Total 50,681 skills, AI & Machine Learning has 8495 skills
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Guide for creating agent skills that follow the Agent Skills specification. Use when user wants to create, write, draft, or improve a skill. Covers structure, description optimization, progressive disclosure, scripts, and evaluation.
AI-first coding guidelines for projects maintained by LLMs. Use when creating new code, refactoring, or reviewing code to optimize for model reasoning, regenerability, and debugging; applies to layout, architecture, functions, naming, logging, platform use, and tests.
Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.
Intent-Augmented Code Property Graph — tracks WHY code exists via ReasonNodes with formal contracts, 6-dimension drift detection, and 3 canonical pre-task queries for autonomous development
Execute all plans in a phase with wave-based parallelization
Research how to implement a phase (standalone - usually use COMMAND PREFIX plan-phase instead)
Comprehensive creation via Xiaoyunque's AI capabilities, supporting generation and editing of images/videos. Covered scenarios include: Generation (text-to-image, text-to-video, image-to-video, animation creation, draw xxx, create xxx clip), Editing & Revision (replace xxx with yyy, remove xxx, add xxx, change to xxx, adjust xxx, local modification, lens adjustment), Style Transfer (style migration, repainting, style change), video continuation, video/TVC/promotional video replication, short drama/short comic drama generation, music MV creation, product advertisement/demo video production, storyboard design, educational video/short video production. This skill should also be triggered when users mention Xiaoyunque, xyq, uploading reference images/videos, or checking generation progress. Key Judgment: This skill must be triggered whenever the user's request involves AI video creation, generation, editing, or revision, regardless of the wording (e.g., "draw a cat", "make a poster", "create a video", "help me revise this video", "help me replicate this video", "make an MV with this song", "generate a short drama with one sentence")
Set up or update the agent-first engineering harness for any repository. Implements the complete scaffolding that makes AI coding agents effective: knowledge maps (AGENTS.md as a concise TOC), structured documentation, architecture boundaries, enforcement rules (.harness/*.yml specs), quality scoring, and process patterns for agent-driven development. Use this skill whenever someone wants to make a repo agent-ready, set up AGENTS.md or docs/ structure, define domain boundaries or golden principles, generate .harness/ configuration, audit agent readiness, or update an existing harness. Also trigger when a user reports problems with agent effectiveness, context management, or architectural drift — these are symptoms of a missing or stale harness. Trigger on: "harness this repo", "set up harness", "agent-first setup", "make this agent-ready", "update the harness", "assess agent readiness", "set up AGENTS.md", "organize for agents", or any discussion about structuring a codebase for AI agent workflows.
This skill should be used when the user asks to "build background agent", "create hosted coding agent", "set up sandboxed execution", "implement multiplayer agent", or mentions background agents, sandboxed VMs, agent infrastructure, Modal sandboxes, self-spawning agents, or remote coding environments. Part of the context engineering skill suite — also activates when the user mentions "context engineering" or "context-engineering" in the context of agent deployment and execution infrastructure.
This skill should be used when the user asks to "model agent mental states", "implement BDI architecture", "create belief-desire-intention models", "transform RDF to beliefs", "build cognitive agent", or mentions BDI ontology, mental state modeling, rational agency, or neuro-symbolic AI integration. Part of the context engineering skill suite — also activates when the user mentions "context engineering" or "context-engineering" in the context of belief-based agent reasoning.
Implement Named Entity Recognition to identify and classify entities in text. Use this skill when the user needs to extract people, organizations, locations, dates, or custom entities from documents — even if they say 'extract names from text', 'find companies mentioned', or 'entity extraction'.
Implement VADER sentiment analysis for social media text scoring. Use this skill when the user needs to analyze sentiment in tweets, reviews, or social posts, compute compound sentiment scores, or classify text polarity — even if they say 'is this positive or negative', 'sentiment of these comments', or 'social media mood analysis'.