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Fin Cog

作者 CellCog · GitHub ↗ · v1.0.12 · MIT-0
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当前安装
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版本数
在 OpenClaw 中安装
/install fin-cog
功能描述
AI financial analysis and stock research powered by CellCog. Stock analysis, valuation models, portfolio optimization, earnings breakdowns, investment resear...
使用说明 (SKILL.md)

Fin Cog - Wall Street-Grade Analysis, Accessible Globally

Wall Street-grade analysis, accessible globally. Deep financial reasoning powered by #1 on DeepResearch Bench (Apr 2026) + SOTA financial models.

The best financial analysis has always lived behind Bloomberg terminals, institutional research desks, and $500/hour consultants. CellCog brings that same depth — stock analysis, valuation models, portfolio optimization, earnings breakdowns — to anyone with a prompt. From raw tickers to boardroom-ready deliverables in one request.

How to Use

For your first CellCog task in a session, read the cellcog skill for the full SDK reference — file handling, chat modes, timeouts, and more.

OpenClaw (fire-and-forget):

result = client.create_chat(
    prompt="[your task prompt]",
    notify_session_key="agent:main:main",
    task_label="my-task",
    chat_mode="agent",
)

All agents except OpenClaw (blocks until done):

from cellcog import CellCogClient
client = CellCogClient(agent_provider="openclaw|cursor|claude-code|codex|...")
result = client.create_chat(
    prompt="[your task prompt]",
    task_label="my-task",
    chat_mode="agent",
)
print(result["message"])

What Financial Work You Can Do

Stock & Equity Analysis

Deep dives into public companies:

  • Company Analysis: "Analyze NVIDIA — revenue trends, margins, competitive moat, and forward guidance"
  • Earnings Breakdowns: "Break down Apple's Q4 2025 earnings — beat/miss, segment performance, management commentary"
  • Valuation Models: "Build a DCF model for Microsoft with bear, base, and bull scenarios"
  • Peer Comparisons: "Compare semiconductor stocks — NVDA, AMD, INTC, TSM — on valuation, growth, and profitability metrics"
  • Technical Analysis: "Analyze Tesla's price action — key support/resistance levels, moving averages, and volume trends"

Example prompt:

"Create a comprehensive stock analysis for Palantir (PLTR):

Cover:

  • Business model and revenue breakdown (government vs commercial)
  • Last 4 quarters earnings performance
  • Key financial metrics (P/E, P/S, FCF margin, revenue growth)
  • Competitive positioning vs Snowflake, Databricks, C3.ai
  • Bull and bear thesis
  • Valuation assessment

Deliver as an interactive HTML report with charts."

Portfolio Analysis & Optimization

Manage and optimize investments:

  • Portfolio Review: "Analyze my portfolio: 40% AAPL, 20% MSFT, 15% GOOGL, 15% AMZN, 10% TSLA — diversification, risk, and recommendations"
  • Asset Allocation: "Design an optimal portfolio for a 35-year-old with $200K, moderate risk tolerance"
  • Risk Assessment: "Calculate the Sharpe ratio, beta, and maximum drawdown for this portfolio over the last 3 years"
  • Rebalancing: "My portfolio drifted from target — recommend rebalancing trades to minimize tax impact"

Financial Modeling

Build professional financial models:

  • DCF Models: "Build a discounted cash flow model for Shopify with sensitivity analysis on growth and discount rate"
  • Startup Financial Models: "Create a 3-year financial projection for a B2B SaaS with $50K MRR growing 15% monthly"
  • LBO Models: "Model a leveraged buyout scenario for a $100M revenue company at 8x EBITDA"
  • Scenario Analysis: "Create a 3-scenario model (recession, baseline, boom) for a retail REIT portfolio"

Financial Documents & Reports

Professional financial deliverables:

  • Investment Memos: "Write an investment memo recommending a position in CrowdStrike"
  • Quarterly Reports: "Create a quarterly financial report for my small business"
  • Financial Statements: "Generate pro forma financial statements for a startup fundraise"
  • Tax Planning: "Analyze tax optimization strategies for a freelancer earning $150K with $30K in capital gains"

Personal Finance

Everyday financial planning:

  • Retirement Planning: "How much do I need to save monthly to retire at 55 with $2M? I'm 30, saving $2K/month currently"
  • Mortgage Analysis: "Compare a 15-year vs 30-year mortgage on a $500K home with 20% down at current rates"
  • Debt Payoff: "Create a debt payoff plan: $15K student loans at 5%, $8K credit card at 22%, $25K car loan at 6%"
  • Budget Optimization: "Analyze my spending breakdown and recommend where to cut to save $1,000/month more"

Output Formats

CellCog delivers financial analysis in multiple formats:

Format Best For
Interactive HTML Dashboard Explorable charts, drill-down analysis, live data presentation
PDF Report Shareable, printable investment memos and reports
XLSX Spreadsheet Editable financial models, projections, calculations
Markdown Quick analysis for integration into your docs

Specify your preferred format in the prompt:

  • "Deliver as an interactive HTML report with charts"
  • "Create a PDF investment memo"
  • "Build this as an editable Excel model"

Chat Mode for Finance

Scenario Recommended Mode
Quick lookups, single stock metrics, basic calculations "agent"
Deep analysis, valuation models, multi-company comparisons, investment research "agent team"
High-stakes investment decisions, M&A due diligence, institutional-grade research "agent team max"

Use "agent team" for most financial analysis. Financial work demands deep reasoning, data cross-referencing, and multi-source synthesis. Agent team mode delivers the depth that serious financial analysis requires.

Use "agent" for quick financial lookups — current stock price, simple calculations, or basic metric checks.

Use "agent team max" for high-stakes financial work — investment decisions with significant capital at risk, M&A due diligence, regulatory filings, or boardroom-ready deliverables where the extra reasoning depth justifies the cost. Requires ≥2,000 credits.


Example Prompts

Comprehensive stock analysis:

"Create a full investment analysis for AMD:

  1. Business Overview — segments, revenue mix, competitive positioning
  2. Financial Performance — last 8 quarters revenue, margins, EPS trends
  3. Valuation — P/E, P/S, PEG vs peers (NVDA, INTC, QCOM)
  4. Growth Catalysts — AI/datacenter, gaming, embedded
  5. Risk Factors — competition, cyclicality, customer concentration
  6. Bull/Bear/Base price targets

Interactive HTML report with comparison charts."

Financial model:

"Build a startup financial model:

Business: B2B SaaS, project management tool Current: $30K MRR, 200 customers, $150 ARPU Growth: 12% MoM for 12 months, then 8% for next 12 Team: 8 people now, hiring 4 in next year Expenses: $180K/month burn rate

Create a 24-month projection showing:

  • Revenue forecast with cohort analysis
  • Expense breakdown and hiring plan
  • Cash flow and runway
  • Unit economics (CAC, LTV, payback period)
  • Break-even analysis

Deliver as Excel spreadsheet with charts."

Personal finance:

"I'm 28, earning $120K/year in San Francisco. I want to:

  1. Max out 401K contributions
  2. Build a 6-month emergency fund ($30K)
  3. Save for a house down payment ($100K in 5 years)
  4. Start investing in index funds

Create a detailed monthly financial plan that shows how to prioritize these goals with my take-home pay after taxes. Include a timeline and visual roadmap."

Earnings analysis:

"Break down Tesla's most recent quarterly earnings:

  • Revenue vs estimates (beat/miss by how much?)
  • Automotive margins — trend over last 4 quarters
  • Energy and services segment performance
  • Key quotes from management on guidance
  • What analysts are saying post-earnings
  • Bull and bear reactions

Deliver as a concise PDF report with charts."


Tips for Better Financial Analysis

  1. Be specific about metrics: "Revenue growth" is vague. "YoY revenue growth for the last 8 quarters with segment breakdown" is precise.

  2. Specify time horizons: "Analyze AAPL" is open-ended. "Analyze AAPL's performance and outlook for the next 12 months" is actionable.

  3. State your purpose: "For an investment decision", "For a board presentation", "For personal planning" — context shapes the analysis.

  4. Include constraints: Budget, risk tolerance, time horizon, tax situation — these matter for financial recommendations.

  5. Request scenarios: "Include bear, base, and bull cases" gives you a range, not just a point estimate.

  6. Ask for the deliverable you need: "Interactive dashboard", "PDF memo", "Excel model" — specify the format for the best result.


If CellCog is not installed

Run /cellcog-setup (or /cellcog:cellcog-setup depending on your tool) to install and authenticate. OpenClaw users: Run clawhub install cellcog instead. Manual setup: pip install -U cellcog and set CELLCOG_API_KEY. See the cellcog skill for SDK reference.

安全使用建议
This skill will send prompts and any data you include to CellCog's API using the provided CELLCOG_API_KEY. Only install if you trust cellcog.ai and are comfortable with that data flow. Limit the API key's permissions if possible, avoid sending sensitive PII or private keys in prompts, and monitor usage (rate limits, billing). If you want tighter control, consider running analyses on locally installed tools or reviewing the separate 'cellcog' SDK code (if available) before providing the API key.
功能分析
Type: OpenClaw Skill Name: fin-cog Version: 1.0.12 The fin-cog skill bundle is a documentation-heavy package designed for financial analysis and stock research using the 'cellcog' Python dependency. The SKILL.md file provides legitimate usage examples, prompt templates, and installation instructions for financial modeling and portfolio optimization, with no evidence of malicious intent, data exfiltration, or harmful prompt injection.
能力标签
cryptocan-make-purchasesrequires-sensitive-credentials
能力评估
Purpose & Capability
Name/description (financial analysis) aligns with what the SKILL.md instructs (using the CellCog SDK to create chats/tasks). Required bits — python3 and CELLCOG_API_KEY — are what you'd expect for a Python SDK that calls an external API.
Instruction Scope
Instructions show example code calling CellCog (client.create_chat) and reference file handling and chat modes in the separate 'cellcog' SDK. The SKILL.md does not request unrelated local files or system credentials, but it does send user prompts/data to an external service (CellCog). Be aware any prompt content or uploaded data may be transmitted to CellCog's servers.
Install Mechanism
No install spec or external downloads are present (instruction-only). This minimizes on-disk install risk; the skill expects the environment to already have python3 and the 'cellcog' dependency available.
Credentials
Only CELLCOG_API_KEY is required and is proportional to the described functionality. No unrelated secrets, system paths, or multiple credentials are requested.
Persistence & Privilege
always:false and no install-time persistence or modification of other agent configs are present. The skill can be invoked autonomously by the agent (platform default), which is expected for an API-backed capability.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install fin-cog
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /fin-cog 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.12
fin-cog 1.0.12 - Added requirements for Python 3 and the CELLCOG_API_KEY environment variable in metadata. - No other functional or documentation changes.
v1.0.11
- Updated the skill description to streamline deliverable formats and clarify offerings. - Improved the usage instructions for clarity, especially distinguishing between agent types. - No changes to core logic—documentation update only.
v1.0.10
- Updated the description to emphasize AI capabilities, deliverable types, and CellCog's DeepResearch Bench ranking. - Added an import statement for `CellCogClient` to the "How to Use" example for enhanced clarity. - Expanded the description of deliverables and features in the opening summary. - No changes to core features or functionality — documentation/content updates only.
v1.0.9
- Simplified and clarified the skill description for easier reading and a more direct explanation of key features. - Shortened and streamlined the "How to Use" section; emphasized prompt usage and essential SDK references. - Condensed instructions for various agent platforms, improving clarity for different workflows. - Removed repetitive and overly detailed explanations, focusing on concise examples and common use cases. - Kept all functional details and example prompts, preserving demonstration of analysis and deliverable options. - No changes to functionality—this is a significant documentation and usability improvement.
v1.0.8
- Major rewrite of the SKILL.md documentation for clarity, depth, and real-world examples. - Expanded usage scenarios and detailed prompt examples for stock analysis, portfolio optimization, financial modeling, reporting, and personal finance. - Clearly described available output formats (HTML dashboard, PDF, XLSX, Markdown) and recommendations for each. - Refined and expanded Chat Mode guidance for different types of financial analysis tasks. - Updated summary to showcase Wall Street-grade capabilities and #1 ranking on DeepResearch Bench (Apr 2026).
v1.0.7
- Simplified and clarified the skill description for easier understanding. - Trimmed and restructured SKILL.md, focusing on core use cases and capabilities. - Added a concise feature list under "What CellCog Has Internally" for quick reference. - Streamlined guidance on agent modes and related skills. - Removed extensive prompt examples and long explanations for brevity.
v1.0.6
fin-cog 1.0.6 changelog - Added example code for OpenClaw agents using the notify_session_key parameter for better support of fire-and-forget (long-running) financial tasks. - Clarified SDK usage, distinguishing OpenClaw agents from others with separate code examples. - Updated prerequisite instructions and code blocks for more accurate and user-friendly guidance. - Minor refinements to documentation flow for clarity and usability.
v1.0.5
**Changelog for fin-cog v1.0.5** - Updated SKILL.md to simplify and clarify SDK usage instructions. - Replaced the code example with a more concise "Quick start" snippet. - Added explicit guidance to reference the cellcog skill for advanced API options and deeper documentation. - Minor editorial cleanups and section reorganizations to improve readability for new users.
v1.0.4
- Updated DeepResearch Bench ranking from "Feb 2026" to "Apr 2026" in the description and introduction. - No functional or API changes; documentation wording only.
v1.0.3
- Improved and clarified the skill description for easier understanding. - Added platform metadata specifying support for Darwin, Linux, and Windows. - Included a homepage link for further information. - No changes to code or functionality; documentation updates only.
v1.0.2
fin-cog 1.0.2 - Added new "agent team max" chat mode for high-stakes financial work, such as M&A due diligence and institutional-grade research. - Updated "Chat Mode for Finance" table and section to include guidance for "agent team max" (requires ≥2,000 credits). - Clarified mode selection: use "agent team" for deep analysis, and "agent" for quick lookups; added advice for when to use "agent team max". - No changes to core capabilities, examples, or prerequisites.
v1.0.1
fin-cog 1.0.1 - Added author field ("CellCog") and dependencies ([cellcog]) to skill metadata. - Clarified prerequisite by renaming "CellCog mothership skill" to "`cellcog` skill". - No changes to core functionality or features.
v1.0.0
Initial release of fin-cog: Wall Street-grade financial analysis for everyone. - Provides advanced financial analysis: stock deep dives, valuation models, portfolio optimization, earnings breakdowns, financial statements, tax planning, and more. - Integrates state-of-the-art financial models and ranks #1 on DeepResearch Bench (Feb 2026). - Supports a wide range of deliverables: interactive HTML dashboards, PDF reports, Excel models, and Markdown. - Designed for use with the CellCog skill for setup and API calls. - Includes extensive documentation, examples, prompt patterns, and guidance for both deep and quick financial tasks.
元数据
Slug fin-cog
版本 1.0.12
许可证 MIT-0
累计安装 10
当前安装数 8
历史版本数 13
常见问题

Fin Cog 是什么?

AI financial analysis and stock research powered by CellCog. Stock analysis, valuation models, portfolio optimization, earnings breakdowns, investment resear... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 2682 次。

如何安装 Fin Cog?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install fin-cog」即可一键安装,无需额外配置。

Fin Cog 是免费的吗?

是的,Fin Cog 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Fin Cog 支持哪些平台?

Fin Cog 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(darwin, linux, windows)。

谁开发了 Fin Cog?

由 CellCog(@nitishgargiitd)开发并维护,当前版本 v1.0.12。

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