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 fabioc-aloha/Alex_Skill_Mall --skill deep-work-optimizationgit clone --depth 1 https://github.com/fabioc-aloha/Alex_Skill_MallWrote 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/fabioc-aloha/alex_skill_mall/deep-work-optimization)<a href="https://agentmods.dev/skills/fabioc-aloha/alex_skill_mall/deep-work-optimization"><img src="https://agentmods.dev/badge/skills/fabioc-aloha/alex_skill_mall/deep-work-optimization.svg" alt="Measured on agentmods" 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 | $0.00020 | $0.02715 |
| Opus 5 | $0.00010 | $0.01358 |
| Sonnet 5 | $0.00004 | $0.00543 |
| Haiku 4.5 | $0.00002 | $0.00271 |
Grade A, and why
deep-work-optimization 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 4d 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 — 368 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Deep Work Optimization
Focus blocks, distraction management, and flow state triggers for cognitively demanding work.
Metadata
| Field | Value |
|---|---|
| Skill ID | deep-work-optimization |
| Version | 1.0.0 |
| Category | Productivity |
| Difficulty | Intermediate |
| Prerequisites | None |
| Related Skills | cognitive-load-management, meeting-efficiency, frustration-recognition |
Overview
Deep work is the ability to focus without distraction on cognitively demanding tasks. In Cal Newport's framework, deep work produces rare and valuable results that can't be replicated by shallow multitasking.
The Deep Work Hypothesis
The ability to perform deep work is becoming increasingly rare at exactly the same time it is becoming increasingly valuable in our economy.
This skill helps maximize deep work capacity for dissertation writing, complex analysis, architecture design, and creative problem-solving.
Module 1: Understanding Deep Work
Deep vs. Shallow Work
| Deep Work | Shallow Work |
|---|---|
| Cognitively demanding | Logistical, low-value |
| Creates new value | Maintains status quo |
| Difficult to replicate | Easily automated |
| Requires uninterrupted focus | Tolerates interruption |
| Examples: Writing, coding, analysis | Examples: Email, meetings, admin |
The Attention Residue Problem
When you switch tasks, attention doesn't fully transfer—residue from the previous task reduces cognitive capacity.
Research finding (Leroy, 2009): People who frequently switch tasks perform worse than those who complete tasks before moving on.
Implication: Batch similar shallow work; protect deep work blocks from interruption.
Deep Work Capacity
| Factor | Impact on Capacity |
|---|---|
| Practice | Increases (like a muscle) |
| Rest | Essential for recovery |
| Start of day | Typically highest capacity |
| After interruption | 23 minutes to recover (Iqbal & Horvitz) |
| Caffeine | Temporary boost, then crash |
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.
- 4d ago First seen · 368 lines · 20 tokens per session scan A a7f3a3428546
deep-work-optimization is a skill published in the GitHub repository fabioc-aloha/Alex_Skill_Mall (4 stars, last pushed today), licensed MIT. It adds 20 tokens to every session and 2,715 once invoked, about $0.0001 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 2026-09-03.
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