Total 50,614 skills, AI & Machine Learning has 8484 skills
Showing 12 of 8484 skills
根据CATLASS算子设计文档生成算子工程交付件
Calculate comprehensive ROI for AI implementation projects. Takes current costs, manual process time, team size, and hourly rates. Generates detailed roi-analysis.md with executive summary, cost-benefit tables, sensitivity analysis, break-even timeline, and comparison scenarios. Use when evaluating AI investments, building business cases, or justifying automation spend.
Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.
Connect to local LLM endpoints (Ollama, llama.cpp, vLLM) with automatic provider fallback. Use when: (1) you need to run LLM inference locally for privacy/cost, (2) you want to use models not available via cloud APIs, (3) you need offline capability, (4) you want automatic fallback to cloud providers when local fails.
Guide for creating, improving, benchmarking, and packaging Claude Agent Skills (SKILL.md files). Invoke when users want to create a skill from scratch, improve or test an existing skill, benchmark skill performance with variance analysis, or optimize a skill description for triggering accuracy. Also invoke when users say "turn this into a skill", "make a skill for X", "help me write a SKILL.md", "my skill isn't firing correctly", or want to convert a workflow/conversation into a reusable skill. Invoke proactively when a conversation has produced a repeatable workflow worth capturing. If the user mentions SKILL.md, skill files, skill descriptions, or skill triggering, this skill applies.
POLAR v2.4 — ETH Alpha Hunter (sniper recalibration). Single-asset ETH lifecycle scanner with conviction-scaled leverage, move-exhaustion scoring, and same-direction re-entry cooldown. v2.4 recalibration after -31.7% ROE on 381 trades: MIN_SCORE raised 8→10 (Cheetah v5.1 APEX pattern), leverage tiers shifted to 7x at 10-11 / 10x at 12+, cooldown raised 120→240 min, new MIN_SM_ACCEL_PCT=0.3 hard gate on 15m velocity. DSL exit managed by plugin runtime via runtime.yaml.
Conduct deep research on any topic — get comprehensive reports with citations, key findings, and actionable insights in minutes. Use when user wants to "deep research", "research this", "investigate", "analysis report", "深度研究", "调研", "リサーチ", "심층 연구".
Provides autonomous project pattern learning by analyzing the codebase to discover development conventions, architectural patterns, and coding standards, then generates project rule files in .claude/rules/. Use when user asks to "learn from project", "extract project rules", "analyze codebase conventions", "discover project patterns", or wants to auto-generate Claude Code rules for the current project.
Decomposes complex, multi-day tasks into optimized milestones using parallel reviewer agents (ultraplan). Spawns 5 independent reviewers that analyze the problem from different angles, then synthesizes their findings into a milestone dependency DAG. Triggers when the user says "plan milestones", "break this into milestones", "ultraplan", or when long-run harness needs milestone generation.
Designs production-grade RAG pipelines with chunking optimization, retrieval evaluation, and pipeline architecture. Use when building a RAG system, selecting a chunking strategy, choosing a vector database, optimizing retrieval quality, designing embedding pipelines, or evaluating RAG performance with RAGAS metrics.
Append project fragment knowledge that is "too short to warrant a separate file but needs to be known by AI every time" to fixed sections of AGENTS.md / CLAUDE.md — such as special compilation flags, services that must be started before running, path pitfalls, command aliases, and environment variable conventions. Triggers: When the user says "make a note", "add to AGENTS", "save to CLAUDE.md", "the project requires X to compile", "must do Y every time from now on", or just encountered a project-specific setting that can be explained in one sentence.
Harness engineering for AI coding agents — five subsystems, memory persistence, session continuity, verification workflows, scope control, lifecycle management.