Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add deciqAI/knowledge-skills --skill cognitive-load-theorygit clone --depth 1 https://github.com/deciqAI/knowledge-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/deciqai/knowledge-skills/cognitive-load-theory)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/cognitive-load-theory"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/cognitive-load-theory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/cognitive-load-theory"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/cognitive-load-theory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00119 | $0.01693 |
| Opus 5 | $0.00060 | $0.00847 |
| Sonnet 5 | $0.00024 | $0.00339 |
| Haiku 4.5 | $0.00012 | $0.00169 |
Grade A, and why
cognitive-load-theory scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cognitive Load Theory
Overview
Working memory holds ~4 chunks of novel information — a hard limit. Instruction that exceeds it produces no learning regardless of effort. CLT (Sweller 1988) identifies three load types: intrinsic (task complexity), extraneous (poor presentation), germane (schema-building effort). Target: minimize extraneous, manage intrinsic by sequencing low-to-high element-interactivity, maximize germane.
Composes with metacognition (learners aware of limits pace themselves), deep-work (same working-memory conditions), and api-and-interface-design (UX design = instructional design).
When to Use
- Designing training, documentation, onboarding, or instructional material
- Learners aren't understanding despite good intent and reasonable material
- Diagnosing why a course / tutorial / interface is underperforming
- Someone says "cognitive load," "working memory," "too much at once," "this is overwhelming"
Not when: audience is already expert (expertise-reversal); bottleneck is motivational not cognitive.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete design problem → run The Process directly.
- Coach mode: user is unfamiliar → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- One-line: when learners aren't getting it, check if working-memory load exceeds capacity — the fix is usually to reduce extraneous load, not add more explanation.
- Check fit. If audience is expert, novice-friendly CLT techniques may backfire (expertise-reversal).
- Elicit the specific failure. Who's learning what, where stuck, what's the current instruction?
[WAIT — do not advance until user responds]
- Diagnose load types one question at a time: intrinsic too high? Extraneous load sources? Expertise match?
[WAIT — do not advance until user responds]
- Close: redesigned instruction with specific CLT effects applied + test with target learners.
[WAIT — do not advance until user responds]
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 119 lines · 119 tokens per session scan A 088ad8fc3cfd
cognitive-load-theory is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 8d ago), licensed MIT. It adds 119 tokens to every session and 1,693 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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