Disciplinary AI Literacy Sequence Designer Agent Skill

Design a sequence where students compare AI's handling of the same question across disciplines, developing a mental model of where AI is…

Context cost
6k tokensestimated from the bundle, loaded when it triggers
Bundle
1 file · 24.2 kBtext throughout, nothing executable
Licence
not declarednone named by its author
Last change
—v1.0
Servers it uses
Noneruns standalone

What it does

Design a sequence where students compare AI's handling of the same question across disciplines, developing a mental model of where AI is reliable vs. distorting based on knowledge type.

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-literacyai-reliabilitybernsteindisciplinary-thinkingdocumentationdomain-specificityknowledge-typeswillingham

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.md24.2 kB · preview
--- # AGENT SKILLS STANDARD FIELDS (v2) name: disciplinary-ai-literacy-sequence-designer disable-model-invocation: false user-invocable: true effort: medium # EXISTING FIELDS skill_id: "ai-literacy/disciplinary-ai-literacy-sequence-designer" skill_name: "Disciplinary AI Literacy Sequence Designer" domain: "ai-literacy" version: "1.0" contributor: "Gareth Manning" evidence_strength: "moderate" evidence_sources: - "Willingham (2007) — Critical thinking: why is it so hard to teach? (domain-specificity)" - "McPeck (1981) — Critical Thinking and Education: domain-specificity of critical thinking" - "Bernstein (1999) — Vertical and horizontal discourse: epistemic and social foundations of research contexts" - "Maton (2013) — Making semantic waves: a key to cumulative knowledge-building" - "Wineburg (2007) — Unnatural and essential: the nature of historical thinking" input_schema: required: - field: "target_disciplines" type: "string" - field: "student_level" type: "string" optional: - field: "anchor_question_type" type: "string" - field: "knowledge_type_focus" type: "string" - field: "subject_area" type: "string" - field: "time_available" type: "string" output_schema: type: "object" fields: - field: "knowledge_type_analysis" type: "object" - field: "anchor_question_set" type: "array" - field: "comparison_protocol" type: "object" - field: "ai_reliability_framework" type: "object" - field: "discussion_guide" type: "object" chains_well_with: - "kud-knowledge-type-mapper" - "curriculum-knowledge-architecture-designer" - "ai-output-critical-audit-designer" teacher_time: "5 minutes" tags: ["AI-literacy", "disciplinary-thinking", "knowledge-types", "Bernstein", "Willingham", "domain-specificity", "AI-reliability"] ---
70# Disciplinary AI Literacy Sequence Designer
71
72## What This Skill Does
73
74Generates a multi-lesson sequence in which students systematically compare AI's handling of the same type of question across disciplines — developing a principled mental model of where AI is reliable and where it distorts, based on the type of knowledge the discipline produces. The central insight is that AI's reliability is not uniform: it handles settled factual knowledge differently from contested interpretive claims, sequential procedural knowledge differently from dispositional knowledge about values and judgment. A student who understands why AI is generally reliable when explaining photosynthesis but unreliable when interpreting the causes of the French Revolution has developed transferable AI literacy — not just a list of "AI gets wrong" examples, but a predictive framework. The sequence follows the logic of Maton's (2013) semantic wave: starting from concrete discipline-specific examples, building to an abstract framework (AI reliability varies by knowledge type), then returning to concrete predictions ("for this assignment, in this subject, I expect AI to be X reliable because..."). The skill draws on the library's existing knowledge architecture framework: sequential knowledge (structured, hierarchical, cumulative — as in mathematics or scientific procedures) tends to be well-served by AI; horizontal knowledge (multiple valid frameworks that don't displace each other — as in historiography or literary criticism) is where AI is most likely to flatten genuine complexity or false-certainty a contested position.
75
76## Evidence Foundation
77
78Willingham (2007) demonstrated that critical thinking is domain-specific — students who think critically in history do not automatically transfer that skill to biology, because what counts as good evidence differs by discipline. This has a direct AI corollary: AI's limitations in one domain do not automatically transfer to predictions about other domains. Disciplinary AI literacy requires domain-by-domain evaluation. McPeck (1981) argued that critical thinking is constituted by disciplinary knowledge — you cannot evaluate AI output in history without understanding how historians argue, any more than you can evaluate scientific AI output without understanding scientific reasoning. The sequence here operationalises this: students use their disciplinary knowledge as the evaluative instrument. Bernstein's (1999) distinction between vertical discourse (hierarchical, cumulative knowledge structures where new knowledge subsumes or displaces old — as in natural sciences) and horizontal discourse (segmented knowledge structures where competing frameworks co-exist — as in social sciences and humanities) directly predicts AI reliability patterns. AI is trained on the full distribution of human knowledge production — in vertical discourse domains, that distribution converges on correct answers; in horizontal discourse domains, it averages across competing frameworks, potentially flattening the distinctions between them. Maton's (2013) semantic wave concept provides the instructional design logic: the sequence must move students between concrete examples (AI answers about specific topics in specific disciplines) and abstract principles (the knowledge-type framework that explains the pattern), then back to concrete predictions. Wineburg (2007) on historical thinking as "unnatural" provides a specific example: the skills experts use in a discipline are not intuitive — students must be taught them. The same applies to disciplinary AI literacy: students must be explicitly taught to ask "what kind of knowledge is this discipline producing, and what does that mean for AI's reliability?"
79
80## Input Schema
81
82The teacher must provide:
83- **Target disciplines:** The subjects to compare. *e.g. "Biology and History" / "Mathematics, History, and Ethics" / "Physics and Literary Studies" / "Geography and Philosophy"*
84- **Student level:** Year group. *e.g. "Year 11, studying multiple subjects for national examinations" / "Year 12, A-level students"*
85
86Optional (injected by context engine if available):
87- **Anchor question type:** The type of question to translate across disciplines
88- **Knowledge type focus:** Which knowledge structure distinction to emphasise
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md3,325 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.

≈890
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
5,160
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, 24.2 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md24.2 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.

# Disciplinary AI Literacy Sequence Designer · 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/disciplinary-ai-literacy-sequence-designer/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/disciplinary-ai-literacy-sequence-designer" ] && { [ ! -e .claude/skills/disciplinary-ai-literacy-sequence-designer ] || { mv .claude/skills/disciplinary-ai-literacy-sequence-designer "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/disciplinary-ai-literacy-sequence-designer" .claude/skills/ && echo "Installed .claude/skills/disciplinary-ai-literacy-sequence-designer"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add garethmanning/disciplinary-ai-literacy-sequence-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/disciplinary-ai-literacy-sequence-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/disciplinary-ai-literacy-sequence-designer/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/disciplinary-ai-literacy-sequence-designer" ] && { [ ! -e ~/.claude/skills/disciplinary-ai-literacy-sequence-designer ] || { mv ~/.claude/skills/disciplinary-ai-literacy-sequence-designer "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/disciplinary-ai-literacy-sequence-designer" ~/.claude/skills/ && echo "Installed ~/.claude/skills/disciplinary-ai-literacy-sequence-designer" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/disciplinary-ai-literacy-sequence-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/disciplinary-ai-literacy-sequence-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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