Coordinate decorrelated multi-agent reasoning with parallel attention-focused agents and a synthesizer.
npx skills add https://github.com/ponylang/llm-skills --skill pony-ensemble
Please help me install this Agent Skill. Skill: pony-ensemble Source: https://github.com/ponylang/llm-skills/tree/main/pony-ensemble Command: npx skills add https://github.com/ponylang/llm-skills --skill pony-ensemble
Coordinate decorrelated multi-agent reasoning to produce higher-confidence outputs by having multiple agents explore the problem with different attention focuses, then a synthesizer merges their results.
Invoke the ensemble workflow when you need higher-confidence results by enabling the ensemble option and specifying attention focuses; the system will spawn parallel agents and synthesize the final answer.
High-intent search queries and answers about installing and using this skill.
Multi-agent orchestration improves coding and analysis accuracy by spawning parallel agents with diverse attention focuses, then using a synthesizer to merge their results and a reviewer loop to exclude low-quality outputs.
Ensemble reasoning coordinates decorrelated multi-agent reasoning to produce higher-confidence outputs. Apply it to complex problem solving in coding, documentation, and analysis tasks where diverse attention focuses and synthesis reduce blind spots.
Set up parallel agents by defining an agent prompt format that includes an attention focus, output format, and reviewer instructions. An orchestrator spawns agents per focus, and a central synthesizer merges their outputs to highlight consensus and gaps.
Yes, multi-agent synthesis is suitable for design reviews and technical analysis where rigor and coverage matter. Diverse viewpoints run on the same task, and a triage reviewer loop excludes off-topic results to improve coverage.
The best way to synthesize multiple agent outputs is using a central synthesizer that merges results from parallel agents, highlights consensus and gaps, and applies a triage reviewer loop to exclude off-topic or low-quality outputs.
Ensemble reasoning for coding sessions requires an orchestrator to spawn agents, a synthesizer to merge results, and a defined agent prompt format. It is limited to contexts where the overhead of parallel agent coordination is justified by complexity.
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