droyd_skill

This skill enables autonomous crypto research and trading via natural language, helping you search content, filter projects, manage positions, and execute

Python

1.1k

GitHub Stars

2

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill openclaw/skills --skill droyd

_meta.json622 B
SKILL.md5.8 KB

Overview

This skill provides an AI-driven crypto trading and research assistant that accepts natural-language instructions to search content, filter projects, manage watchlists, and execute trades. It supports autonomous and managed trading with stop-loss, take-profit, and quant-based strategies, and works across Solana for live trading plus Ethereum, Base, and Arbitrum for research and filtering. The skill exposes agent chat, semantic and recent search, project discovery, and programmatic filters for market criteria.

How this skill works

You interact by sending natural-language commands or structured API calls to the DROYD agent. The agent performs semantic search across posts, news, tweets and on-chain metadata; filters projects by market-cap, momentum, or technical indicators; and optionally opens, manages, or closes positions on Solana using configurable risk rules. Responses include IDs and parameters you can use to inspect positions, run further queries, or execute trades programmatically.

When to use it

When you need AI-assisted crypto research across news, social, and on-chain signals
To discover or filter token projects by market cap, momentum, or custom quant criteria
When you want to run autonomous or semi-autonomous trades with stop-loss and take-profit rules
To maintain and query watchlists or positions with natural language
When you want semantic answers to complex crypto questions (risks, trends, tokenomics)

Best practices

Provide clear, concise goals (e.g., risk %, target timeframe) when initiating trades or managed strategies
Use project IDs for deterministic trading and management operations
Prefer semantic mode for deep analysis and recent mode for time-sensitive news
Start with small position sizes when enabling autonomous agents and validate behavior in dry-run/testing
Monitor rate limits and API session windows to avoid 429 responses

Example use cases

Ask the agent: “Find trending micro-cap Solana aggregator tokens with strong trader growth and show top 5”
Run a semantic search: “What are the regulatory risks for liquid staking across Ethereum vs Solana?”
Open a managed trade on Solana with 10% stop-loss and 25% take-profit via a single command
Filter projects on Ethereum by market cap under $50M and high mentions in last 24h
Use agent chat to build a research brief and then convert the top idea into a watchlist item and trade order

FAQ

Live trading is supported on Solana. Research, project discovery and filtering also support Ethereum, Base, and Arbitrum.

How do I control risk for autonomous trades?

Specify stop-loss and take-profit percentages or use quant-based triggers when creating managed trades; start with conservative sizes and monitor positions frequently.