Agent Skills: denario
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
ID: obinopaul/agents-backend/denario
21
Install this agent skill to your local
Skill Files
The files that make up denario.
Loading file tree…
More agent skills
- analyzing-nft-rarity'Calculate NFT rarity scores and rank tokens by trait uniqueness.
- paypal-integrationIntegrate PayPal payment processing with support for express checkout, subscriptions, and refund management. Use when implementing PayPal payments, processing online transactions, or building e-commerce checkout flows.
- frontend-designAnti-slop frontend design for web UI — landing pages, portfolios, marketing/product sites, and redesigns. Reads the brief, infers the right direction, and ships interfaces that don't look templated. Real design systems when applicable, audit-first on redesigns, strict pre-flight check. NOT for mobile apps.
- project-developmentThis skill should be used for project-level decisions about LLM-powered systems: whether an LLM is the right primitive for the task at hand, the shape of a multi-stage batch or agent pipeline, token and cost estimation, choosing between single-agent and multi-agent at the project level, structured output design for downstream parsing, and structuring agent-assisted iteration. Use this when the unit of work is a whole project or a multi-stage pipeline. Route individual tool design to tool-design and individual skill-loading or context-budget tactics to context-optimization.
- math-progress-monitorMetacognitive check-ins during problem solving - detects when to pivot or persist
- python-anti-patternsUse this skill when reviewing Python code for common anti-patterns to avoid. Use as a checklist when reviewing code, before finalizing implementations, or when debugging issues that might stem from known bad practices.