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GitHub リポジトリ

Multi-Agent-Research-System

Multi-Agent-Research-System には ahmedibrahim085 から収集した 3 個の skills があり、リポジトリ単位の職業カバレッジとサイト内 skill 詳細ページを表示します。

収集済み skills
3
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0
更新
2025-11-19
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職業カバレッジ
4 件の職業カテゴリ · 100% 分類済み
リポジトリエクスプローラー

このリポジトリの skills

internet-deep-orchestrator
統計補助員

Orchestrate comprehensive 7-phase RBMAS research for multi-dimensional queries (4+ dimensions). Coordinates multiple specialist agents through SCOPE → PLAN → RETRIEVE → TRIANGULATE → DRAFT → CRITIQUE → PACKAGE methodology. Use for thorough internet research requiring multiple sources, iterative refinement, and quality gates. Triggers - research, investigate, analyze with 4+ distinct dimensions or comprehensive depth requirements.

2025-11-19
internet-light-orchestrator
ソフトウェア開発者

Orchestrate lightweight parallel internet research (2-4 dimensions). Spawns light-research-researcher workers for each subtopic dimension, coordinates findings, synthesizes final reports. Use for standard research queries with 2-3 distinct angles. Examples - cloud gaming optimization, quantum computing overview, WebRTC performance analysis. Triggers - research, investigate, analyze with multiple aspects.

2025-11-19
internet-research-orchestrator
市場調査アナリスト・マーケティングスペシャリストその他の社会科学者・関連従事者

Orchestrate comprehensive TODAS research for novel/emerging domains (1-7 subagents adaptive). Specializes in unprecedented topics, post-training data, and emerging technologies. Uses adaptive depth-based methodology: straightforward queries (1 agent), standard queries (2-3 agents), complex queries (5-7 agents). Handles depth-first (multiple perspectives), breadth-first (distinct sub-topics), and straightforward investigations. Triggers include "novel", "emerging", "2025", "2026", "unprecedented", "new technology", research on topics that didn't exist during training cutoff.

2025-11-19