Log task metrics into project memory files after completion.
npx skills add https://github.com/PandaWithAPlan/mas --skill task-metrics
Please help me install this Agent Skill. Skill: task-metrics Source: https://github.com/PandaWithAPlan/mas/tree/main/development-team/global-config/skills/task-metrics Command: npx skills add https://github.com/PandaWithAPlan/mas --skill task-metrics
This Skill automates the recording of task metrics, ensuring accurate tracking and analysis of project progress and performance.
Trigger the task-metrics skill after accepting or escalating a task to log its metrics.
High-intent search queries and answers about installing and using this skill.
Automated task metrics logging records complexity, cycles, and agent usage data after task completion. This skill coordinates data aggregation and logs statistics directly into project memory files for performance analysis and process optimization.
You can automatically record task metrics including complexity, cycles, and agent usage. The skill aggregates these statistics to identify execution bottlenecks and updates the project's memory file with the latest performance data.
Data aggregation for performance analysis collects post-completion task metrics and coordinates logging within project memory files. This mechanism identifies bottlenecks and optimizes task execution by aggregating statistics from recorded complexity and cycle data.
Yes, you need access to task context and project memory files. The skill requires these dependencies to coordinate data aggregation and successfully log performance statistics for ongoing project management and analysis.
Tracking project bottlenecks is best achieved by automatically aggregating task metrics post-completion. This approach logs complexity and cycle data into project memory files, allowing you to identify execution bottlenecks and optimize processes.
Task metrics logging works without manual data entry by automating the recording process. Once triggered after task acceptance or escalation, it automatically coordinates data aggregation and updates the project memory file with relevant performance statistics.
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