Personal Agent Attention Residue Context Switch
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Personal Agent Attention Residue Context Switchbatch my unfinished tasks to reduce attention residue and recover deep work hours"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
genpark-personal-agent-attention-residue-context-switch-skill
GenPark Personal Agent Attention Residue Context Switch models Sophie Leroy's attention residue theory and Shannon application entropy to protect personal deep work.
flowchart TD
A[Task Switch Log & Unfinished Task States] --> B[Leroy Attention Residue Decay Engine]
B --> C[Cognitive Tax Minutes Lost Per Switch]
D[Active Application Time Distribution] --> E[Shannon Fragmentation Entropy Calculator]
F[Unstructured To-Do List] --> G[Optimal Context Batching Optimizer]
C & E & G --> H[Recovered Deep Work Hours]Features
Attention Residue Quantification: Calculates minutes of mental capacity lost to unfinished task transitions.
Application Entropy Scoring: Measures digital multitasking thrashing across active desktop windows.
Intelligent Context Batching: Groups related tasks to eliminate up to 75% of daily context switches.
Related MCP server: Personal Agent Adaptive Deep Work & Focus Guardian
Quickstart
from client import PersonalAgentAttentionResidueContextSwitch
residue = PersonalAgentAttentionResidueContextSwitch()
res = residue.optimize_task_batching_schedule([{"title": "Email 1", "category": "INBOX"}, {"title": "Email 2", "category": "INBOX"}])
print("Recovered Focus Minutes:", res["estimated_recovered_focus_minutes"])FastMCP Integration
python -m genpark-personal-agent-attention-residue-context-switch-skill.mcp_serverLicense
MIT
This server cannot be deployed
Maintenance
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