Total 56,507 skills, AI & Machine Learning has 9399 skills
Showing 12 of 9399 skills
Multi-path parallel product analysis with cross-model test-time compute scaling. Spawns parallel agents (Claude Code agent teams + Codex CLI) to explore product from multiple perspectives, then synthesizes findings into actionable optimization plans. Can invoke competitors-analysis for competitive benchmarking. Use when "product audit", "self-review", "发布前审查", "产品分析", "analyze our product", "UX audit", or "信息架构审计".
Practical AI agent workflows and productivity techniques. Provides optimized patterns for daily development tasks such as commands, shortcuts, Git integration, MCP usage, and session management.
MCP server development including tool design, resource endpoints, prompt templates, and transport configuration
Save current session state to Apple Notes at session end. Triggers on handoff, bye, done, wrap up, or Chinese equivalents. Multi-agent architecture with private (per-agent) and shared (cross-agent) notes. Three-tier memory: Active, Archive, Long-term. Use whenever the user wants to end a session, save progress, or says anything indicating they are done for now.
Mine LITCOIN — a proof-of-comprehension and proof-of-research cryptocurrency on Base. Use when the user wants to mine crypto with AI, earn tokens through reading comprehension or solving optimization problems, stake LITCOIN, open vaults, mint LITCREDIT (compute-pegged stablecoin), manage mining guilds, run autonomous research experiments, deploy agents, or interact with the LITCOIN DeFi protocol. Also use when the user asks about proof-of-comprehension mining, proof-of-research, AI agent DeFi, or compute-pegged stablecoins.
Autonomous goal execution — give a goal, get a plan, confirm, execute, report. You steer, Claude drives.
Generate a persistent .nexus-map/ knowledge base that lets any AI session instantly understand a codebase's architecture, systems, dependencies, and change hotspots. Use when starting work on an unfamiliar repository, onboarding with AI-assisted context, preparing for a major refactoring initiative, or enabling reliable cold-start AI sessions across a team. Produces INDEX.md, systems.md, concept_model.json, git_forensics.md and more. Requires shell execution and Python 3.10+. For ad-hoc file queries or instant impact analysis during active development, use nexus-query instead.
Extract falsifiable ideas from input, deep-research each one, and return evidence for or against with strength ratings. Use when user says "find evidence for this", "is this true?", "back this up with data", or "fact-check these claims". Honest about when evidence contradicts the idea.
View and operate Lista Lending vaults/markets. Use when user asks about LENDING-ONLY positions or wants to deposit/withdraw/borrow/repay. For report-style overview, use lista instead.
Run a fast autonomous meeting with auto-selected personas, implement the decision, create a MR/PR, commit, push, and post a French summary — all without user intervention.
Fact-checks LLM responses by extracting verifiable claims, verifying each via web search, producing an audit report with verdicts, and optionally revising inaccurate responses. Use when the user asks to audit, fact-check, double-check, or verify a response.
Detect and neutralize prompt injection attacks in OpenClaw skill content, user inputs, and external data sources. Prevents instruction hijacking and context manipulation.