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Einstein Research Suite
by
RunByDaVinci
· GitHub ↗
· v1.0.0
· MIT-0
134
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Install in OpenClaw
/install einstein-research
Description
A complete quantitative market research toolkit for serious traders and investors. Includes 11 specialized skills covering backtesting, breadth analysis, bub...
README (SKILL.md)
Einstein Research Suite
Professional quantitative market research for active traders and investors.
Included Skills
- Backtest Expert — Methodology for systematic strategy validation
- Backtest Engine — Run backtests on historical data with full metrics
- Market Breadth Analyzer — 0-100 composite breadth health score
- Market Bubble Detector — Quantitative bubble risk assessment
- Edge Candidate Generator — Turn observations into research tickets
- Follow-Through Day Detector — O'Neil FTD market bottom signals
- Options Strategy Advisor — Black-Scholes pricing, Greeks, P&L simulation
- Portfolio Risk Analyzer — VaR, stress testing, correlation matrix
- Macro Regime Detector — Cross-asset regime transition detection
- Headline Scenario Analyzer — 18-month scenario projections from news
- Market Theme Detector — Trending themes with lifecycle scoring
Quick Start
Install all skills in this pack:
clawhub install einstein-research-backtest-dv
clawhub install einstein-research-backtest-engine-dv
clawhub install einstein-research-breadth-dv
clawhub install einstein-research-bubble-dv
clawhub install einstein-research-edge-dv
clawhub install einstein-research-ftd-dv
clawhub install einstein-research-options-dv
clawhub install einstein-research-portfolio-risk-dv
clawhub install einstein-research-regime-dv
clawhub install einstein-research-scenario-dv
clawhub install einstein-research-themes-dv
License
MIT
Usage Guidance
This pack appears to be a wrapper/pack that points to 11 separate skills rather than containing their code, and it lacks an install spec for the Python libraries the README mentions. Before installing: 1) Verify the origin — there's no homepage and the owner is an opaque ID; check the registry listing and owner reputation. 2) Inspect each included sub-skill's SKILL.md and code (or registry page) to confirm they don't request unrelated credentials or network endpoints. 3) Be prepared to create an isolated environment (VM or container, or Python venv) and review/approve any Python packages (yfinance, pandas, numpy, scipy) before installing. 4) Do not provide brokerage or cloud credentials to the pack itself until you've audited each sub-skill that requests them. If you want higher assurance, install and test the sub-skills first in a sandbox and review any network calls they make.
Capability Analysis
Type: OpenClaw Skill
Name: einstein-research
Version: 1.0.0
The Einstein Research Suite is a meta-package or 'skill pack' that serves as a manifest for 11 individual quantitative finance skills. The provided files (SKILL.md, README.md, and _meta.json) contain no executable code, obfuscation, or malicious instructions; they only provide documentation and standard installation commands for the sub-components via the OpenClaw ecosystem's package manager.
Capability Assessment
Purpose & Capability
The SKILL.md and README claim a coherent set of 11 quantitative research tools (backtesting, breadth, options, etc.). However, the repository does not actually contain those sub-skill code files; it only lists them as included and shows clawhub install commands. The README also lists Python dependencies (yfinance, pandas, numpy, scipy) but the pack has no install spec declaring or installing them. This is a provenance / completeness mismatch rather than an immediate functional contradiction.
Instruction Scope
Runtime instructions are limited to describing the pack and showing clawhub install commands for the listed sub-skills. There are no instructions to read arbitrary files, exfiltrate data, or access environment variables. The scope is constrained to installing/using other skills.
Install Mechanism
There is no install spec in the pack itself (instruction-only). That is low-risk, but the README dependency list (Python packages) is not enforced or declared in an install step, which may lead to surprising post-install requirements. Also, because the pack defers to clawhub to fetch sub-skills, the security of those sub-skills depends entirely on their registry entries.
Credentials
The pack declares no required environment variables, credentials, or config paths. That is proportionate to the high-level description. Still, the included sub-skills (which will be installed separately) may request credentials; you should inspect them before granting any secrets.
Persistence & Privilege
The pack is not marked always:true and does not request elevated/persistent privileges in its metadata. Autonomous invocation is allowed (default) but that is normal; nothing in this pack requests additional persistent system access.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install einstein-research - After installation, invoke the skill by name or use
/einstein-research - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release: 11 quantitative market research skills for traders and investors
Metadata
Frequently Asked Questions
What is Einstein Research Suite?
A complete quantitative market research toolkit for serious traders and investors. Includes 11 specialized skills covering backtesting, breadth analysis, bub... It is an AI Agent Skill for Claude Code / OpenClaw, with 134 downloads so far.
How do I install Einstein Research Suite?
Run "/install einstein-research" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Einstein Research Suite free?
Yes, Einstein Research Suite is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Einstein Research Suite support?
Einstein Research Suite is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Einstein Research Suite?
It is built and maintained by RunByDaVinci (@clawdiri-ai); the current version is v1.0.0.
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