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Agent Skill · 研究检索

financial-deep-research(金融深度研究)

financial-deep-research 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

financial-deep-research 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证为 MIT,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 12,839。

总安装 9,932周安装 418GitHub Stars 4下载 3,478核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:financial-deep-research(金融深度研究)
来源仓库:https://github.com/eng0ai/eng0-template-skills
仓库路径:skills/financial-deep-research
安装命令:
npx skills add https://github.com/eng0ai/eng0-template-skills --skill financial-deep-research
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/eng0ai/eng0-template-skills --skill financial-deep-research

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

全面的金融研究,包括多源综合、监管合规性跟踪和经过验证的市场分析。

执行 8 个阶段的研究流程(范围、计划、检索、三角测量、综合、批评、细化、打包),并跨 5-10 个并发源和代理进行并行数据收集 优先考虑一级监管来源(SEC EDGAR、美联储、公司备案),进行可信度评分和引文验证,以防止出现幻觉的财务索赔 支持四种执行模式(快速 2-5 分钟、标准 5-10 分钟、深度 10-20 分钟、超深 20-45 分钟以上),渐进式报告组装可实现无限的输出长度 生成三种输出格式(markdown、麦肯锡风格的 HTML、专业 PDF),并带有财务指标仪表板、数据表和有组织的文件夹结构,以便于访问 执行严格的反幻觉协议:每项财务声明都需要立即引用来源 [N]、明确的事实与分析区别以及 9 项质量检查的自动验证,包括来源计数、引用准确性和财务数据一致性

来源仓库内路径:skills/financial-deep-research

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/eng0ai/eng0-template-skills --skill financial-…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

financial-deep-research 怎么安装?

在终端执行 npx skills add https://github.com/eng0ai/eng0-template-skills --skill financial-deep-resea…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

financial-deep-research 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

financial-deep-research 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/eng0ai/eng0-template-skills --skill financial-deep-research;npx skills add eng0ai/eng0-template-skills --skill "financial-deep-research" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

financial-deep-research 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

financial-deep-research 用的人多吗?

公开数据是累计安装 9,932 次,近一周安装 418 次,下载 3,478 次,GitHub 4 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

financial-deep-research 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
financial-deep-research

金融深度研究 · 本页

9,9324执行命令MIT有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Financial Deep Research

Core System Instructions

Purpose: Deliver citation-backed, verified financial research reports through 8-phase pipeline (Scope > Plan > Retrieve > Triangulate > Synthesize > Critique > Refine > Package) with financial source credibility scoring, regulatory compliance tracking, and progressive context management.

Financial Focus: This skill specializes in:

  • Market analysis and investment research
  • Due diligence and competitive benchmarking
  • Regulatory compliance and risk assessment
  • Financial modeling support and valuation analysis
  • Earnings analysis and financial statement review
  • Sector/industry deep dives

Context Strategy: This skill uses 2025 context engineering best practices:

  • Static instructions cached (this section)
  • Progressive disclosure (load references only when needed)
  • Avoid "loss in the middle" (critical info at start/end, not buried)
  • Explicit section markers for context navigation

Decision Tree (Execute First)

Request Analysis
|-- Simple stock quote? -> STOP: Use WebSearch, not this skill
|-- Basic company lookup? -> STOP: Use WebSearch, not this skill
|-- Debugging code? -> STOP: Use standard tools, not this skill
+-- Complex financial analysis needed? -> CONTINUE

Mode Selection
|-- Quick market check? -> quick (3 phases, 2-5 min)
|-- Standard analysis? -> standard (6 phases, 5-10 min) [DEFAULT]
|-- Investment decision? -> deep (8 phases, 10-20 min)
|-- Due diligence/M&A? -> ultradeep (8+ phases, 20-45 min)

Execution Loop (per phase)
|-- Load phase instructions from [methodology](./reference/methodology.md#phase-N)
|-- Execute phase tasks
|-- Spawn parallel agents if applicable
+-- Update progress

Validation Gate
|-- Run `python scripts/validate_report.py --report [path]`
|-- Pass? -> Deliver
+-- Fail? -> Fix (max 2 attempts) -> Still fails? -> Escalate

Workflow (Clarify > Plan > Act > Verify > Report)

AUTONOMY PRINCIPLE: This skill operates independently. Infer assumptions from query context. Only stop for critical errors or incomprehensible queries.

1. Clarify (Rarely Needed - Prefer Autonomy)

DEFAULT: Proceed autonomously. Derive assumptions from query signals.

ONLY ask if CRITICALLY ambiguous:

  • Query is incomprehensible (e.g., "analyze the thing")
  • Contradictory requirements (e.g., "quick 50-source ultradeep analysis")
  • Critical compliance/regulatory scope unclear

When in doubt: PROCEED with standard mode. User will redirect if incorrect.

Default assumptions:

  • Company analysis -> Assume investor/analyst audience
  • Sector query -> Assume comprehensive market view needed
  • Valuation query -> Assume institutional-quality analysis
  • Regulatory query -> Assume US jurisdiction unless specified
  • Standard mode is default for most queries

2. Plan

Mode selection criteria:

  • Quick (2-5 min): Market snapshot, earnings preview, quick check
  • Standard (5-10 min): Most analysis, balanced depth/speed [DEFAULT]
  • Deep (10-20 min): Investment decisions, detailed due diligence
  • UltraDeep (20-45 min): M&A due diligence, comprehensive sector analysis

Announce plan and execute:

  • Briefly state: selected mode, estimated time, number of sources
  • Example: "Starting standard mode financial research (5-10 min, 15-30 sources)"
  • Proceed without waiting for approval

3. Act (Phase Execution)

All modes execute:

  • Phase 1: SCOPE - Define financial analysis boundaries (method)
  • Phase 3: RETRIEVE - Parallel financial data gathering (5-10 concurrent searches + agents) (method)
  • Phase 8: PACKAGE - Generate report using template

Standard/Deep/UltraDeep execute:

  • Phase 2: PLAN - Financial research strategy formulation
  • Phase 4: TRIANGULATE - Verify 3+ sources per financial claim
  • Phase 4.5: OUTLINE REFINEMENT - Adapt structure based on evidence (WebWeaver 2025) (method)
  • Phase 5: SYNTHESIZE - Generate investment insights

Deep/UltraDeep execute:

  • Phase 6: CRITIQUE - Risk analysis and bear case
  • Phase 7: REFINE - Address gaps, strengthen thesis

Critical: Avoid "Loss in the Middle"

  • Place key findings at START and END of sections, not buried
  • Use explicit headers and markers
  • Structure: Summary > Details > Conclusion (not Details sandwiched)

Progressive Context Loading:

  • Load methodology sections on-demand
  • Load template only for Phase 8
  • Do not inline everything - reference external files

Anti-Hallucination Protocol (CRITICAL for Financial Data):

  • Source grounding: Every financial claim MUST cite a specific source immediately [N]
  • Clear boundaries: Distinguish between FACTS (from filings/data) and ANALYSIS (your interpretation)
  • Explicit markers: Use "According to [1]..." or "[1] reports..." for source-grounded statements
  • No speculation without labeling: Mark inferences as "This suggests..." not "Data shows..."
  • Verify before citing: If unsure whether source actually says X, do NOT fabricate citation
  • When uncertain: Say "No sources found for X" rather than inventing references
  • Financial precision: Always include specific numbers, dates, and currency when available

Parallel Execution Requirements (CRITICAL for Speed):

Phase 3 RETRIEVE - Mandatory Parallel Financial Search:

  1. Decompose query into 5-10 independent search angles before ANY searches
  2. Launch ALL searches in single message with multiple tool calls (NOT sequential)
  3. Quality threshold monitoring for FFS pattern:

- Track source count and avg credibility score - Proceed when threshold reached (mode-specific, see methodology) - Continue background searches for additional depth

  1. Spawn 3-5 parallel agents using Task tool for deep-dive investigations

Financial Search Decomposition Strategy:

[Single message with 8+ parallel tool calls]
WebSearch #1: Company fundamentals + recent filings
WebSearch #2: Earnings/financial performance
WebSearch #3: Industry/sector analysis
WebSearch #4: Competitive landscape
WebSearch #5: Regulatory/compliance news
WebSearch #6: Analyst ratings/price targets
WebSearch #7: Risk factors/bear case
WebSearch #8: Recent news + catalysts
Task agent #1: SEC filing deep dive (10-K, 10-Q analysis)
Task agent #2: Financial statement analysis
Task agent #3: Industry comparison/benchmarking

4. Verify (Always Execute)

Step 1: Citation Verification (Catches Fabricated Sources)

python scripts/verify_citations.py --report [path]

Financial-Specific Checks:

  • SEC filing references (verify EDGAR links)
  • Financial data accuracy (cross-check key metrics)
  • Date accuracy (earnings dates, filing dates)
  • Flags suspicious entries (future financials, impossible metrics)

If suspicious citations found:

  • Review flagged entries manually
  • Remove or replace fabricated sources
  • Re-run until clean

Step 2: Structure & Quality Validation

python scripts/validate_report.py --report [path]

9 automated checks (financial-enhanced):

  1. Executive summary length (50-250 words)
  2. Required sections present (+ recommended: Risk Factors, Valuation)
  3. Citations formatted [1], [2], [3]
  4. Bibliography matches citations
  5. No placeholder text (TBD, TODO)
  6. Word count reasonable (500-10000)
  7. Minimum 10 sources
  8. No broken internal links
  9. Financial data consistency (dates, currencies, units)

If fails:

  • Attempt 1: Auto-fix formatting/links
  • Attempt 2: Manual review + correction
  • After 2 failures: STOP > Report issues > Ask user

5. Report

CRITICAL: Generate COMPREHENSIVE, DETAILED financial markdown reports

File Organization (CRITICAL - Clean Accessibility):

1. Create Organized Folder in /code:

  • ALWAYS create dedicated folder: /code/[TickerOrTopic]_Financial_Research_[YYYYMMDD]/
  • Extract clean topic name from research question
  • Examples:

- "AAPL investment analysis" -> /code/AAPL_Financial_Research_20251104/ - "compare cloud providers" -> /code/Cloud_Sector_Analysis_20251104/ - "fintech due diligence" -> /code/Fintech_Due_Diligence_20251104/

  • If folder exists, use it; if not, create it
  • This ensures clean organization and easy accessibility

2. Save All Formats to Same Folder:

Markdown (Primary Source):

  • Save to: [Documents folder]/financial_report_[YYYYMMDD]_[topic_slug].md
  • Also save copy to: /code/research_output/ (internal tracking)
  • Full detailed report with all findings

HTML (McKinsey Style - ALWAYS GENERATE):

  • Save to: [Documents folder]/financial_report_[YYYYMMDD]_[topic_slug].html
  • Use McKinsey template: mckinsey_template
  • Design principles: Sharp corners (NO border-radius), muted corporate colors (navy #003d5c, gray #f8f9fa), ultra-compact layout, info-first structure
  • Place critical financial metrics dashboard at top (extract 3-4 key metrics: market cap, P/E, revenue growth, etc.)
  • Use data tables for dense financial information
  • 14px base font, compact spacing, no decorative gradients or colors
  • OPEN in browser automatically after generation

PDF (Professional Print - ALWAYS GENERATE):

  • Save to: [Documents folder]/financial_report_[YYYYMMDD]_[topic_slug].pdf
  • Use generating-pdf skill (via Task tool with general-purpose agent)
  • Professional formatting with headers, page numbers
  • OPEN in default PDF viewer after generation

3. File Naming Convention: All files use same base name for easy matching:

  • financial_report_20251104_aapl_analysis.md
  • financial_report_20251104_aapl_analysis.html
  • financial_report_20251104_aapl_analysis.pdf

Length Requirements (UNLIMITED with Progressive Assembly):

  • Quick mode: 2,000+ words (baseline quality threshold)
  • Standard mode: 4,000+ words (comprehensive analysis)
  • Deep mode: 6,000+ words (thorough investigation)
  • UltraDeep mode: 10,000-50,000+ words (NO UPPER LIMIT)

How Unlimited Length Works: Progressive file assembly allows ANY report length by generating section-by-section. Each section is written to file immediately (avoiding output token limits). Complex analyses with many findings? Generate 20, 30, 50+ findings - no constraint!

Content Requirements:

  • Use template as exact structure
  • Generate each section to APPROPRIATE depth (determined by evidence, not word targets)
  • Include specific financial data, statistics, dates, numbers
  • Multiple paragraphs per finding with evidence
  • Each section gets focused generation attention
  • DO NOT write summaries - write FULL analysis

Writing Standards (Financial Precision):

  • Data-driven: Every claim backed by specific numbers from sources
  • Precision: Exact figures with currency, dates, and units
  • Economy: No fluff, eliminate unnecessary modifiers
  • Clarity: Financial terminology used correctly and consistently
  • Directness: State findings without embellishment
  • High signal-to-noise: Dense information, respect reader's time
  • Examples:

- Bad: "revenue increased significantly" -> Good: "revenue grew 23% YoY to $94.8B in FY2024 [1]" - Bad: "strong margins" -> Good: "gross margin of 43.2%, up 180bps YoY [2]" - Bad: "expensive valuation" -> Good: "trades at 28x forward P/E vs sector median 22x [3]"

Source Attribution Standards (Critical for Financial Research):

  • Immediate citation: Every financial claim followed by [N] citation in same sentence
  • Quote sources directly: Use "According to [1]..." or "[1] reports..." for factual statements
  • Distinguish fact from analysis:

- GOOD: "Q3 revenue was $24.9B, up 8% YoY [1]." - BAD: "Revenue grew strongly last quarter."

  • No vague attributions:

- NEVER: "Analysts believe...", "Market expects...", "Sources indicate..." - ALWAYS: "Goldman Sachs estimates..." [1], "Per SEC 10-K filing..." [2]

  • Label speculation explicitly:

- GOOD: "This suggests potential margin expansion..." (analysis, not fact) - BAD: "Margins will expand..." (presented as fact without citation)

Deliver to user:

  1. Executive summary with key investment thesis (inline in chat)
  2. Organized folder path (e.g., "All files saved to: /code/AAPL_Financial_Research_20251104/")
  3. Confirmation of all three formats generated:

- Markdown (source) - HTML (McKinsey-style, opened in browser) - PDF (professional print, opened in viewer)

  1. Source quality assessment summary (source count, regulatory vs news mix)
  2. Key financial metrics summary
  3. Risk factors summary
  4. Next steps (if relevant)

Generation Workflow: Progressive File Assembly (Unlimited Length)

[Same progressive assembly workflow as base skill - see deep-research SKILL.md]


Financial Data Sources (Priority Order)

Tier 1: Primary/Regulatory Sources (Highest Credibility)

  • SEC EDGAR: 10-K, 10-Q, 8-K, proxy statements, insider filings
  • Federal Reserve: FRED data, monetary policy, banking data
  • FDIC/OCC: Banking regulation, call reports
  • Treasury: Economic data, fiscal policy
  • Company IR: Investor relations, earnings calls, presentations
  • Exchange Filings: NYSE, NASDAQ company disclosures

Tier 2: Financial Data Providers (High Credibility)

  • Bloomberg: Real-time data, analysis, news
  • Reuters: News, data, analysis
  • S&P Global: Ratings, research, Capital IQ data
  • Moody's/Fitch: Credit ratings, research
  • FactSet: Financial data, analytics
  • Morningstar: Fund data, equity research
  • PitchBook: Private market data, VC/PE

Tier 3: Financial News & Research (Moderate-High Credibility)

  • Wall Street Journal: Business news, analysis
  • Financial Times: Global finance news
  • Barron's: Investment analysis
  • Institutional research: Goldman, Morgan Stanley, JPM research
  • Industry publications: American Banker, Insurance Journal

Tier 4: General Business Sources (Moderate Credibility)

  • CNBC, Yahoo Finance: Market news (verify with primary sources)
  • Seeking Alpha: Analysis (note: user-generated, verify claims)
  • Industry blogs: Supplement only, not primary citation

Source Verification Requirements:

  • Tier 1 sources: Can cite directly, highest trust
  • Tier 2 sources: Reliable, cross-check major claims
  • Tier 3 sources: Good for analysis, verify data with Tier 1-2
  • Tier 4 sources: Use sparingly, always verify with higher tiers

Output Contract

Format: Comprehensive financial markdown report following template EXACTLY

Required sections (all must be detailed):

  • Executive Summary with Investment Thesis (50-250 words)
  • Company/Topic Overview (background, business model)
  • Financial Analysis (revenue, margins, cash flow, balance sheet)
  • Valuation Analysis (multiples, DCF if applicable, peer comparison)
  • Competitive Position (market share, moat, competitive dynamics)
  • Risk Factors (business, financial, regulatory, market risks)
  • Investment Thesis / Recommendations
  • Bibliography (CRITICAL - see rules below)
  • Methodology Appendix

Financial-Specific Sections (include when relevant):

  • Earnings Analysis (quarterly trends, guidance vs actual)
  • Management Assessment (track record, insider activity)
  • Regulatory Environment (compliance, pending regulation)
  • ESG Considerations (if material to thesis)
  • Catalyst Timeline (upcoming events, catalysts)

Bibliography Requirements (ZERO TOLERANCE):

  • MUST include EVERY citation [N] used in report body
  • Format: [N] Source (Date). "Title". Publication/Filing. URL (Retrieved: Date)
  • Each entry on its own line, complete with all metadata
  • NO placeholders, NO ranges, NO truncation
  • Validation WILL FAIL if bibliography is incomplete

Strictly Prohibited:

  • Placeholder text (TBD, TODO, [citation needed])
  • Uncited financial claims
  • Forward-looking statements presented as facts
  • Broken links
  • Missing required sections
  • Short summaries instead of detailed analysis
  • Vague statements without specific data

Quality gates (enforced by validator):

  • Minimum 2,000 words (standard mode)
  • Average credibility score >70/100 (higher bar for financial)
  • 3+ sources per major financial claim
  • Clear facts vs. analysis distinction
  • All sections present and detailed
  • Key financial metrics included with sources

Error Handling & Stop Rules

Stop immediately if:

  • 2 validation failures on same error > Pause, report, ask user
  • <5 sources after exhaustive search > Report limitation, request direction
  • Critical financial data unavailable > Note gap, proceed with caveats
  • User interrupts/changes scope > Confirm new direction

Graceful degradation:

  • 5-10 sources > Note in limitations, proceed with extra verification
  • Missing recent filing > Note, use most recent available
  • Private company (limited data) > Acknowledge, use available sources
  • Time constraint reached > Package partial results, document gaps

Error format:

Issue: [Description]
Context: [What was attempted]
Tried: [Resolution attempts]
Options:
   1. [Option 1]
   2. [Option 2]
   3. [Option 3]

Quality Standards (Always Enforce)

Every financial report must:

  • 10+ sources (document if fewer)
  • 3+ sources per major financial claim
  • Executive summary <250 words with clear thesis
  • Full citations with URLs to filings/sources
  • Credibility assessment (source tier breakdown)
  • Risk factors section
  • Methodology documented
  • Key metrics with sources
  • No placeholders

Priority: Accuracy over speed. Financial data must be verified.


Inputs & Assumptions

Required:

  • Financial research question (string)

Optional:

  • Mode (quick/standard/deep/ultradeep)
  • Time constraints
  • Specific data requirements (valuation focus, risk focus, etc.)
  • Output format preferences
  • Jurisdiction (default: US)

Assumptions:

  • User requires verified, citation-backed financial information
  • Institutional-quality analysis expected
  • 10-50 sources available on topic
  • Time investment: 5-45 minutes
  • USD unless otherwise specified
  • US regulatory framework unless specified

When to Use / NOT Use

Use when:

  • Investment analysis (buy/sell/hold thesis)
  • Company due diligence
  • Sector/industry deep dives
  • M&A analysis
  • Competitive benchmarking
  • Earnings analysis
  • Regulatory impact assessment
  • Financial modeling research

Do NOT use:

  • Simple stock quotes (use WebSearch)
  • Basic company lookups (use WebSearch)
  • Real-time trading decisions (need live data)
  • Personal financial advice (not qualified)
  • Tax/legal advice (not qualified)

Scripts (Offline, Python stdlib only)

Location: ./scripts/

  • research_engine.py - Orchestration engine
  • validate_report.py - Quality validation (9 checks, financial-enhanced)
  • citation_manager.py - Citation tracking
  • source_evaluator.py - Financial source credibility scoring (0-100)
  • verify_citations.py - Citation verification with SEC filing checks

No external dependencies required.


Progressive References (Load On-Demand)

Do not inline these - reference only:

Context Management: Load files on-demand for current phase only. Do not preload all content.



Dynamic Execution Zone

User Query Processing: [User financial research question will be inserted here during execution]

Retrieved Information: [Search results and sources will be accumulated here]

Generated Analysis: [Findings, synthesis, and report content generated here]

Note: This section remains empty in the skill definition. Content populated during runtime only.

平台分布

Claude Code

30%
按下载量换算1,043

OpenCode

24.11%
按下载量换算839

Codex

17.04%
按下载量换算593

Cursor

12.2%
按下载量换算424

Gemini CLI

7.64%
按下载量换算266

Antigravity

3.26%
按下载量换算113

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/eng0ai/eng0-template-skills --skill financial-deep-research;npx skills add eng0ai/eng0-template-skills --skill "financial-deep-research" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。

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