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Get Started Free →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'.
.claude/skills/asgard-ai-platform-grad-cognitive-load/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | -32% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 44% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 26% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 43% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 9% | 0% |
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:
markdown## 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]
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-03 | pass→pass | 48,488 | 27,049 | -44% | 1 | 1 | 0% | 6,789 | 3,785 | -44% | 0 | 0 | — |
case-01 | pass→pass | 27,461 | 16,304 | -41% | 1 | 1 | 0% | 3,735 | 3,570 | -4% | 0 | 0 | — |
case-02 | pass→pass | 25,042 | 14,944 | -40% | 1 | 1 | 0% | 3,964 | 3,620 | -9% | 0 | 0 | — |
case-04 | pass→fail | 19,070 | 23,837 | +25% | 1 | 1 | 0% | 2,603 | 4,335 | +67% | 0 | 0 | — |
case-05 | pass→pass | 18,823 | 18,755 | -0% | 1 | 1 | 0% | 2,340 | 3,666 | +57% | 0 | 0 | — |
case-06 | pass→pass | 17,225 | 18,995 | +10% | 1 | 1 | 0% | 2,158 | 4,009 | +86% | 0 | 0 | — |
case-07 | fail→pass | 41,995 | 23,968 | -43% | 1 | 1 | 0% | 7,599 | 5,145 | -32% | 0 | 0 | — |
case-08 | fail→pass | 22,920 | 29,705 | +30% | 1 | 1 | 0% | 3,436 | 4,962 | +44% | 0 | 0 | — |
case-09 | pass→pass | 19,450 | 25,696 | +32% | 1 | 1 | 0% | 2,932 | 4,356 | +49% | 0 | 0 | — |
case-10 | fail→pass | 31,872 | 28,192 | -12% | 1 | 1 | 0% | 4,242 | 5,347 | +26% | 0 | 0 | — |
case-11 | pass→pass | 22,509 | 18,646 | -17% | 1 | 1 | 0% | 3,551 | 4,161 | +17% | 0 | 0 | — |
case-12 | pass→pass | 21,772 | 17,504 | -20% | 1 | 1 | 0% | 2,696 | 3,313 | +23% | 0 | 0 | — |
case-13 | pass→pass | 19,167 | 15,460 | -19% | 1 | 1 | 0% | 2,901 | 3,639 | +25% | 0 | 0 | — |
case-14 | fail→pass | 24,726 | 29,239 | +18% | 1 | 1 | 0% | 3,441 | 4,904 | +43% | 0 | 0 | — |
case-15 | pass→pass | 31,839 | 31,309 | -2% | 1 | 1 | 0% | 4,158 | 5,096 | +23% | 0 | 0 | — |
case-16 | pass→pass | 15,871 | 14,514 | -9% | 1 | 1 | 0% | 2,008 | 3,085 | +54% | 0 | 0 | — |
case-17 | pass→pass | 15,428 | 15,587 | +1% | 1 | 1 | 0% | 2,296 | 3,276 | +43% | 0 | 0 | — |
case-18 | pass→pass | 22,272 | 37,776 | +70% | 1 | 1 | 0% | 2,962 | 5,010 | +69% | 0 | 0 | — |
case-19 | fail→pass | 22,335 | 15,566 | -30% | 1 | 1 | 0% | 3,394 | 3,695 | +9% | 0 | 0 | — |
case-20 | pass→pass | 26,376 | 24,444 | -7% | 1 | 1 | 0% | 3,727 | 4,282 | +15% | 0 | 0 | — |
case-21 | pass→pass | 21,632 | 14,526 | -33% | 1 | 1 | 0% | 3,229 | 3,344 | +4% | 0 | 0 | — |
case-22 | pass→pass | 19,002 | 26,200 | +38% | 1 | 1 | 0% | 2,975 | 4,155 | +40% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +18 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.