A specialized tool for instructional designers and learners to audit training materials for evidence-based difficulty strategies that strengthen long-term memory retention.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install bookforge-desirable-difficulty-classifier
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install bookforge-desirable-difficulty-classifier using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Desirable Difficulty Classifier?
The Desirable Difficulty Classifier is a precision instrument designed to evaluate whether the challenges within a learning program actually contribute to knowledge retention or simply create pointless friction. Based on the principles from the learning science masterpiece Make It Stick, this skill helps you distinguish between desirable difficulties—those that trigger deep encoding—and undesirable ones that lead to the illusion of mastery without durable results.
By leveraging this tool within the Openclaw Skills ecosystem, developers, educators, and L&D professionals can move beyond subjective critiques. It provides a structured analysis of six proven cognitive strategies: spacing, interleaving, variation, retrieval, generation, and elaboration. Whether you are auditing a corporate onboarding program or a self-study plan, this skill ensures that every ounce of effort expended by the learner translates into measurable, long-term skill development.
Desirable Difficulty Classifier Use Cases
- Instructional designers performing a pre-launch evidence-based audit of new course outlines.
- Corporate L&D teams diagnosing why employees are failing to retain information from training sessions.
- Self-directed learners seeking to replace passive habits like rereading with active, high-impact study methods.
- Coaches and trainers evaluating practice regimens for athletes, musicians, or technical professionals.
- Educational consultants auditing syllabi to ensure they meet modern cognitive psychology standards.
How Desirable Difficulty Classifier Works
- Context Extraction: The skill gathers descriptions of the learning activity, the target skill, and the learner's background level.
- Structural Mapping: It identifies the core design elements, including practice timing, order, variation, and assessment methods.
- Bjork Classification: Each element is screened against Bjork's two-part test to determine if the effort strengthens encoding and if the learner is capable of overcoming the challenge.
- Taxonomy Mapping: The identified desirable difficulties are mapped against the six-strategy taxonomy to identify presence, partial implementation, or complete absence.
- Priority Ranking: The skill identifies the most critical gaps (e.g., lack of retrieval practice or spacing) and ranks them by their potential impact on learning outcomes.
- Report Generation: A comprehensive report is produced, including a classification table, strategy presence status, and actionable redesign recommendations.
Desirable Difficulty Classifier Setup
To use this skill within your environment, ensure you have the Openclaw Skills framework installed. You can invoke the classifier by providing a description of your learning activity.
# Example of initiating an audit via CLI if applicable
openclaw run desirable-difficulty-classifier --input "training_syllabus.md"
No specialized file access is required unless you wish to provide a specific document (PDF/Markdown) for the agent to read.
Desirable Difficulty Classifier Data Schema & Taxonomy
The skill produces a structured difficulty analysis report using the following taxonomy:
| Data Component | Description |
|---|---|
| Classification Table | Labels elements as DESIRABLE, UNDESIRABLE, or AMBIGUOUS based on encoding strength. |
| Strategy Presence | A status report (PRESENT/PARTIAL/ABSENT) for Spacing, Interleaving, Variation, Retrieval, Generation, and Elaboration. |
| Redesign Priorities | A ranked list of specific changes, including effort estimates and expected impact. |
| Screening Questions | A 7-question self-audit checklist for future instructional design projects. |
Desirable Difficulty Classifier Advanced Features
- Bilateral Contrast Analysis: Directly compares effective mechanisms against common but ineffective counterparts like massed practice or rereading.
- Effort-Impact Triage: Automatically categorizes redesign suggestions by the level of work required versus the expected boost in learner retention.
- Hybrid Execution Mode: Combines automated cognitive mapping with human-in-the-loop decision-making for final instructional implementation.
- Evidence-Based Grounding: Every classification is linked to specific chapters and findings from the science of successful learning.
SKILL.md
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