Aggregate per-skill runtime metrics into ecosystem health reports with mutex and synergy detection.
npx skills add https://github.com/isLinXu/under-one --skill bagua-zhen
Please help me install this Agent Skill. Skill: bagua-zhen Source: https://github.com/isLinXu/under-one/tree/main/underone/skills/bagua-zhen Command: npx skills add https://github.com/isLinXu/under-one --skill bagua-zhen
Bagua-Zhen eliminates blind spots in multi-skill agent operation by turning scattered runtime metrics into an ecosystem health report, while detecting conflicting (mutex) and mutually beneficial (synergy) skill combinations.
runtime_data/*_metrics.jsonl from monitored skills and computes per-skill and ecosystem-level stability indicators.Use Case: You run an agent-ops pipeline with 10 skills and need to continuously answer: which skills are degrading, which pairs conflict right now, and which pairings should be encouraged or avoided. This skill produces ecosystem_report_v10.json plus an HTML panel to support operational decisions.
Run the Bagua-Zhen coordinator to generate ecosystem_report_v10.json from your current runtime_data/*_metrics.jsonl files.
High-intent search queries and answers about installing and using this skill.
Ecosystem health monitoring aggregates per-skill runtime metrics from `runtime_data/*_metrics.jsonl` files to compute success, quality, and error consistency statistics for ecosystem stability indicators. This generates an `ecosystem_report_v10.json` file and a dashboard HTML artifact for governance review.
Mutex relationship detection between concurrently active skills applies static relationship matrices and optionally overrides them with dynamically inferred relationships from runtime telemetry. The coordinator evaluates concurrent skill activity to identify conflicting combinations requiring operational arbitration.
Synergy detection in multi-skill ecosystem monitoring identifies mutually beneficial skill pairings by analyzing concurrently active skills using runtime JSONL telemetry. It applies static and optionally dynamic relationship matrices to recommend pairings that should be encouraged for improved ecosystem stability.
Runtime metrics can generate dashboard reporting for skill governance by processing `runtime_data/*_metrics.jsonl` telemetry files. The system computes ecosystem ratings and produces a dashboard HTML artifact alongside an `ecosystem_report_v10.json` file for quick operational decision support.
You need `runtime_data/*_metrics.jsonl` files containing per-skill runtime metrics to start ecosystem monitoring. These JSONL telemetry files serve as the required input for computing success, quality, and error consistency statistics across the monitored multi-skill set.
Arbitrating conflicting skill combinations in agent-ops pipelines aggregates runtime metrics into an ecosystem health report that detects mutex pairs using static and dynamic relationship matrices. This produces coordinator outputs for governance decisions and conflict resolution.
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