Skill UI

Guidelines for automating Mailsoftly tasks through Composio’s Mailsoftly toolkit via Rube MCP, covering discovery, connection checks, execution steps, and schema-aware tool usage for reliable workflows.

This guide details advanced architectural patterns for building complex AI systems using multiple Language Model instances (agents). It addresses the single-agent context bottleneck by distributing work, enabling parallelism, and facilitating specialization across different subtasks. Key patterns include Supervisor/Orchestrator, Peer-to-Peer/Swarm, and Hierarchical designs, providing guidelines for context isolation and effective coordination protocols.

A comprehensive workflow designed for clinical professionals to search, retrieve, and synthesize medical evidence. It allows users to query clinical literature with specialized filters (e.g., specialty, evidence level), check potential drug interactions against patient profiles, and look up the latest specialty guidelines from major health organizations (like ACC/AHA, NICE). The output provides structured citations, confidence scores, and evidence grading to support informed clinical decision-making.

This skill provides advanced guidelines for designing robust and effective tools specifically for autonomous AI agents. It shifts the focus from traditional developer APIs to contracts optimized for Language Models (LLMs). Key topics covered include treating tool descriptions as prompt engineering, implementing namespacing, applying the Consolidation Principle, and utilizing Architectural Reduction patterns (like file system access) to maximize agent reliability and workflow complexity.

This tool automates the end-to-end workflow for GDPR Data Subject Access Requests (DSAR). It handles identity verification, discovers PII across structured and unstructured data sources using NER and regex, maps data to Article 15 requirements, and generates auditable compliance reports, ensuring adherence to ICO/EDPB guidelines and scaling compliance efforts.

This guide outlines the structure and content guidelines for an AGENTS.md file. This file serves as a comprehensive 'README for agents,' providing detailed, technical context and operational instructions (such as setup, development workflow, testing procedures, and code style) that enable advanced AI coding agents to interact with and understand a codebase effectively. It greatly complements traditional READMEs by focusing purely on machine-readable technical requirements.

Provides security guidelines and best practices for LLM-based trading agents, covering risk management, data protection, and safe deployment.

This tool allows users to fine-tune the evaluation criteria for AI-generated content. Users can configure minimum scoring thresholds, adjust the relative weights of dimensions (such as brand voice, hallucination risk, and claim verification), and set composite auto-reject rules. It ensures the quality bar matches specific brand guidelines, industry needs, or content formats, making quality trade-offs explicit and adjustable.

This skill facilitates the persistent storage of critical brand knowledge, including campaign learnings, brand guidelines, and competitive intelligence, into a specialized memory layer (such as a vector database). It automatically indexes content, deduplicates entries via hashing, and applies rich metadata tagging, ensuring that valuable institutional knowledge is preserved and retrievable through natural language search across different sessions.

Provides comprehensive guidelines for designing, auditing, and documenting empirical evidence for high-level AI research submissions (e.g., AAAI). It covers essential methodologies including strong baselines, single-factor ablations, robustness testing (shift, seed, prompt), statistical significance, and ensuring perfect alignment with reproducibility checklists and ethical reporting. Use this guide before submission to ensure empirical evidence strongly supports all claimed AI contributions.

Provides comprehensive guidelines for designing, auditing, and reporting high-rigor multi-agent experiments, especially in game theory and self-play systems. It emphasizes testing the *interaction* claim—such as convergence to equilibrium, truthful behavior, or emergent cooperation—against held-out opponents, populations, and strategic deviations, moving beyond simple single-agent leaderboard scores.

Provides comprehensive guidelines for authors preparing supplementary materials for conference submissions (AAMAS). It covers structuring full proofs, extended game definitions, extra experiments, and anonymized code assets, ensuring the main paper remains comprehensible while maximizing the impact of supporting evidence.