원클릭으로
retrieve-info
Analyze and synthesize information from previously collected documents to extract specific financial data points and insights.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
메뉴
Analyze and synthesize information from previously collected documents to extract specific financial data points and insights.
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
This skill outlines two methods to change the root password in Linux: one when you have the current password, and another when you need to reset it without the current password.
This skill outlines the workflow for generating a Know Your Customer (KYC) report, encompassing company website research, GLEIF/LEI lookup, and adverse media/news screening to build a foundational client profile.
This skill describes how to perform an LEI search on the GLEIF website to find a company's LEI number for KYC analysis.
This skill describes how to reset a forgotten Linux root password by booting into single-user mode through the Grub menu.
This skill summarizes the "Social Media Manager Confidential" podcast, which offers realistic, positive, and insightful resources for social media managers. It covers strategies, content tips, and transparent advice for building ethical and profitable businesses that support their lifestyle goals. The podcast also extends an invitation to an exclusive, free private community for social media managers.
Understand the basic structure of an Excel file, differentiating between a workbook and its constituent worksheets, and how to navigate between them. It is foundational for anyone new to Excel or needing a refresher on its basic organization.
| name | retrieve-info |
| description | Analyze and synthesize information from previously collected documents to extract specific financial data points and insights. |
Use this skill for:
def retrieve_info(query: str, documents: list[str] | None = None) -> dict:
"""Retrieve and synthesize information from collected documents.
Args:
query: What information to extract
documents: Optional list of document IDs to search within
Returns:
Dict with extracted info, sources, and confidence
"""
context = document_store.search(query, doc_ids=documents)
synthesis = llm.synthesize(
query=query,
context=context,
instruction='Extract precise financial data with sources',
)
return {
'answer': synthesis.text,
'sources': [s.id for s in synthesis.sources],
'confidence': synthesis.confidence,
}
confidence score: values below 0.5 should be flagged for review.documents IDs when you know which sources to search — improves speed and precision.parse-html or edgar-search have collected source material.