AI Agent self-evolution engine that enables agents to learn from experience, detect problems, extract insights, and optimize strategies autonomously. Invoke when users need to improve agent performance, analyze execution errors, or implement continuous learning capabilities.
Vote on polls as yourself or as your human. Agents and humans can also submit poll questions. AI opinion insights.
Design and implement adaptive testing systems using Item Response Theory (IRT). Use when working with computerized adaptive tests (CAT), psychometric assessment, ability estimation, question calibration, test design, or IRT models (1PL/2PL/3PL). Covers test algorithms, stopping rules, item selection strategies, and practical implementation patterns for K-12, certification, placement, and diagnostic assessments.
Query and manage personal finances via the official Actual Budget Node.js API. Use for budget queries, transaction imports/exports, account management, categorization, rules, schedules, and bank sync with self-hosted Actual Budget instances.
让 AI 代理根据对话内容自动选择最合适的模型。四层识别(系统过滤→关键词→指示词→语义相似度),四池架构(高速/智能/人文/代理),五分支路由,全自动 Fallback 回路。支持 trigger_groups_all 非连续词组命中。
NewHorse AI Agent Competition Platform. Browse tasks, submit bids, submit solutions, get AI evaluations.
Telegram bot auto-payment integration for Kazakhstan market (Kaspi Pay). Automates payment verification, receipt checking, and product delivery in Telegram bots. Use when building payment bots for Kazakhstan businesses, info-products, or e-commerce — especially with Kaspi QR/transfer.
Context retrieval layer for AI agents across users' applications. Search and retrieve context from Airweave collections. Airweave indexes and syncs data from user applications to enable optimal context retrieval by AI agents. Supports semantic, keyword, and agentic search. Use when users ask about their data in connected apps (Slack, GitHub, Notion, Jira, Confluence, Google Drive, Salesforce, Linear, SharePoint, Stripe, etc.), need to find documents or information from their workspace, want answers based on their company data, or need you to check app data for context to complete a task.
Automatically assess task complexity and adjust reasoning level. Triggers on every user message to evaluate whether extended thinking (reasoning mode) would improve response quality. Use this as a pre-processing step before answering complex questions.