Total 54,043 skills, AI & Machine Learning has 8989 skills
Showing 12 of 8989 skills
Set up, supervise, and control a persistent multi-layer "explore → execute → escalate" agent loop on a project. Use whenever a user asks to keep an agent running on a task across sessions or days — finding bugs, polishing writing, distilling a style, watching feeds, scanning for gaps, or any task whose value grows with how many findings the agent produces. Also use when the user wants to inspect, pause, resume, stop, or send a new instruction to an already-running perpetuum task.
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
Enables Claude to manage Discord servers, send messages, moderate communities, and handle voice channel operations
Conduct enterprise-grade financial research with multi-source synthesis, regulatory compliance tracking, and verified market analysis. Use when user needs comprehensive financial analysis requiring 10+ sources, verified claims, market comparisons, or investment research. Triggers include "financial research", "market analysis", "investment analysis", "due diligence", "financial deep dive", "compare stocks/funds", or "analyze [company/sector]". Do NOT use for simple stock quotes, basic company lookups, or questions answerable with 1-2 searches.
Use this skill when you see `/omo`. Multi-agent orchestration for "code analysis / bug investigation / fix planning / implementation". Choose the minimal agent set and order based on task type + risk; recipes below show common patterns.
This skill should be used when users need help with content planning, calendar management, research organization, content ideation, or multi-platform planning. It activates when users ask about content planning, calendar management, research organization, content ideation, or multi-platform planning.
Use when retrieving the most relevant skills from a local or private skill library instead of relying on network-based skill discovery.
Detect and annotate hallucinations, unsupported claims, fabricated studies, and incorrect conclusions in text so that AI only cites verifiable, trustworthy content. Use this skill whenever the user asks you to fact-check, validate sources, check for hallucinations, or ensure that generated content is grounded in real evidence, even if they do not explicitly use the word "hallucination".
Help a CS or AI PhD student design hypothesis-driven experiments with baselines, variables, metrics, controls, logging, and stop conditions. Use this skill whenever the user is about to run experiments, compare models, plan an ablation, debug inconclusive results, prepare an experiment section, or wants to avoid changing too many things at once.
Generate objective reference check reports about the user from real AI collaboration data — session history, git logs, GitHub profile, and memory files. Like a colleague writing a professional reference, but grounded in actual shared work. Use whenever the user asks to evaluate them as a developer, wants a reference letter, work style analysis, introduced by my agents content, interview prep from collaboration history, or blog topics from past discussions. Triggers on: write a reference, analyze my work patterns, what do you think of me, 나에 대한 레퍼런스 써줘, 내 작업 스타일 분석해줘. Not for general code review, architecture docs, cover letters, or codebase-only analysis.
Router skill for LLMQuant Data primitive workflows. Use when the user needs SEC filings, 13F holders, macro snapshots, or source-grounded macro briefs.
Run a Google Alerts-style keyword coverage tracker. Uses news-search for recent keyword queries, lets the LLM dedupe and classify real features versus junk, stores decisions in SQLite, and alerts only on new real coverage.