decomposed into a set of discrete "knowledge components" (production rules) that can be individually learned, practised, and tracked. The system maintains a model of what each student knows (knowledge tracing), selects problems that target the knowledge components the student has not yet mastered, and provides step-level feedback when errors occur. Pane et al. (2014) conducted a large-scale RAND evaluation finding that Cognitive Tutor Algebra I improved student outcomes compared to traditional instruction. AI is specifically valuable here because the architecture requires real-time tracking of individual knowledge states across many components — a task that is computationally straightforward but humanly impossible for a teacher managing 30 students.
76
77## Evidence Foundation
78
79Anderson et al. (1995) developed the Cognitive Tutor based on ACT-R (Adaptive Control of Thought—Rational), a cognitive architecture that models human learning as the acquisition of production rules — IF-THEN knowledge components. For example, in algebra, one knowledge component might be "IF the equation has the form ax + b = c, THEN subtract b from both sides as the first step." The key insight: complex skills can be decomposed into a finite set of these components, each of which can be independently tracked and tutored. The Cognitive Tutor maintained a student model that estimated the probability that each student had learned each knowledge component, and selected problems that targeted the weakest components. Corbett & Anderson (1995) developed knowledge tracing (KT), the algorithm that tracks student mastery. KT uses a hidden Markov model: each knowledge component is either "learned" or "unlearned" (the hidden state). Each time a student attempts a problem step that involves a knowledge component, the system observes whether the step was correct or incorrect (the observable) and updates its estimate of the probability that the component is learned. Four parameters govern the model: P(L₀) — the probability the student knew the component before instruction; P(T) — the probability of learning the component at each opportunity; P(G) — the probability of guessing correctly without knowing; and P(S) — the probability of making a slip despite knowing. When P(Lₙ) exceeds a threshold (typically 0.95), the component is considered mastered. Koedinger & Aleven (2007) articulated the "assistance dilemma" in the context of cognitive tutors: providing too much assistance (immediate hints, step-level guidance) can lead students to learn the tutor's interface rather than the domain knowledge. They found that the optimal level of assistance depended on the student's current knowledge state — struggling students benefited from more assistance, while advanced students benefited from less. Ritter et al. (2007) reported on the deployment of Cognitive Tutor Algebra in over 2,600 schools, demonstrating the approach's scalability. Pane et al. (2014) conducted a RAND-funded randomised controlled trial of Cognitive Tutor Algebra I and found modest but significant positive effects on algebra achievement, particularly for students who used the system as designed (with fidelity to the intended model).
80
81## Input Schema
82
83The teacher must provide:
84- **Skill domain:** What students need to learn. *e.g. "Solving linear equations — from one-step (x + 5 = 12) through two-step (3x + 5 = 20) to multi-step equations with variables on both sides (4x + 3 = 2x + 11)" / "Balancing chemical equations" / "Constructing and interpreting histograms from raw data"*
85- **Knowledge components:** The building blocks. *e.g. "For linear equations: (1) identifying the unknown, (2) inverse operations (addition ↔ subtraction), (3) inverse operations (multiplication ↔ division), (4) order of operations (undo addition/subtraction before multiplication/division), (5) applying operation to both sides, (6) combining like terms, (7) moving variables to one side" / "For histograms: (1) identifying continuous data, (2) choosing appropriate class intervals, (3) calculating frequency for each interval, (4) scaling axes, (5) drawing bars with no gaps, (6) reading values from a histogram, (7) comparing distributions"*
86
87Optional (injected by context engine if available):
88- **Student level:** Year group and proficiency
89- **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.md3,783 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.

≈830
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
5,295
on trigger
The instruction body, read only when the skill fires.
3.1%
of a 200k window
Ten skills this size would take about 31% of the window before you open a file.
050k100k150k200k context window

6.1k 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, 24.5 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md24.5 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.

# Cognitive Tutoring Architecture Designer · 6.1k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/cognitive-tutoring-architecture-designer/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/cognitive-tutoring-architecture-designer" ] && { [ ! -e .claude/skills/cognitive-tutoring-architecture-designer ] || { mv .claude/skills/cognitive-tutoring-architecture-designer "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/cognitive-tutoring-architecture-designer" .claude/skills/ && echo "Installed .claude/skills/cognitive-tutoring-architecture-designer"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add garethmanning/cognitive-tutoring-architecture-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/cognitive-tutoring-architecture-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/cognitive-tutoring-architecture-designer/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/cognitive-tutoring-architecture-designer" ] && { [ ! -e ~/.claude/skills/cognitive-tutoring-architecture-designer ] || { mv ~/.claude/skills/cognitive-tutoring-architecture-designer "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/cognitive-tutoring-architecture-designer" ~/.claude/skills/ && echo "Installed ~/.claude/skills/cognitive-tutoring-architecture-designer" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/cognitive-tutoring-architecture-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/cognitive-tutoring-architecture-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.

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