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 itallstartedwithaidea/agent-skills --skill cognitive-scaffoldinggit clone --depth 1 https://github.com/itallstartedwithaidea/agent-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/itallstartedwithaidea/agent-skills/cognitive-scaffolding)<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/cognitive-scaffolding"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/cognitive-scaffolding/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/itallstartedwithaidea/agent-skills/cognitive-scaffolding"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/cognitive-scaffolding.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00038 | $0.02335 |
| Opus 5.5 | $0.00015 | $0.00934 |
| Sonnet 5.5 | $0.00008 | $0.00467 |
| Haiku 4.5 | $0.00004 | $0.00233 |
Grade A, and why
cognitive-scaffolding 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 1mo 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 — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cognitive Scaffolding
Part of Agent Skills™ by googleadsagent.ai™
Description
Cognitive Scaffolding structures an agent's context window using principles from cognitive science — primacy effects, recency bias, chunking, and attention allocation. Language models, like human working memory, are not uniform processors. Information placed at the beginning and end of the context receives disproportionate attention (primacy and recency effects), while content in the middle can be effectively invisible. Cognitive Scaffolding exploits these properties to ensure the most critical information receives maximum model attention.
This skill was developed through extensive experimentation on the Buddy™ agent at googleadsagent.ai™, where analysis accuracy improved by measurable margins simply by restructuring how information was arranged in the context window. Campaign performance data placed at strategic positions within the prompt produced significantly better recommendations than the same data placed arbitrarily. The same principle applies to code context, documentation, and any other information an agent must reason over.
The cognitive scaffolding framework organizes context into four zones: the anchor zone (first 5% of context — highest attention, used for identity and immutable rules), the foreground zone (last 20% — high attention, used for the current task and recent context), the structured middle (60% — moderate attention, organized into clearly delimited chunks), and the background zone (15% — lowest attention, used for reference material and fallbacks). Each zone has specific content strategies that maximize the model's ability to utilize the information placed there.
Use When
- Agent accuracy varies inconsistently despite using the same information
- Long context windows degrade performance compared to shorter interactions
- Critical instructions or constraints are occasionally ignored by the agent
- You need to present large amounts of reference data without overwhelming the agent
- Multi-document reasoning requires the agent to attend to specific sections
- You are optimizing agent behavior for specific model architectures
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.
- 1mo ago First seen · 220 lines · 38 tokens per session scan A 4d9a6c96c33a
cognitive-scaffolding is a skill published in the GitHub repository itallstartedwithaidea/agent-skills (44 stars, last pushed 6mo ago), licensed MIT. It adds 38 tokens to every session and 2,335 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.
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