Total 55,535 skills, AI & Machine Learning has 9237 skills
Showing 12 of 9237 skills
Create a new runbook with guided assistance. A runbook is a structured markdown document that tells a coding agent how to accomplish a complex, multi-step task with evaluation loops and quality gates. Use this skill whenever the user wants to create, build, scaffold, or write a runbook — including 'create runbook', 'new runbook', 'build a runbook', 'make a runbook', 'runbook wizard', 'help me write a runbook', 'I need a runbook for...', 'automate this task with a runbook', or 'turn this into a runbook'. Also trigger when the user describes a multi-step agent task that would benefit from structured evaluation and iteration loops, even if they don't use the word 'runbook' — for example, 'I want to build an automated pipeline that evaluates its own output' or 'create a repeatable process with quality gates'.
This skill should be used when the user asks to "design multi-agent system", "implement supervisor pattern", "create swarm architecture", "coordinate multiple agents", or mentions multi-agent patterns, context isolation, agent handoffs, sub-agents, or parallel agent execution. Part of the context engineering skill suite — also activates when the user mentions "context engineering" or "context-engineering" in the context of orchestrating context across multiple agents.
Implement session-based recommendation from short-term user behavior sequences without long-term profiles. Use this skill when the user needs to recommend in anonymous sessions, predict next click from browsing sequence, or build recommendations for non-logged-in users — even if they say 'what should they click next', 'anonymous user recommendations', or 'browsing sequence prediction'.
Build employee turnover prediction models to identify flight risk and retention drivers. Use this skill when the user needs to predict which employees are likely to leave, identify retention risk factors, or prioritize HR interventions — even if they say 'attrition prediction', 'who is going to quit', or 'employee retention model'.
Generate Triton kernel code for Ascend NPU based on operator design documents. Used when users need to implement Triton operator kernels and convert requirement documents into executable code. Core capabilities: (1) Parse requirement documents to confirm computing logic (2) Design tiling partitioning strategy (3) Generate high-performance kernel code (4) Generate test code to verify correctness.
LangGraph-based agent framework for consistent tool calling with automatic tool loops. Use when you need reliable multi-step task execution with OpenAI-compatible providers (Z.AI/GLM-5, OpenRouter, Groq, DeepSeek, Ollama).
Automatic LLM provider failover with fallback chains, inspired by OpenClaw/ZeroClaw model configuration.
Wit.ai integration. Manage data, records, and automate workflows. Use when the user wants to interact with Wit.ai data.
Design and implement autonomous AI marketing agent systems using the PRAL, BDI, and OODA frameworks. Invoke when a client is ready to move beyond reactive GenAI prompting to proactive, autonomous marketing workflows, or when planning an AI-first marketing operations architecture.
Creates well-structured Agent Skills following best practices. Use when building new skills for Claude Code, designing skill directory structures, writing SKILL.md files, or improving existing skills with progressive disclosure patterns.
Run structured multi-role design reviews and architecture debates for technical decisions. Use when Codex needs to compare options, pressure-test tradeoffs, recommend an MVP path, or simulate a meeting with distinct evaluation roles such as moderator, skeptic, pragmatist, minimalist, maximalist, retrieval architect, Granary workflow lead, semantic purist, lightweight contrarian, context economist, or workflow conservative.
Get AI-powered match predictions for Premier League and Champions League including scores, next goal, and corners.