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research-coordinator
Multi-agent research coordination — deduplicate findings, assign work, and synthesize results
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Multi-agent research coordination — deduplicate findings, assign work, and synthesize results
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Onboard a new client/company onto this platform's real Agentic OS: create the company record, scan its websites/repos, auto-provision specialist agents, activate its 24x7 agency runtime, and know exactly which "OS" building blocks (memory, integrations, dashboard) already exist versus which are roadmap gaps. ADAPTED FROM: a third-party giveaway skill ("agentic-os-installer" by Gennaro Santoro / Operations Heroes) that described a generic vault + Google-suite + skill-pack installer. That skill's product (Obsidian vault, Gmail/Calendar/ Drive wiring, "skill packs") does not exist in this repo and its promotional content (Skool community link) does not belong here. This is a clean-room rewrite that keeps the useful idea — "stand up a working agency OS for a client from a short checklist" — and maps every step to the real module that already implements it in this codebase, per CLAUDE.md architecture rules.
Agile sprint planning, velocity tracking, and burndown metrics for agent-managed projects
Initiative-level portfolio management with dependency tracking and milestone coordination
AI-assisted engineering impact analysis — productivity metrics and code quality insights
Cross-harness agent patterns — standardize agent execution across different coding assistants
Temporal context graph for agent memory — track entity relationships and state changes over time
| name | research-coordinator |
| description | Multi-agent research coordination — deduplicate findings, assign work, and synthesize results |
Inspired by: Build a Multi-Agent Research Assistant
Purpose: Decompose research questions into parallel sub-tasks, dispatch them to specialized agents, and synthesize results.
A coordinator builds a dependency DAG of research tasks (web search, doc reading, summarization, critique, synthesis) and runs them in topological order. Each role runs against a least-loaded specialist agent.
agents/research_coordinator.pyfrom agents.research_coordinator import (
ResearchOrchestrator, ResearchAgent, AgentRole,
)
def web_handler(task, ctx): return "found X, Y, Z"
orch = ResearchOrchestrator()
orch.register_agent(ResearchAgent("web1", AgentRole.WEB_SEARCHER, web_handler))
# ... register agents for all roles ...
orch.plan("How does feature X work?")
orch.run()
print(orch.synthesize())
web_search ──┐
├─→ summarize ─→ critique ─→ synthesize
doc_read ───┘ │
└────────────┘
WEB_SEARCHER — external knowledgeDOC_READER — internal docs / repoSUMMARIZER — distills findingsCRITIC — gap analysisSYNTHESIZER — final answer