Apply Cognitive Load Theory to optimize instructional design by managing intrinsic, extraneous, and germane load within working memory limits. Use this skill when the user needs to diagnose why learners are overwhelmed, redesign training or documentation for better comprehension, evaluate UI/UX information architecture for cognitive burden, or when they ask 'why is this tutorial confusing', 'how to simplify complex instructions', or 'what causes information overload'.
npx skills add https://github.com/asgard-ai-platform/skills --skill grad-cognitive-load
Cognitive Load Theory (Sweller, 1988) is grounded in the architecture of human cognition: working memory is severely limited in capacity (7 +/- 2 items) and duration, while long-term memory is essentially unlimited. Effective instructional design must manage three types of cognitive load — intrinsic (task complexity), extraneous (poor design), and germane (schema construction) — so that total load does not exceed working memory capacity.
IRON LAW: Working memory capacity is FIXED and limited —
instructional design must minimize extraneous load to maximize
germane processing. Total load (intrinsic + extraneous + germane)
must not exceed working memory capacity.
Key assumptions:
Assess how many information elements must be processed simultaneously:
| Source | Description | Design Flaw |
|--------|-------------|-------------|
| Split-attention | Integrating spatially/temporally separated sources | Text far from diagram |
| Redundancy | Processing identical information in multiple formats | Narration duplicating on-screen text |
| Transient information | Information disappears before processing completes | Fast animations without pause |
| Expertise reversal | Scaffolding that helps novices but hinders experts | Forced step-by-step for advanced users |
| Seductive details | Interesting but irrelevant information | Decorative images, tangential stories |
Strategies to manage total cognitive load:
## Cognitive Load Analysis: [Context]
### Intrinsic Load Assessment
- Element interactivity: [Low/Medium/High]
- Key interacting elements: [list]
- Learner expertise level: [Novice/Intermediate/Expert]
### Extraneous Load Audit
| Source | Present? | Severity | Fix |
|--------|----------|----------|-----|
| Split-attention | [Yes/No] | [High/Med/Low] | [solution] |
| Redundancy | [Yes/No] | [High/Med/Low] | [solution] |
| Transient info | [Yes/No] | [High/Med/Low] | [solution] |
| Seductive details | [Yes/No] | [High/Med/Low] | [solution] |
### Load Budget
- Estimated total load: [Within/Exceeding capacity]
- Extraneous reduction potential: [High/Medium/Low]
### Redesign Recommendations
1. [Primary extraneous load reduction]
2. [Segmenting or sequencing change]
3. [Germane load enhancement]
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
Convert PyTorch AT_DISPATCH macros to AT_DISPATCH_V2 format in ATen C++ code. Use when porting AT_DISPATCH_ALL_TYPES_AND*, AT_DISPATCH_FLOATING_TYPES*, or other dispatch macros to the new v2 API. For ATen kernel files, CUDA kernels, and native operator implementations.
Write docstrings for PyTorch functions and methods following PyTorch conventions. Use when writing or updating docstrings in PyTorch code.
Take asgard-ai-platform/grad-cognitive-load from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.