A
licenseNot graded
qualityB
maintenanceEnables users to schedule 90-minute ultradian focus blocks, triage incoming interruptions against contact priority thresholds, and capture execution checkpoints for instant resumption.
7
MIT
personal-agent-skills
by Alpha-Park
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
personal-agent-skills
Sovereign Personal Agent Master Orchestrator for the OpenClaw / GenPark ecosystem. Built to liberate users from prompt fatigue, notification anxiety, and manual calendar juggling through zero-dependency mathematical modeling, circadian rhythm alignment, and ambient intent fusion.
flowchart TD
subgraph AmbientInputs [Ambient Multi-Modal Sensing]
A1[HRV & Physiological Readiness DFA-alpha1]
A2[Screen Active Context & Typing Rhythm]
A3[Calendar Proximity & Meeting Deadlines]
A4[Inbound Notifications & Urgency Stream]
end
subgraph PersonalAgentKernel [Personal Agent Sovereign Kernel]
K1[Circadian Energy Curve Modeler]
K2[Ambient Focus State & Flow Classifier]
K3[Proactive Intervention Utility Arbiter]
end
subgraph AutonomousOutcomes [Proactive Interventions]
O1[Optimal Cognitive Workload Scheduling]
O2[Silent Do-Not-Disturb Flow Protection]
O3[Contextual Pre-Meeting Briefing]
O4[Zero-Interruption Notification Drawer]
end
AmbientInputs --> PersonalAgentKernel
PersonalAgentKernel --> AutonomousOutcomesDistinctive Personal Agent Pillars
Circadian Energy Awareness: Matches heavy cognitive tasks to physiological peak focus windows ($E(h) = R \cdot \exp(-((h - \mu)/\sigma)^2)$) rather than naive calendar packing.
Ambient Zero-Prompt Ingress: Passively deduces user flow state from typing cadence, screen activity, and meeting proximity without demanding manual prompts.
Utility-Gated Interruption Policy: Mathematically computes net interruption utility ($U = ext{Importance} - c \cdot ext{Focus}$), preventing distraction unless urgent.
Local-First & Sovereign: 100% pure Python standard library. Zero third-party trackers, zero external telemetry leaks.
Related MCP server: Personal Agent Adaptive Deep Work & Focus Guardian
Curated Personal Agent Skills in the Ecosystem
Skill Repository | Core Personal Capability |
| Real-time autonomic nervous system fatigue and DFA $\alpha_1$ aerobic-anaerobic threshold detection. |
| Zero-click glance surface intent recognition on lockscreens and ambient displays. |
| Native desktop/mobile screen context OCR, UI element bounding, and active window state. |
| Episodic personal memory vault using ACT-R spreading activation decay. |
| Automated personal inbox triage via Eisenhower matrix and auto-drafting. |
| Converts spoken personal notes and meetings into structured Linear/Jira tasks. |
| Spaced-repetition lifelong learning optimizer powered by SuperMemo SM-2. |
| Personal inbound call screening, IVR navigation, and automated appointment setting. |
| Personal tax deduction tracking, receipt ledgering, and multi-currency expense reconciliation. |
| Sovereign Agent-to-Agent preference alignment using the Gale-Shapley stable matching algorithm. |
Quickstart
from client import PersonalAgentOrchestrator
orchestrator = PersonalAgentOrchestrator()
tasks = [{"id": "t1", "title": "System Architecture Deep-Dive", "cognitive_load": 0.95}]
res = orchestrator.schedule_circadian_focus_blocks(tasks, circadian_peak_hour=10.5, current_hrv_readiness=0.90)
print("Scheduled Slot:", res["schedule"][0]["scheduled_slot"])
print("Average Cognitive Fit:", res["average_cognitive_fit"])FastMCP Integration
python -m personal-agent-skills.mcp_serverLicense
MIT
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