Automate Beads project triage and planning using .beads/beads.jsonl graph metrics.
npx skills add https://github.com/trungtnm/claude-code-utils --skill bv-trungtnm
Please help me install this Agent Skill. Skill: bv Source: https://github.com/trungtnm/claude-code-utils/tree/main/plugins/skills/bv Command: npx skills add https://github.com/trungtnm/claude-code-utils --skill bv-trungtnm
Beads teams face challenges in triage and planning due to large issue graphs; BV provides a graph-aware engine to surface priorities and drive deterministic execution plans.
Run BV in robot mode to triage issues and surface bottlenecks with graph metrics.
High-intent search queries and answers about installing and using this skill.
To prioritize issues in a Beads project using a dependency graph, you can apply graph metrics like PageRank and betweenness to surface critical bottlenecks and identify the most impactful work items. BV automates this analysis directly from your .beads/beads.jsonl data.
Graph metrics like PageRank and betweenness in issue triage are used to reveal blockers and critical paths within large dependency graphs. They mathematically identify high-impact issues and bottlenecks that delay overall project progress.
Yes, you can automate action plans for multiple agents working on Beads repositories by running the engine in robot mode. This generates deterministic, robot-guided execution plans to coordinate complex features across multiple agents.
Yes, this triage tool requires the .beads/beads.jsonl file format to analyze Beads issue graphs. It specifically parses these dependency graphs to calculate graph metrics and output deterministic recommendations for your project.
The best way to identify bottlenecks in a large Beads dependency graph is to calculate cycle detection and betweenness metrics. These graph algorithms highlight the specific issues causing circular dependencies and blocking downstream work.
If your automated Beads project recommendations are not deterministic, ensure you are using the built-in robot-mode commands correctly. The engine is designed to output consistent, deterministic recommendations based on the static .beads/beads.jsonl graph structure.
DicklesworthstoneAnalyzes beads graphs with nine graph metrics to surface bottlenecks and generate deterministic recommendations.
richardnpaulCoordinate multiple AI agents to plan and execute code tasks in parallel across isolated git worktrees.
oakossAnalyze .beads.jsonl dependency graphs with PageRank, betweenness, and cycles.
ZpankzAnalyze beads issue-tracker dependency graphs with 9 graph metrics.
