Productive Failure & Desirable Difficulty Designer Agent Skill

Redesign a direct instruction sequence to include productive struggle before the explanation phase.

Context cost
7.1k tokensestimated from the bundle, loaded when it triggers
Bundle
1 file · 28.4 kBtext throughout, nothing executable
Licence
not declarednone named by its author
Last change
—v1.0
Servers it uses
Noneruns standalone

What it does

Redesign a direct instruction sequence to include productive struggle before the explanation phase. Use when teaching concepts that benefit from failure-first approaches.

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.

bjorkcognitive-offloadingconsolidationdesirable-difficultygenerationkapurproductive-failurestruggle

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.md28.4 kB · preview
--- # AGENT SKILLS STANDARD FIELDS (v2) name: productive-failure-desirable-difficulty-designer disable-model-invocation: false user-invocable: true effort: medium # EXISTING FIELDS skill_id: "ai-learning-science/productive-failure-desirable-difficulty-designer" skill_name: "Productive Failure & Desirable Difficulty Designer" domain: "ai-learning-science" version: "1.0" evidence_strength: "strong" evidence_sources: - "Kapur (2008) — Productive failure" - "Kapur (2016) — Examining productive failure, productive success, unproductive failure, and unproductive success in learning" - "Bjork (1994) — Memory and metamemory considerations in the training of human beings" - "Bjork & Bjork (2011) — Making things hard on yourself, but in a good way: creating desirable difficulties to enhance learning" - "Soderstrom & Bjork (2015) — Learning versus performance: an integrative review" input_schema: required: - field: "target_concept" type: "string" - field: "current_approach" type: "string" optional: - field: "student_level" type: "string" - field: "subject_area" type: "string" - field: "time_available" type: "string" - field: "prerequisite_knowledge" type: "string" - field: "risk_tolerance" type: "string" output_schema: type: "object" fields: - field: "productive_failure_sequence" type: "object" - field: "desirable_difficulties" type: "array" - field: "failure_safeguards" type: "object" - field: "cognitive_offloading_risks" type: "object" chains_well_with: - "adaptive-hint-sequence-designer" - "worked-example-to-problem-solving-transition-designer" - "self-explanation-prompt-designer" - "intelligent-tutoring-dialogue-designer" teacher_time: "5 minutes" tags: ["productive-failure", "desirable-difficulty", "Kapur", "Bjork", "struggle", "generation", "consolidation", "cognitive-offloading"] ---
70# Productive Failure & Desirable Difficulty Designer
71
72## What This Skill Does
73
74Redesigns a teaching sequence to incorporate productive failure (Kapur, 2008, 2016) and desirable difficulties (Bjork, 1994; Bjork & Bjork, 2011), replacing the standard "teach then practise" model with a "struggle then consolidate" model that produces deeper, more durable learning. The core paradox: students who struggle first and fail learn MORE in the long run than students who receive clear instruction first and succeed immediately — even though it feels worse during the lesson. Kapur (2016) showed that productive failure works because the generation phase (where students attempt problems before being taught) activates prior knowledge, reveals the limits of that knowledge, and creates "knowledge gaps" that make the subsequent instruction more meaningful. Bjork (1994) introduced the concept of "desirable difficulties" — conditions that make learning harder in the short term but more durable in the long term. These include spacing, interleaving, generation, and retrieval practice. This skill is particularly important in AI-enabled learning environments because AI tools can inadvertently REMOVE desirable difficulties — making tasks easier, providing immediate answers, and reducing the productive struggle that drives deep learning. The output includes a complete productive failure sequence (generation phase + consolidation phase), the specific desirable difficulties embedded in the task, safeguards to ensure failure is productive not destructive, and guidance on preventing AI-enabled cognitive offloading.
75
76## Evidence Foundation
77
78Kapur (2008, 2016) developed the productive failure framework through a series of studies in mathematics classrooms. In the canonical design, students are given a complex, novel problem BEFORE any instruction — a problem they are expected to fail at. They work in small groups, generating multiple solution approaches, none of which are fully correct. THEN the teacher provides instruction on the canonical solution, explicitly comparing it to the students' generated approaches. Kapur (2016) found that students in the productive failure condition significantly outperformed students who received direct instruction first on measures of conceptual understanding and transfer — even though the direct instruction students performed better on immediate procedural tests. The key finding: it's not the failure that produces learning, but the GENERATION. Students who generate ideas, even wrong ones, develop richer representations of the problem space, which makes subsequent instruction more meaningful. Bjork (1994) and Bjork & Bjork (2011) articulated the broader principle of desirable difficulties: conditions that reduce performance during learning but enhance long-term retention and transfer. They identified four key desirable difficulties: (1) spacing — distributing practice over time rather than massing it, (2) interleaving — mixing different problem types rather than blocking them, (3) generation — producing answers rather than reading them, and (4) retrieval practice — testing yourself rather than restudying. All four share a common mechanism: they make the learning experience feel harder and less fluent, which paradoxically produces stronger memory traces and deeper understanding. Soderstrom & Bjork (2015) made the critical distinction between LEARNING and PERFORMANCE. Performance is what you can do RIGHT NOW — it's visible and measurable in the moment. Learning is the long-term change in knowledge or skill — it's invisible during the lesson and only measurable later. Desirable difficulties reduce performance (students get more wrong during the lesson) but enhance learning (students remember more and transfer better weeks later). This distinction is essential because teachers — and AI systems — tend to optimise for performance (making students succeed now) rather than learning (making students remember and transfer later).
79
80## Input Schema
81
82The teacher must provide:
83- **Target concept:** What students need to learn. *e.g. "The concept of standard deviation — not just calculating it, but understanding what it MEANS and when to use it" / "The causes of WWI — not just listing them, but understanding how they interacted to produce war" / "Persuasive writing techniques — not just naming them, but choosing the right technique for a specific audience and purpose"*
84- **Current approach:** How it's currently taught. *e.g. "I teach the formula, work through examples, then give practice questions. Students can calculate SD but don't understand what it tells them about data" / "I give a timeline and explain each cause. Students can list causes in an exam but can't explain how they connected" / "I teach techniques one at a time with examples. Students can identify techniques but struggle to use them independently"*
85
86Optional (injected by context engine if available):
87- **Student level:** Year group and proficiency
88- **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.md4,123 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.

≈820
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
6,280
on trigger
The instruction body, read only when the skill fires.
3.5%
of a 200k window
Ten skills this size would take about 36% of the window before you open a file.
050k100k150k200k context window

7.1k 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, 28.4 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md28.4 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.

# Productive Failure & Desirable Difficulty Designer · 7.1k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/productive-failure-desirable-difficulty-designer/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/productive-failure-desirable-difficulty-designer" ] && { [ ! -e .claude/skills/productive-failure-desirable-difficulty-designer ] || { mv .claude/skills/productive-failure-desirable-difficulty-designer "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/productive-failure-desirable-difficulty-designer" .claude/skills/ && echo "Installed .claude/skills/productive-failure-desirable-difficulty-designer"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add garethmanning/productive-failure-desirable-difficulty-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/productive-failure-desirable-difficulty-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/productive-failure-desirable-difficulty-designer/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/productive-failure-desirable-difficulty-designer" ] && { [ ! -e ~/.claude/skills/productive-failure-desirable-difficulty-designer ] || { mv ~/.claude/skills/productive-failure-desirable-difficulty-designer "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/productive-failure-desirable-difficulty-designer" ~/.claude/skills/ && echo "Installed ~/.claude/skills/productive-failure-desirable-difficulty-designer" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/productive-failure-desirable-difficulty-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/productive-failure-desirable-difficulty-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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