Sweller's Cognitive Load Theory (CLT) for agents writing skill content, designing prompts, building UI/dashboards, and authoring documentation. Working memory holds roughly 4 chunks at a time; CLT classifies load into three types — intrinsic (irreducible task difficulty), extraneous (unnecessary load from poor presentation, ELIMINATE), and germane (load that builds schemas, PROMOTE). Use when writing a SKILL.md body (does this section add extraneous load?), designing prompts (am I asking the model to hold too much at once?), building dashboards (what is the per-screen cognitive budget?), or authoring docs (is intrinsic load minimized via segmentation?). Do NOT use for retrieval and context-loading design (use context-management), prompt engineering tactics (use prompt-craft), or instructional design beyond what grounds the theory.
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name
cognitive-load-theory
description
Sweller's Cognitive Load Theory (CLT) for agents writing skill content, designing prompts, building UI/dashboards, and authoring documentation. Working memory holds roughly 4 chunks at a time; CLT classifies load into three types — intrinsic (irreducible task difficulty), extraneous (unnecessary load from poor presentation, ELIMINATE), and germane (load that builds schemas, PROMOTE). Use when writing a SKILL.md body (does this section add extraneous load?), designing prompts (am I asking the model to hold too much at once?), building dashboards (what is the per-screen cognitive budget?), or authoring docs (is intrinsic load minimized via segmentation?). Do NOT use for retrieval and context-loading design (use context-management), prompt engineering tactics (use prompt-craft), or instructional design beyond what grounds the theory.
license
MIT
compatibility
{"notes":"Markdown, Git, any agent-skill runtime"}
allowed-tools
Read Grep
metadata
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Concept Card
What it is: Cognitive Load Theory (CLT) is a psychological framework originating with John Sweller (1988) that explains how human working memory processes new information and what design decisions help or hinder schema formation. It is the scientific backbone behind "keep it simple" intuitions — but with actionable precision about which complexity to cut and which to preserve.
Mental model: Working memory is a small workspace — roughly 4 independent chunks at once (Miller's 7±2, revised by Cowan to ~4). CLT divides the total load on that workspace into three additive buckets: intrinsic (the task itself), extraneous (how you presented the task), and germane (the effort of building a mental model). Total load must stay below capacity or learning and comprehension fail.
Why it exists: Without CLT, agents default to "simplify" without knowing what to cut. They might remove worked examples (germane load, valuable) while keeping verbose prose around a table (extraneous load, wasteful). CLT gives a precise vocabulary for the tradeoff.
What it is NOT: Not a general UX heuristic. Not the same as plain-language writing. Not about reducing all complexity — germane load (building schemas) is desirable and should not be minimized. Not retrieval design (see context-management).
One-line analogy: CLT is like RAM management for humans — intrinsic load is the program you must run, extraneous load is unnecessary background processes, and germane load is the OS caching data so the next run is faster.
Common misconception: "Reducing cognitive load always means making things shorter or simpler." Wrong — cutting worked examples, removing context, or over-abstracting increases intrinsic load and destroys germane load. The correct target is extraneous load only.
Cognitive Load Theory
Domain Context
What is this skill? Sweller's Cognitive Load Theory for agents writing skill content, designing prompts, building UI/dashboards, and authoring documentation. Use when evaluating whether a skill body, prompt, UI screen, or doc section is imposing unnecessary cognitive load on its reader or user. Do NOT use for retrieval and context-loading design (use context-management), prompt engineering tactics (use prompt-craft), or instructional design beyond what grounds the theory here.
Coverage
The three-load taxonomy (intrinsic, extraneous, germane) with definitions and examples; working memory limits and chunking strategies; application to skill writing, prompt design, and UI/dashboard cognitive budgets; a worked example reducing extraneous load in agent audit output; and common failure modes including the "simplify to lose germane content" trap. Does not cover Mayer's multimedia learning extensions, split-attention effect research design, or assessment/test design.
Philosophy
Every agent output — a SKILL.md body, a prompt, a dashboard widget, a Linear comment — competes for space in the reader's working memory alongside everything else they hold. An agent that generates a 3-paragraph preamble before the actual answer forces the reader to hold that prose until they reach the substance. An agent that uses a consistent table format for comparative data lets the reader chunk the format into a single schema slot and spend their remaining capacity on the content. CLT is the theoretical justification for structure-over-prose, examples-before-explanation, and segmentation. Without it, quality guidance is cargo-culted. With it, agents can reason precisely about why a design choice reduces or increases cognitive burden.
Cross-Domain Synergy
CLT is the theoretical foundation that neighboring skills draw on implicitly:
teaching-patterns: Uses CLT when choosing progressive disclosure (segmentation), worked examples (germane load promotion), and scaffolding removal.
context-management: The discipline of controlling what enters a session is CLT applied to the agent's own working memory, not just the human reader's.
context-window: Token budget management mirrors the working-memory budget — both measure available capacity against load.
compression: Token-efficient compression is an application of extraneous-load elimination.
editorial-standards: Structure-over-prose editorial rules are CLT operationalized for written output.
1. When to Invoke CLT
Use this skill when answering any of these questions:
Question
CLT lens
"Is this skill section too long?"
Which load type is the length adding? Cut only extraneous.
2.1 Intrinsic Load — irreducible difficulty of the task itself
Determined by the element interactivity of the material: how many things must be held simultaneously because they interact with each other. High interactivity = high intrinsic load.
Signal
Example
Many interdependent concepts
Debugging a recursive algorithm while tracking stack frames
Prior knowledge required
Reading a diff that assumes familiarity with a domain-specific pattern
Multiple steps that reference each other
A 7-step migration guide where step 4 depends on step 2's output
Management strategies:
Segmentation: Break into sub-tasks where each segment can be completed before the next is shown.
Prior-knowledge activation: Surface prerequisite concepts before the main material so they pre-load as chunks.
Worked examples: Replace problem-solving with example-study while schemas are forming (reduces intrinsic load during early learning).
Rule: Intrinsic load cannot be eliminated — the task is what it is. It can only be managed by sequencing and scaffolding.
2.2 Extraneous Load — unnecessary load from poor presentation
Load that arises from the way the information is presented, not from the information itself. This is the only load type that should be aggressively eliminated.
Source
Example
Split-attention effect
Having to look at a diagram in one place and its legend somewhere else
Redundancy effect
Restating in prose what a table already communicates clearly
Coherence violation
Including interesting-but-irrelevant context that the reader must process and then discard
Verbose preamble
Three paragraphs explaining what you're about to say before saying it
Inconsistent formatting
Different heading levels for the same type of content across sections
Missing visual hierarchy
A flat wall of text with no structural cues
Elimination tactics:
Integrate labels directly into diagrams (remove the need to look between locations).
Use tables for comparative data (reader chunks the format as a single schema slot).
Remove decorative prose around already-clear structures.
Apply consistent formatting so structure itself becomes a chunk the reader can ignore.
2.3 Germane Load — load that builds schemas
Cognitive effort spent constructing, organizing, or automating a mental model. This load is desirable — it is the mechanism of learning and skill acquisition. The goal is to maximize germane load within available capacity (total load ≤ capacity).
Source
Example
Worked examples
Step-by-step solutions with explicit reasoning
Variability practice
Multiple examples of the same concept in different contexts
Self-explanation prompts
"Why does this rule apply here?" nudges the reader to generate their own connections
Analogies
Mapping a new concept onto a familiar schema (e.g., "CLT is like RAM management")
Contrasting cases
Showing what's wrong alongside what's right
Promotion tactics:
Include worked examples even when they lengthen the skill body.
Provide multiple instantiations of abstract rules (varied examples increase schema strength).
Use analogies that map onto known structures.
Show failure modes alongside success patterns.
3. Working Memory Limits
The ~4-chunk finding
Cowan (2001) revised Miller's 7±2 downward: the reliable limit of working memory is approximately 4 independent chunks — not 7. Crucially, what counts as a "chunk" depends on expertise: an expert chess player chunks a board position into one unit; a novice processes each piece separately.
Implications:
Situation
Working-memory implication
Novice reader encountering an unfamiliar pattern
Each element is a separate chunk; total capacity exhausted quickly
Expert reader encountering a known pattern
The entire pattern is one chunk; more capacity for novel details
An agent writing for a mixed audience
Design for the novice's chunking capacity; experts will auto-compress
A 10-column dashboard widget
If column meanings are not already chunked, viewer hits capacity before first insight
Chunking strategies
Label by type, not by instance: Instead of 10 separate data points, introduce a category that chunks them (e.g., "fulfillment metrics" as a header that frames the following 5 rows).
Consistent structure as a free chunk: When every SKILL.md section follows the same format, the reader chunks the format itself — freeing all 4 slots for content.
Pre-load schemas: Front-load the concept card / summary so the reader builds a lightweight schema before encountering detail.
Segment before elaborating: Deliver the simplest complete unit first, add complexity in subsequent passes.
4. Application: Writing Skills
When authoring a SKILL.md body, apply the three-load taxonomy to each section before committing.
Checklist
Question
If YES → action
Does this section restate what the heading already conveys?
Extraneous redundancy → cut the restatement
Is this section longer than the information it communicates?
Extraneous verbosity → compress to table or bullets
Does this section require the reader to cross-reference another section to understand it?
Split-attention → integrate or re-sequence
Does this section contain a worked example?
Germane → keep it; do not cut for length
Does this section use a consistent format with neighboring sections?
If YES: format-as-chunk is working → maintain consistency
Is this section building a schema the reader will use in practice?
Germane → expand if needed
Anti-patterns in skill bodies
Anti-pattern
Load type
Fix
Four-paragraph intro before the actual content
Extraneous (preamble)
Move preamble to Concept Card; open sections with the content
"This section covers X. X is important because..."
Extraneous (redundancy)
Remove the meta-commentary; start with X
Removing the worked example to save tokens
Germane destruction
Keep the example; cut prose around it instead
Using different heading levels for equivalent content
Extraneous (inconsistency)
Normalize heading hierarchy
5. Application: Designing Prompts
Prompt design is working-memory design for the model. The model's context window is not the same as working memory, but the principles transfer: a prompt that requires holding many interdependent instructions simultaneously creates functional equivalents of high cognitive load.
High-intrinsic-load prompt signals
Signal
Why it's high load
Multiple tasks stated in one prompt without sequencing
Model must track all tasks simultaneously
Later instructions that modify earlier ones
Model must retroactively reinterpret prior context
Abstract instructions with no examples
Model must construct the intent without schema
Long preamble before the actual instruction
Model must retain preamble while processing what follows
CLT-informed prompt design
Strategy
What it does
Segment high-intrinsic tasks
Split into sequential prompts; each builds on the prior output
Example-first for novel formats
Show the output format before describing it abstractly
Front-load the primary task
State what you want in sentence 1; justification follows
Consistent instruction schema
Reuse the same structure across related prompts so the format becomes a chunk
One constraint per instruction
Avoid compound constraints ("do X but not Y, unless Z")
Example: refactoring a high-load prompt
Before (high extraneous load):
I need you to look at this audit output from our skill review system and think about which findings are the most important ones and then also consider whether any of them might actually be incorrect given the context of how skills work in our system, and then write a summary that could be used in a Linear comment explaining what needs to be done.
After (segmented, reduced extraneous load):
Step 1: Classify each finding below as VALID or INVALID. A finding is invalid if it misunderstands how our skill schema works.
[findings]
Step 2: Of the VALID findings, rank by severity: BLOCKING, ADVISORY, NITPICK.
Step 3: Write a Linear comment summarizing the BLOCKING findings and their required fixes.
The second version segments intrinsic load, removes preamble verbosity, and provides a consistent schema for each step.
6. Application: UI / Dashboards
Per-screen cognitive budget is a CLT concept: a screen imposes load through the number of independent data points, interaction affordances, and visual decisions the viewer must process simultaneously.
Budget heuristics
Guideline
Rationale
Max 4-5 independent data points per widget
Matches working-memory chunk limit
Group related metrics under a labeled category
The label becomes a single chunk
Consistent card structure across the dashboard
Format chunked → all capacity for content
Progressive disclosure for detail
Show summary first; reveal detail on demand
Avoid same-visual-weight competing CTAs
Multiple salient actions compete for the same chunk slot
Common dashboard extraneous-load sources
Source
Fix
12-column table with no grouping
Group by category; hide secondary columns behind expand
Redundant title + axis label + legend
Integrate axis label into chart; remove redundant title
Color-coded data with no legend visible
Move legend adjacent to chart or encode in tooltip
Mixed decimal precision across a single widget
Normalize precision so reader doesn't parse each format separately
Status indicators without consistent icon meaning
Define a fixed icon vocabulary (same icon = same meaning everywhere)
7. Worked Example: Reducing Extraneous Load in Audit Output
Before (high extraneous load):
Audit Results for skills/my-skill/SKILL.md:
After reviewing the skill carefully I noticed that there are a few things that
could be improved. The first issue I want to mention is that the description field
in the frontmatter doesn't actually have the use_when and not_for patterns that
we require. Also, looking at the relations section, I see that verify_with is
empty which means the skill has no verification partners. Additionally, the eval
file has only one eval which is below the minimum we set.
I think these should be fixed but the skill is otherwise in reasonable shape.
After (low extraneous load, structured):
## Audit: skills/my-skill/SKILL.md
| Finding | Severity | Fix |
|---------|----------|-----|
| description missing use_when / not_for pattern | BLOCKING | Add "Use when: ... Do NOT use for: ..." |
| relations.verify_with is empty | ADVISORY | Add at least one verify_with skill |
| evals/evals.json has 1 eval (min: 2) | BLOCKING | Add a second eval case |
Overall: 2 BLOCKING. Fix before marking PASS.
What changed:
Removed verbal preamble ("After reviewing the skill carefully I noticed...")
Replaced prose with a table — each finding is now one chunk
Made severity and fix explicit rather than embedded in prose
Removed hedging ("I think", "otherwise in reasonable shape") — extraneous load with no information content
8. Common Failure Modes
Failure mode
Load consequence
Detection signal
"Simplify" interpreted as "make shorter"
Cuts germane content (worked examples, analogies)
The reader now has less schema, more confusion
Removing context to reduce word count
Increases intrinsic load (reader must infer what was removed)
Reader asks follow-up questions that context would have answered
Adding beautiful prose to describe what a table already shows
Extraneous redundancy
Section length ≠ information density
Designing a prompt in one large block
High intrinsic load; model can't segment
Model produces mixed output or drops constraints
Consistent formatting abandoned mid-document
Format chunk broken; reader spends capacity re-orienting
Equating "expert content" with "high cognitive load"
False: experts chunk more; appropriate material for audience reduces load
Ask: who is the reader? What do they already chunk?
9. Rating Criteria
Rate any output (skill body, prompt, doc section, dashboard widget) against CLT:
Score
Criterion
5
Extraneous load eliminated. Germane load preserved or promoted. Structure is consistent and chunks the format. Worked examples present where schema-formation is the goal. Intrinsic load segmented where it exceeds single-pass capacity.
4
Extraneous load mostly eliminated. Minor redundancy remains but does not impede comprehension. Germane content intact.
3
Some extraneous elements (preamble, redundant prose) present but not dominant. Germane content mostly intact. Structure inconsistent in places.
2
Significant extraneous load (verbose intro, split-attention, redundancy). Some germane content removed. Reader must do significant work to find substance.
1
Dominant extraneous load (wall of text, no structure, redundancy throughout). Germane content absent or destroyed. Reader cannot reliably extract the core information.
Health Block
Field
Value
structural_verdict
UNVERIFIED
truth_verdict
UNVERIFIED
comprehension_verdict
UNVERIFIED
application_verdict
UNVERIFIED
last_audited
2026-05-19
eval_failed_ids
[]
drift_status
UNKNOWN
freshness
2026-05-19
The four-verdict shape is per ADR 0011. All four verdicts default to UNVERIFIED on a fresh port; gates 1–8 and the application-eval pilot populate them in subsequent audits.
Do NOT Use When
Instead of this skill
Use
Why
cognitive-load-theory
context-management
context-management owns retrieval and session working-set design
cognitive-load-theory
prompt-craft
prompt-craft owns the full prompt engineering tactic set
cognitive-load-theory
teaching-patterns
teaching-patterns owns explanation and pedagogy methods
cognitive-load-theory
compression
compression owns token-efficiency strategies for context windows
