Coordinate 3-10 independent solver agents to debug via majority voting.
npx skills add https://github.com/michaelboeding/skills --skill debug-council
Please help me install this Agent Skill. Skill: debug-council Source: https://github.com/michaelboeding/skills/tree/main/skills/debug-council Command: npx skills add https://github.com/michaelboeding/skills --skill debug-council
This Skill enables independent solver agents to debug complex problems using self-consistency, reducing reliance on a single perspective.
Spawn 3-10 debug solver agents and feed them the exact user prompt. Collect their results and apply majority voting to determine the final answer.
High-intent search queries and answers about installing and using this skill.
Use independent solver agents to analyze the problem in isolation, then apply majority voting to select the most reliable solution. This self-consistency approach reduces single-perspective bias and works best for critical bugs or tricky algorithms where accuracy is essential.
Majority voting is ideal when debugging critical bugs, complex algorithms, or problems with a single correct solution where reliability matters more than speed. It requires 3-10 agents analyzing the same prompt independently to reach consensus.
Distributed reasoning spawns multiple independent agents to explore the problem space separately, then combines results through majority voting. Single-agent debugging relies on one perspective, making it less robust for critical issues where false negatives are costly.
Deploy 3-10 independent solver agents for debugging. Each agent receives the raw prompt without shared context, explores the problem independently, and contributes to the final majority-voted result.
This approach works best for problems with a single correct solution. For algorithms with multiple valid answers, majority voting may not reliably identify correctness without additional validation criteria.
Provide raw prompts describing the problem directly to each agent without preprocessing or shared context. Each agent analyzes the exact user prompt independently, then their results are aggregated through majority voting.
michaelboedingOrchestrate parallel solver agents to synthesize feature implementations into a plan.
MantisWareGenerate multiple reasoning paths and aggregate final answers by majority voting.
raykCoordinate subagents with parallel calls, token budgets, and voting.
ZpankzOrchestrates multi-agent reasoning to produce deterministic, verifiable results via MAD decomposition, voting, and red-flag filtering.
