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Crypto Cog

作者 CellCog · GitHub ↗ · v1.0.12 · MIT-0
darwinlinuxwindows ✓ 安全检测通过
2409
总下载
7
收藏
6
当前安装
13
版本数
在 OpenClaw 中安装
/install crypto-cog
功能描述
AI crypto research and analysis powered by CellCog. Token deep-dives, on-chain metrics, DeFi protocol breakdowns, wallet portfolio reviews, market sentiment,...
使用说明 (SKILL.md)

Crypto Cog - Deep Research for a 24/7 Market

The market never sleeps, and neither does your analysis. #1 on DeepResearch Bench (Apr 2026) applied to crypto.

Crypto moves fast. Narratives shift overnight. New protocols launch daily. You need research depth that keeps pace with a market that never closes. CellCog brings the same deep reasoning that tops financial research benchmarks — now applied to tokens, DeFi, on-chain data, and the entire Web3 landscape. From degen plays to institutional due diligence, one prompt covers it all.

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 Crypto Research You Can Do

Token Analysis

Deep dives into any token or project:

  • Fundamental Analysis: "Analyze Solana — technology, ecosystem growth, validator economics, and competitive positioning vs Ethereum L2s"
  • Tokenomics Review: "Break down Arbitrum's tokenomics — supply schedule, inflation, governance power, and value accrual mechanisms"
  • New Token Research: "Research this new AI token that just launched — team, backers, tokenomics, red flags, and honest assessment"
  • Comparative Analysis: "Compare L2 solutions: Arbitrum vs Optimism vs Base vs zkSync — TVL, transactions, developer activity, and token performance"

Example prompt:

"Create a comprehensive analysis of Ethereum's current state:

Cover:

  • Network metrics: TVL, daily transactions, gas trends, staking ratio
  • Post-merge economics: ETH supply dynamics, burn rate, is it deflationary?
  • L2 ecosystem impact on mainnet revenue
  • Competitor positioning vs Solana, Avalanche, Cosmos
  • Key upcoming catalysts and risks
  • Bull and bear thesis for the next 12 months

Deliver as an interactive HTML report with charts."

DeFi Protocol Research

Understand protocols before you ape in:

  • Protocol Deep Dives: "Analyze Aave V3 — how it works, risk parameters, yield mechanics, and governance"
  • Yield Analysis: "Compare yield opportunities across Lido, Rocket Pool, and Coinbase cbETH — risks, returns, and tradeoffs"
  • Risk Assessment: "Evaluate the smart contract risk of this new DEX — audit status, TVL history, team background"
  • Ecosystem Mapping: "Map the Cosmos ecosystem — key protocols, IBC activity, and where value is concentrating"

Example prompt:

"Research Uniswap V4:

  • What's new vs V3? Hook system explained
  • Impact on LP profitability
  • Fee switch status and UNI token value accrual
  • Volume and market share trends
  • Competition from aggregators and new DEXes
  • Developer adoption of the hooks framework

Clear, no-BS analysis. I want to understand if the upgrade actually matters."

On-Chain & Market Intelligence

Data-driven market understanding:

  • Whale Tracking: "What are the largest ETH wallets doing this month? Accumulating or distributing?"
  • Market Sentiment: "Analyze current crypto market sentiment — funding rates, Fear & Greed index, social activity, and exchange flows"
  • Narrative Research: "What are the emerging crypto narratives for this quarter? AI tokens, RWA, DePIN — which have substance?"
  • Exchange Analysis: "Compare DEX vs CEX volume trends over the last 6 months — is DeFi gaining share?"

Portfolio & Strategy

Manage your crypto positions:

  • Portfolio Review: "Analyze my portfolio: 50% ETH, 20% SOL, 15% LINK, 10% ARB, 5% PEPE — diversification, risk, and rebalancing suggestions"
  • Entry/Exit Strategy: "Help me think through an accumulation strategy for Bitcoin at current prices — DCA schedule, key levels, position sizing"
  • Tax Optimization: "Research crypto tax strategies for US residents — harvest losses, long-term vs short-term, staking income treatment"
  • Risk Management: "Design a risk framework for a $100K crypto portfolio — position sizing, stop losses, correlation analysis"

Whitepaper & Smart Contract Analysis

Due diligence on new projects:

  • Whitepaper Review: "Analyze this project's whitepaper — is the technology feasible? Are the claims realistic? Red flags?"
  • Smart Contract Evaluation: "Review the security profile of this protocol — audits, bug bounties, incident history, code quality indicators"
  • Team Research: "Research the founding team of this new L1 — backgrounds, previous projects, VC backers, credibility assessment"
  • Comparison Research: "This new protocol claims to be better than Aave. Analyze their claims vs reality."

Output Formats

Format Best For
Interactive HTML Dashboard Token dashboards with charts, metrics, drill-downs
PDF Report Shareable research reports and investment memos
XLSX Spreadsheet Portfolio trackers, tokenomics models, yield comparisons
Markdown Quick analysis for integration into your notes

Chat Mode for Crypto

Scenario Recommended Mode
Quick price checks, simple token lookups, basic metrics "agent"
Deep token analysis, DeFi research, ecosystem mapping, portfolio strategy "agent team"
Institutional due diligence, high-stakes portfolio decisions, regulatory analysis "agent team max"

Use "agent team" for most crypto research. The crypto space requires synthesizing information from many sources — protocol docs, on-chain data, market analysis, social sentiment. Agent team mode delivers the multi-source depth that serious crypto research demands.

Use "agent" for quick lookups — current prices, basic metrics, or simple factual questions.

Use "agent team max" for institutional-grade crypto work — fund-level due diligence, high-stakes portfolio rebalancing, regulatory compliance research, or any crypto analysis where significant capital is at risk. All settings maxed for the deepest reasoning. Requires ≥2,000 credits.


Example Prompts

Token deep dive:

"Create a full research report on Chainlink (LINK):

  • Oracle technology explained simply
  • CCIP and its implications for cross-chain DeFi
  • Staking economics — real yields, participation rate
  • Competition: Pyth, API3, Band — does Chainlink's moat hold?
  • Revenue model and path to sustainability
  • Token price performance vs fundamentals

Honest assessment. I want to know both the bull case and what could go wrong."

DeFi yield research:

"Compare the best yield opportunities for stablecoins right now:

  • Aave/Compound lending
  • Curve/Convex liquidity provision
  • Ethena sUSDe
  • Sky (MakerDAO) savings rate
  • RWA-backed yields

For each: current APY, risk level, lock-up requirements, smart contract risk, and minimum recommended allocation.

I have $50K in USDC. What's the optimal split for risk-adjusted yield?"

Market narrative analysis:

"Research the current state of AI tokens (TAO, RENDER, FET, NEAR, etc.):

  • What's the actual thesis for AI x Crypto?
  • Which projects have real usage vs pure narrative?
  • On-chain metrics: users, transactions, revenue
  • VC activity and funding in this sector
  • Is this the next DeFi Summer or the next metaverse hype?

Give me the honest picture. I want signal, not hype."

Portfolio assessment:

"Review my crypto portfolio and help me optimize:

Holdings: 2 BTC, 15 ETH, 5000 SOL, 10000 LINK, 50000 ARB Total value: ~$300K Risk tolerance: Moderate (I can handle 30% drawdowns but not 70%) Time horizon: 2+ years

Analyze: concentration risk, correlation, sector exposure, and suggest rebalancing. Should I add any positions for better diversification?"


Tips for Better Crypto Research

  1. Be specific about what you need: "Analyze SOL" is broad. "Analyze Solana's network performance and validator economics post-Firedancer" is focused.

  2. State your experience level: "I'm new to DeFi" vs "I understand impermanent loss" changes the depth of explanation.

  3. Mention your purpose: "For a $10K investment decision" vs "For a research article" shapes the output.

  4. Ask for honest assessments: CellCog doesn't shill. Explicitly asking "what could go wrong?" gets you balanced analysis.

  5. Timeframe matters: "Next month" vs "next 2 years" leads to very different analysis.

  6. Don't trust, verify: Use CellCog's research as a starting point. Always verify on-chain data with primary sources before making financial decisions.


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 appears to be what it says: a CellCog-powered crypto research helper. Before installing, confirm you trust cellcog.ai and protect your CELLCOG_API_KEY (treat it like any API secret). Do not paste private keys, seed phrases, or signing-capable credentials into prompts — portfolio reviews only need public addresses or exported CSVs. Note the SKILL.md lists the 'cellcog' dependency but gives no install instructions; ensure the runtime will install that package from a trusted source (PyPI or your vetted repo). If you need stronger guarantees, ask the publisher for the package source and a privacy/data-retention policy for CellCog.
功能分析
Type: OpenClaw Skill Name: crypto-cog Version: 1.0.12 The crypto-cog skill bundle is a documentation-heavy wrapper for the 'cellcog' research platform, providing metadata and usage instructions for AI agents to perform cryptocurrency analysis. It contains no executable code or scripts, relying entirely on the external 'cellcog' dependency and a required API key (CELLCOG_API_KEY). No evidence of malicious intent, data exfiltration, or harmful prompt injection was found in SKILL.md or _meta.json.
能力标签
cryptorequires-walletrequires-sensitive-credentials
能力评估
Purpose & Capability
The name/description promise crypto research via CellCog and the SKILL.md shows usage of the CellCog SDK. Requiring python3 and a CELLCOG_API_KEY is consistent with that purpose; no unrelated credentials, binaries, or config paths are requested.
Instruction Scope
SKILL.md provides examples of calling CellCog (create_chat) and requests report outputs (HTML, PDF, XLSX). The instructions do not ask the agent to read unrelated system files or other environment variables, but they reference file handling and report generation — so the agent may create or read files provided by the user. There is no directive to collect or exfiltrate system-wide secrets.
Install Mechanism
This is an instruction-only skill (no install spec), which is low-risk. The SKILL.md lists a dependency 'cellcog' but does not provide an install step (pip install, etc.). That is not inherently malicious but means the runtime will need the package available or will attempt to install it; verify how the environment obtains the package and from which source.
Credentials
Only CELLCOG_API_KEY is required. That matches the declared integration. No other tokens or secrets are requested. The skill's tasks (analysis, portfolio review) do not require private keys — only public addresses and API access — so the requested env var is proportional.
Persistence & Privilege
always is false and the skill is agent-invocable in the normal way. It does not request persistent system-wide configuration or access to other skills' credentials.
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 crypto-cog
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /crypto-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
- Added minimum requirements to SKILL.md: Python 3 must be available and the CELLCOG_API_KEY environment variable is required. - No other changes to functionality or usage.
v1.0.11
- Updated skill description for improved clarity and conciseness. - Refined agent usage examples in the "How to Use" section for better guidance, especially on agent compatibility. - No changes to research features, use cases, or output formats—only documentation and instructions improved.
v1.0.10
- Updated the description in SKILL.md for improved clarity and conciseness. - Enhanced the usage example for agent integration with code import guidance. - No changes to core functionality; documentation and onboarding clarity improved.
v1.0.9
**Changelog for version 1.0.9:** - SKILL.md has been rewritten for conciseness and clarity. - Shorter description and streamlined introduction. - Simplified usage instructions with updated code samples. - Pruned and re-organized sections for easier navigation. - No changes to functionality; documentation only.
v1.0.8
- Major SKILL.md rewrite focused on conciseness and practical crypto use cases - Modernized description: emphasizes real-time, deep crypto research covering tokens, DeFi, on-chain data, and portfolio strategy - Added detailed prompt examples for token analysis, DeFi, market intelligence, portfolio review, and smart contract research - Clarified and expanded output format options (HTML dashboard, PDF, XLSX, Markdown) - Updated chat mode recommendations for different crypto research scenarios (agent, agent team, agent team max) - Removed outdated text for improved clarity and user guidance
v1.0.7
- Updated description and documentation for clarity and brevity. - Added a summary of internal capabilities, including multi-source crypto research and report generation. - Streamlined usage instructions and removed lengthy example prompts. - Clarified supported research types and recommended chat modes. - Linked to related skills for expanded research and analysis.
v1.0.6
- Updated SDK usage instructions in the "Prerequisites" section, adding specific guidance for OpenClaw agents (fire-and-forget) with the notify_session_key parameter. - Clarified which `client.create_chat` method to use for OpenClaw versus other agents. - Cleaned up references to delivery modes and file handling, now directing users to the cellcog skill for full API reference. - No functionality or feature changes to the research capabilities or output formats. - Documentation improvement only; no impact on user workflows outside SDK setup.
v1.0.5
- Simplified the "Quick start" SDK usage example for easier onboarding. - Updated setup instructions to reference the main cellcog skill for advanced usage details. - Clarified that all advanced API features (delivery modes, messaging, timeouts, file handling) are documented in the cellcog skill documentation. - No changes to research capabilities or supported output formats.
v1.0.4
- Updated DeepResearch Bench leaderboard reference from Feb 2026 to Apr 2026 in the description and introduction. - No functional or feature changes; documentation text only.
v1.0.3
- Added operating system compatibility metadata (darwin, linux, windows) to SKILL.md. - Included homepage URL (https://cellcog.ai) in SKILL.md metadata.
v1.0.2
- Added a new recommended chat mode: "agent team max" for institutional-grade crypto analysis and high-stakes research. - Updated instructions to clarify scenarios for each chat mode, including use cases and credit requirements for advanced research. - No changes to underlying code or SDK integration. Documentation enhancement only.
v1.0.1
crypto-cog 1.0.1 - Added author and dependencies metadata for improved skill documentation. - Updated prerequisite instructions to reference the `cellcog` skill directly. - No changes to functionality or usage.
v1.0.0
crypto-cog v1.0.0 - Initial release of advanced crypto research skill based on DeepResearch Bench-topping methodology. - Supports deep dives into tokens, DeFi protocols, on-chain metrics, portfolio review, market sentiment, and whitepaper/smart contract analysis. - Offers multiple output formats: interactive HTML dashboards, PDF reports, XLSX models, and Markdown summaries. - Features "agent team" mode for multi-source, high-depth crypto research and "agent" mode for quick lookups. - Includes detailed example prompts for a wide range of crypto research tasks.
元数据
Slug crypto-cog
版本 1.0.12
许可证 MIT-0
累计安装 6
当前安装数 6
历史版本数 13
常见问题

Crypto Cog 是什么?

AI crypto research and analysis powered by CellCog. Token deep-dives, on-chain metrics, DeFi protocol breakdowns, wallet portfolio reviews, market sentiment,... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 2409 次。

如何安装 Crypto Cog?

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

Crypto Cog 是免费的吗?

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

Crypto Cog 支持哪些平台?

Crypto 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 Crypto Cog?

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

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