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 natea/ExoMind --skill smart-recommendationsgit clone --depth 1 https://github.com/natea/ExoMindWrote 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/natea/exomind/smart-recommendations)<a href="https://agentmods.dev/skills/natea/exomind/smart-recommendations"><img src="https://agentmods.dev/badge/skills/natea/exomind/smart-recommendations/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/natea/exomind/smart-recommendations"><img src="https://agentmods.dev/badge/skills/natea/exomind/smart-recommendations.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 | $0.00000 | $0.03910 |
| Opus 5 | $0.00000 | $0.01955 |
| Sonnet 5 | $0.00000 | $0.00782 |
| Haiku 4.5 | $0.00000 | $0.00391 |
Grade A, and why
smart-recommendations 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 5d 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 — 584 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Smart Recommendations Skill
Purpose
AI-powered recommendation engine that analyzes patterns in your life data to provide intelligent suggestions for optimizing schedule, tasks, meetings, energy management, and goal achievement.
Core Capabilities
1. Schedule Optimization
- Best Times for Tasks: Analyzes completion history to suggest optimal time blocks
- Energy-Based Scheduling: Recommends high-focus tasks during peak energy periods
- Context Switching Reduction: Suggests task batching by context/type
- Buffer Time Insertion: Identifies where breathing room is needed
- Deadline Alignment: Recommends when to start tasks based on historical velocity
2. Task Prioritization Intelligence
- Eisenhower Matrix Auto-Assignment: Suggests urgency/importance quadrants
- Impact Scoring: Estimates task value based on goals and dependencies
- Effort Estimation: Predicts realistic time requirements from similar tasks
- Procrastination Detection: Flags repeatedly postponed tasks with suggestions
- Quick Win Identification: Highlights high-impact, low-effort opportunities
3. Meeting Optimization
- Meeting Value Assessment: Analyzes if your presence is truly needed
- Decline Suggestions: Recommends meetings to skip with reasoning
- Reschedule Opportunities: Suggests better timing for recurring meetings
- Meeting-Free Day Protection: Identifies days needing full focus time
- Preparation Time Blocking: Auto-suggests prep time before important meetings
4. Focus Time Protection
- Deep Work Block Suggestions: Recommends when to schedule uninterrupted time
- Distraction Pattern Analysis: Identifies common interruption sources
- Communication Batching: Suggests specific times for email/chat checking
- Context Preservation: Recommends how to minimize task switching
- Flow State Triggers: Identifies conditions that enable your best work
5. Energy Management
- Break Reminders: Suggests breaks based on work intensity and duration
- Energy Dip Detection: Identifies low-energy periods and suggests appropriate activities
- Recovery Time Calculation: Recommends downtime after intense periods
- Burnout Prevention: Flags unsustainable pace before it's too late
- Ultradian Rhythm Alignment: Suggests work/rest cycles matching natural patterns
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.
- 5d ago First seen · 584 lines · 0 tokens per session scan A a5b7c3cd0f0a
smart-recommendations is a skill published in the GitHub repository natea/ExoMind (21 stars, last pushed 10mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,910 tokens. 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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