Coordinate multiple autonomous agents to explore codebases and synthesize findings.
npx skills add https://github.com/heyAyushh/stacc --skill council-heyayushh
Please help me install this Agent Skill. Skill: council Source: https://github.com/heyAyushh/stacc/tree/main/configs/commands/skills/council Command: npx skills add https://github.com/heyAyushh/stacc --skill council-heyayushh
This skill enables coordinated, multi-agent exploration and task delegation to deeply analyze complex codebases, reducing manual research time and enabling diverse perspectives.
Invoke the council skill against your area of interest and spawn parallel agents to cover key aspects such as core implementation, integration points, tests, and documentation gaps. Then synthesize results into a unified plan or report.
High-intent search queries and answers about installing and using this skill.
Multi-agent codebase exploration coordinates multiple autonomous agents to investigate complex modules from different angles. Agents perform parallel investigation of core implementation, integration points, and tests, then a final synthesis step generates a unified analysis report.
Multi-agent synthesis for cross-cutting concerns aggregates findings from parallel agents exploring large repositories. It applies structured prompts for diverse perspectives, generating a cohesive analysis that identifies documentation gaps and architectural edge cases.
Yes, you can spawn parallel agents to analyze git history and identify documentation gaps. The default orchestration coordinates 10 agents with structured prompts to cover diverse aspects, followed by a final synthesis step to unify the findings.
Generating an execution plan from gathered codebase data uses plan mode to produce a high-level strategy from multi-agent findings. The aggregated research covers architecture, edge cases, and integration points, synthesized into a unified actionable plan.
Multi-agent parallel investigation works for analyzing module architecture by delegating research tasks across autonomous agents. This approach reduces manual research time and enables diverse perspectives on complex codebase areas.
Multi-agent task delegation for code analysis is not suited for simple, isolated code queries or small repositories lacking cross-cutting concerns. It requires orchestration overhead and is designed for complex modules requiring parallel investigation and structured synthesis.
Jamie-CuiDecompose research questions, delegate sub-tasks to an explore agent, and synthesize findings into a structured report.
bketelsenDecompose delivery plans into file-level execution sequences with test checkpoints.
samcdavidIdentify gaps and weaknesses in test suites using static analysis cues.
granietDelegate coding tasks to the Claude Code CLI for automation.
