⚠️Install with caution. This skill has very few installs. Always review the source and verify it on clawhub.ai before installing. Community-built skills run with agent permissions — only install ones you trust.

🎓 Education & Learning

Einstein Research — Edge Candidate Generatorv0.1.0

Name: Einstein Research — Edge Candidate Generator
Author: clawdiri-ai

einstein-research-edge-dv

clawdiri-ai

Generate and prioritize US equity long-side edge research tickets from EOD observations, then export pipeline-ready candidate specs for trade-strategy-pipeli...

latest

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Installs (all time)

Installs (current)

Downloads

615

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CreatedMar 22, 2026

UpdatedMay 1, 2026

Install & Quick Start

Install via ClawdBot CLI:

clawdbot install clawdiri-ai/einstein-research-edge-dv

Skill Package16 files

📋SKILL.mdmarkdown

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Quality Score

B63/100

Grade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.

Market Validation7/35

· 615 downloads (moderate demand)
· No tracked installs (may still have real users via manual install)

Documentation20/25

· SKILL.md present
· Detailed documentation (≥3000 chars)
· Contains usage examples or trigger description
· Detailed summary

Package Completeness11/15

· skillAssets present (15 files)

💡

Usage Guide

Generated Oct 5, 2026

Quantitative ResearchersRetail Traders with Programming SkillsProp TradersFintech Product Managersintermediate

💡 Application Scenarios

Systematic Equity Long-Only Alpha DiscoveryAsset Management

A quantitative researcher at a long-only equity fund uses the skill to turn a market anomaly observation (e.g., post-earnings drift) into a formal research ticket, then export it as a pipeline-ready candidate for backtesting. This ensures rigorous documentation and reproducibility before committing compute resources.

Retail Quant Strategy PrototypingRetail Trading

An individual investor with coding skills uses the skill to structure a trading hypothesis about momentum breakouts into a testable strategy.yaml and metadata.json. They can then run the backtest engine without needing to manually define interfaces, accelerating their learning loop.

Prop Trading Desk Idea FunnelProprietary Trading

A prop trading firm's research team uses the skill to collect and prioritize strategy ideas from traders, scoring them based on novelty, data availability, and computational cost. The export step standardizes handoff to the backtest engine, reducing engineering overhead.

Fintech Platform Strategy MarketplaceFintech

A fintech platform integrates the skill to allow users to submit strategy hypotheses and automatically generate backtestable candidates. The platform can then monetize by charging for backtest runs or premium data, and curate a library of validated strategies.

Academic Quantitative Finance ResearchEducation

A university professor uses the skill to convert theoretical anomalies from academic papers into reproducible research tickets. Students can then run backtests using the standardized pipeline, facilitating empirical validation and teaching best practices in systematic investing.

💼 Business Models

SaaS Subscription for Quant Research TeamsMonthly recurring subscription fees, plus overage charges for extra backtests or data sources.

Offer the skill as part of a cloud-based research management platform where teams can create, prioritize, and export research tickets. Subscription tiers based on number of users, data storage, and backtest compute credits.

Freemium with Premium Backtesting ServicesFreemium conversions to paid backtest runs, enterprise licenses, and data marketplace commissions.

Provide the ticket generation and export for free to attract users, then charge for running backtests on the platform's infrastructure or for advanced analytics like parameter optimization. Upsell to enterprise for collaboration features.

White-Label Integration for BrokeragesAnnual licensing fees based on AUM or user count, plus revenue sharing from trades executed by strategies developed using the tool.

License the skill to brokerages to embed within their trading platforms, enabling clients to turn ideas into backtestable strategies. Brokerages pay a licensing fee and may share revenue from increased trading activity.

💬 Integration Tip

Ensure the edge-generator CLI is installed and accessible, and that the output directory structure aligns with trade-strategy-pipeline expectations. Validate the strategy.yaml schema against edge-finder-candidate/v1 before running backtests.