AI Learning Boundary Mapper Agent Skill

Map which elements of an assignment benefit from AI assistance vs. which AI use undermines.

Context cost
6k tokensestimated from the bundle, loaded when it triggers
Bundle
1 file · 24.0 kBtext throughout, nothing executable
Licence
not declarednone named by its author
Last change
—v1.0
Servers it uses
Noneruns standalone

What it does

Map which elements of an assignment benefit from AI assistance vs. which AI use undermines. Use when redesigning tasks for AI-age classrooms or setting defensible AI use policies for specific assignments.

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.

ai-boundariesai-literacyai-policyassignment-designbackward-designdocumentationlearning-objectivestool-selection

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.md24.0 kB · preview
--- # AGENT SKILLS STANDARD FIELDS (v2) name: ai-learning-boundary-mapper disable-model-invocation: false user-invocable: true effort: medium # EXISTING FIELDS skill_id: "ai-literacy/ai-learning-boundary-mapper" skill_name: "AI Learning Boundary Mapper" domain: "ai-literacy" version: "1.0" contributor: "Gareth Manning" evidence_strength: "moderate" evidence_sources: - "Wiggins & McTighe (2005) — Understanding by Design (backward design and assessment alignment)" - "Bjork et al. (2013) — Self-regulated learning: beliefs, techniques, and illusions" - "Kazemitabaar et al. (2023) — Studying the effect of AI code generators on supporting novice learners" - "Kirschner, Sweller & Clark (2006) — Why minimal guidance during instruction does not work" - "Wineburg & McGrew (2019) — Lateral reading and the nature of expertise" input_schema: required: - field: "assignment_description" type: "string" - field: "learning_objectives" type: "string" optional: - field: "current_ai_policy" type: "string" - field: "student_level" type: "string" - field: "subject_area" type: "string" - field: "assessment_context" type: "string" - field: "tool_comparison_needed" type: "boolean" output_schema: type: "object" fields: - field: "objective_analysis" type: "object" - field: "component_boundary_map" type: "object" - field: "ai_policy_recommendations" type: "object" - field: "tool_comparison" type: "object" - field: "redesign_suggestions" type: "array" chains_well_with: - "metacognitive-monitoring-ai-contexts" - "backwards-design-unit-planner" - "assessment-validity-checker" teacher_time: "5 minutes" tags: ["AI-literacy", "assignment-design", "AI-policy", "backward-design", "tool-selection", "learning-objectives", "AI-boundaries"] ---
73# AI Learning Boundary Mapper
74
75## What This Skill Does
76
77Generates a component-by-component analysis of a specific assignment, mapping which elements benefit from AI assistance, which are neutral, and which are undermined by AI involvement — based on the learning objectives the assignment serves. This is the teacher-facing design tool for AI-age assignment redesign: it takes an existing assignment and produces a boundary map that allows teachers to set specific, defensible AI use policies rather than blanket "AI allowed" or "no AI" positions. The central insight is that within any single assignment, different components serve different learning objectives — and AI assistance that helps with one component may undermine another. An essay that requires both research (AI can assist with summarising context) and original argumentation (AI assistance bypasses the cognitive work of constructing an argument) benefits from a component-level policy, not a uniform one. The output includes an objective analysis (for each learning objective, whether AI assistance supports or undermines it), a component boundary map, defensible AI policy recommendations, an optional Google vs. AI chatbot tool comparison for information-gathering tasks, and redesign suggestions that preserve learning-critical challenge while permitting AI use where it genuinely helps. This skill is the teacher-design complement to metacognitive-monitoring-ai-contexts: boundary-mapping prevents the metacognitive risk from arising; metacognitive-monitoring-ai-contexts addresses it when it does.
78
79## Evidence Foundation
80
81Wiggins & McTighe (2005) established the backward design principle: assessment design should start with learning objectives (Stage 1) and work backward through evidence of learning (Stage 2) to learning activities (Stage 3). This principle applies directly to AI boundary-setting: the question is not "should AI be used in this assignment?" but "which learning objectives does this assignment serve, and does AI assistance support or bypass the cognitive work those objectives require?" Bjork et al. (2013) documented illusions of competence — conditions where learners feel they have learned more than they actually have. AI assistance produces the fluency illusion: tasks completed with AI assistance feel complete and correct, but the cognitive work that generates durable learning has been bypassed. The boundary map is designed to identify which assignment components are most vulnerable to this effect. Kazemitabaar et al. (2023) provided direct empirical evidence: AI-assisted programming students completed tasks faster and with fewer errors but showed weaker understanding on subsequent tasks without AI support. This effect is used here as the model for identifying "AI-undermining" components — any task where the cognitive process (not just the product) is the learning objective. Kirschner, Sweller & Clark (2006) established that minimally guided instruction produces weaker learning than explicit instruction for novices, because novice learners need the cognitive challenge of the task itself to build the knowledge structures required for expertise. This supports identifying components where removing cognitive challenge (via AI) also removes learning. Wineburg & McGrew (2019) provide indirect support for the tool-comparison dimension: different information tools have different epistemic properties (verifiable citations vs. synthesised inference), and students benefit from explicit guidance about which tool to use for which information need.
82
83## Input Schema
84
85The teacher must provide:
86- **Assignment description:** What students do. *e.g. "Year 10 History: write a 600-word essay arguing whether the Treaty of Versailles was the main cause of WWII, using at least three named historians' arguments" / "Year 9 Science: write a lab report for the rates of reaction experiment — method, results, analysis, conclusion" / "Year 12 English: comparative essay on two texts studied in class"*
87- **Learning objectives:** What the assignment develops. *e.g. "Students learn to construct an evidence-based historical argument, evaluate competing historiographical interpretations, and use source evidence appropriately" / "Students learn to write scientific analysis from data they collected themselves, drawing valid conclusions"*
88
89Optional (injected by context engine if available):
90- **Current AI policy:** What's currently permitted
91- **Student level:** Year group
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md3,345 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.

≈850
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
5,150
on trigger
The instruction body, read only when the skill fires.
3.0%
of a 200k window
Ten skills this size would take about 30% of the window before you open a file.
050k100k150k200k context window

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

  • SKILL.md24.0 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.

# AI Learning Boundary Mapper · 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/ai-learning-boundary-mapper/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/ai-learning-boundary-mapper" ] && { [ ! -e .claude/skills/ai-learning-boundary-mapper ] || { mv .claude/skills/ai-learning-boundary-mapper "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/ai-learning-boundary-mapper" .claude/skills/ && echo "Installed .claude/skills/ai-learning-boundary-mapper"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add garethmanning/ai-learning-boundary-mapper

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

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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