Prompt Literacy Sequence Designer Agent Skill

Design a learning sequence teaching prompt quality — comparing vague vs. refined prompts to show why specificity and context transform AI…

Context cost
5.7k tokensestimated from the bundle, loaded when it triggers
Bundle
1 file · 22.7 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 learning sequence teaching prompt quality — comparing vague vs. refined prompts to show why specificity and context transform AI output. Use when students use AI without understanding why output quality varies.

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-useconstraintscontextdocumentationprompt-engineeringprompt-literacyspecificity

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.md22.7 kB · preview
--- # AGENT SKILLS STANDARD FIELDS (v2) name: prompt-literacy-sequence-designer disable-model-invocation: false user-invocable: true effort: medium # EXISTING FIELDS skill_id: "ai-literacy/prompt-literacy-sequence-designer" skill_name: "Prompt Literacy Sequence Designer" domain: "ai-literacy" version: "1.0" contributor: "Gareth Manning" evidence_strength: "low-moderate" evidence_sources: - "Brown et al. (2020) — Language Models are Few-Shot Learners (GPT-3 few-shot prompting)" - "Liu et al. (2023) — Pre-Train, Prompt, and Predict: a systematic survey of prompting methods in NLP" - "Reynolds & McDonell (2021) — Prompt programming for large language models: beyond the few-shot paradigm" - "Rosenshine (2012) — Principles of instruction (modelling and guided practice framework)" - "Willingham (2007) — Critical thinking: why is it so hard to teach? (specificity in task design)" input_schema: required: - field: "subject_area" type: "string" - field: "student_level" type: "string" - field: "ai_task_type" type: "string" optional: - field: "prompt_literacy_focus" type: "string" - field: "common_student_prompts" type: "string" - field: "time_available" type: "string" output_schema: type: "object" fields: - field: "prompt_anatomy" type: "object" - field: "compare_contrast_sequence" type: "object" - field: "prompt_rewriting_activity" type: "object" - field: "pricing_exercise" type: "object" - field: "prompt_principles_summary" type: "object" chains_well_with: - "ai-output-critical-audit-designer" - "metacognitive-monitoring-ai-contexts" - "explicit-instruction-sequence-builder" teacher_time: "4 minutes" tags: ["AI-literacy", "prompt-engineering", "prompt-literacy", "specificity", "AI-use", "context", "constraints"] ---
70# Prompt Literacy Sequence Designer
71
72## What This Skill Does
73
74Generates a structured learning sequence that teaches students why prompt quality determines AI output quality — and what specific prompt moves produce more useful, accurate, and contextually appropriate responses. The sequence follows a compare-contrast structure: students run vague and refined prompts on the same question, analyse the difference in output quality, and abstract the principles. The core insight is that AI fills missing context with the most statistically common response — so a prompt with no context about audience, purpose, discipline, or constraints will receive an answer calibrated for the average case, not the student's specific situation. The Pricing Exercise (Kharbach, 2026) is included as the anchor activity: students take a context-free AI answer ("What should I charge for a service?") and iteratively add constraints (type of service, location, target market, quality level), showing in real time how specificity transforms output from generically unhelpful to genuinely useful. The sequence teaches five prompt dimensions: context (who am I, what am I doing?), task (exactly what do I want?), constraints (what limits apply?), format (how should the output be structured?), and persona (what role should the AI take?). Prompt literacy is a prerequisite for effective AI use and a direct complement to AI output evaluation skills.
75
76## Evidence Foundation
77
78Brown et al. (2020) in the GPT-3 paper demonstrated empirically that the way a prompt is formulated dramatically affects model output quality — few-shot examples in the prompt (showing the AI what a good response looks like) produce substantially better results than zero-shot prompts (no examples). This is the foundational evidence that prompt design is not arbitrary. Liu et al. (2023) conducted a systematic survey of prompting methods, documenting the research on how different prompt structures (chain-of-thought, role-play, instruction-following, few-shot) affect output quality across tasks. Their survey establishes that prompt engineering is a skill with learnable principles, not a matter of chance. Reynolds & McDonell (2021) extended this to the concept of "metaprompts" — prompts that explicitly instruct the AI about how to reason, structure its response, or adopt a persona — showing that these structural elements can substantially improve output quality. These three sources provide the AI-specific evidence base for prompt literacy instruction. However, the pedagogical evidence base for *teaching* prompt literacy to students is currently very limited — this is frontier territory in educational research. The remaining sources support the *instructional design* of this sequence: Rosenshine (2012) provides the modelling → guided practice → independent practice structure used here; Willingham (2007) provides the domain-specificity argument (what counts as a good prompt in history is different from what counts as one in mathematics) that justifies subject-specific prompt literacy instruction.
79
80## Input Schema
81
82The teacher must provide:
83- **Subject area:** The discipline. *e.g. "History" / "Biology" / "English Language & Literature" / "Mathematics"*
84- **Student level:** Year group and current AI usage. *e.g. "Year 10, regularly use ChatGPT for homework but get generic outputs they don't find useful" / "Year 12, use AI for research and draft writing but haven't been explicitly taught prompt strategies"*
85- **AI task type:** What students use AI for. *e.g. "Research: getting background information on essay topics" / "Writing: generating draft paragraphs and getting feedback" / "Explanation: asking AI to explain concepts they've missed"*
86
87Optional (injected by context engine if available):
88- **Prompt literacy focus:** Which prompt dimension to emphasise
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md3,233 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.

≈860
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
4,815
on trigger
The instruction body, read only when the skill fires.
2.8%
of a 200k window
Ten skills this size would take about 28% of the window before you open a file.
050k100k150k200k context window

5.7k 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, 22.7 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md22.7 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.

# Prompt Literacy Sequence Designer · 5.7k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/prompt-literacy-sequence-designer/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/prompt-literacy-sequence-designer" ] && { [ ! -e .claude/skills/prompt-literacy-sequence-designer ] || { mv .claude/skills/prompt-literacy-sequence-designer "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/prompt-literacy-sequence-designer" .claude/skills/ && echo "Installed .claude/skills/prompt-literacy-sequence-designer"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add garethmanning/prompt-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/prompt-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/prompt-literacy-sequence-designer/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/prompt-literacy-sequence-designer" ] && { [ ! -e ~/.claude/skills/prompt-literacy-sequence-designer ] || { mv ~/.claude/skills/prompt-literacy-sequence-designer "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/prompt-literacy-sequence-designer" ~/.claude/skills/ && echo "Installed ~/.claude/skills/prompt-literacy-sequence-designer" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/prompt-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/prompt-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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