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 human-avatar/skills-for-humanity --skill s4h-cognition-attentiongit clone --depth 1 https://github.com/human-avatar/skills-for-humanityWrote 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/human-avatar/skills-for-humanity/s4h-cognition-attention)<a href="https://agentmods.dev/skills/human-avatar/skills-for-humanity/s4h-cognition-attention"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-cognition-attention/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/human-avatar/skills-for-humanity/s4h-cognition-attention"><img src="https://agentmods.dev/badge/skills/human-avatar/skills-for-humanity/s4h-cognition-attention.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 | $0.00087 | $0.01610 |
| Opus 5 | $0.00044 | $0.00805 |
| Sonnet 5 | $0.00017 | $0.00322 |
| Haiku 4.5 | $0.00009 | $0.00161 |
Grade A, and why
s4h-cognition-attention 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 13d 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.
Cognition: Attention
Attention is the scarcest cognitive resource — and unlike memory or reasoning, it cannot be trained to be unlimited. It can only be allocated, protected, and defended. Most focus problems are not character failures; they are design failures. The environment, the workflow, or the communication system is structured in a way that systematically fragments attention and prevents sustained engagement with what matters most.
The attention economy framing, developed from William James's foundational work on voluntary and involuntary attention, treats focus as a resource subject to supply and demand. What demands attention is not random: salience (bright, loud, moving things), novelty (what's new or unexpected), emotional charge (threats, social signals, strong affect), and personal relevance all reliably override deliberate focus. Design your attention environment knowing what the competition is.
The key distinction: attention threats come in two forms. Capture — things that involuntarily seize focus through sensory or emotional hooks — and depletion — things that gradually drain the capacity for sustained focus even when no single interruption is dramatic. Both matter. Capture is visible and acute; depletion is slow, cumulative, and often invisible until cognitive capacity is exhausted.
Your Process
Step 1: Define the Attention Context Identify whose attention, in what context, for what purpose. Individual focus (a person trying to do deep work)? Group attention (a team, a meeting)? Communication attention (an audience you need to hold)? The diagnosis differs by context.
Framing check: Confirm the specific attention context before continuing. State what you've identified — whose attention, in what setting, and what the goal is — in one sentence, then use AskUserQuestion:
- Question: "I'm reading this as: [your one-sentence framing of whose attention is at issue, in what context, and what you're trying to achieve]. Is that right?"
- Header: "Framing"
- Options:
- Yes — proceed — framing is correct
- Adjust — one element is off; user will correct it before you continue
- Reframe — different situation than read; incorporate the correction before proceeding
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.
- 13d ago First seen · 119 lines · 87 tokens per session scan A 7abb6a0a797b
s4h-cognition-attention is a skill published in the GitHub repository human-avatar/skills-for-humanity (223 stars, last pushed 1mo ago), licensed MIT. It adds 87 tokens to every session and 1,610 once invoked, about $0.0004 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 2026-08-30.
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