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LLM & AI

Large Language Models and AI agents.

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llm-ai

xhs-viral-post

AI 一键生成小红书爆款笔记（标题、正文、标签、封面提示、发帖策略）。真实种草风，防封号友好。

openclaw

data-ai

open

llm-ai

Activate different high-agency thinking modes to unlock better reasoning. Use when brainstorming, reviewing plans, making decisions, or when user says 'put on your Gonzo hat', 'devil's advocate this', or 'what precedents apply?'

openclaw

data-ai

open

llm-ai

swarm-coding-skill

Autonomous multi-agent code generation. Planner creates manifest, specialized roles execute tasks. Generates complete projects with tests, Docker, CI, and decision logs.

openclaw

data-ai

open

llm-ai

deep-research

Automated deep research that performs comprehensive multi-source investigation and produces detailed reports with citations. Use when user requests research, investigation, or in-depth analysis of any topic. Capabilities: generate structured research plans, and start sub agent to execute the plan.

openclaw

data-ai

open

llm-ai

install-founderclaw

Install FounderClaw — a complete multi-agent system for OpenClaw. Adds 6 agents, 29 skills, creates communication channels, configures models. Interactive setup. Requires user input for model selection and channel setup. Triggered by: "install founderclaw", "setup founderclaw", "get founderclaw".

openclaw

data-ai

open

llm-ai

founderclaw

FounderClaw — Multi-agent engineering team for OpenClaw. 29 skills, 6 agents (CEO + 5 departments), structured workspace, auto mode, vision sub-agent routing. Build startups with AI agents.

openclaw

data-ai

open

llm-ai

3.8K

add-pattern

Use this skill when you learn one or more design pattern(s) in the Langroid (multi) agent framework, and want to make a note for future reference for yourself. Use this either autonomously, or when asked by the user to record a new pattern.

langroid

data-ai

open

llm-ai

3.8K

patterns

Design patterns for the Langroid multi-agent LLM framework. Covers agent configuration, tools, task control, and integrations.

langroid

data-ai

open

llm-ai

3.6K

session-investigator

Investigate fast-agent session and history files to diagnose issues. Use when a session ended unexpectedly, when debugging tool loops, when correlating sub-agent traces with main sessions, or when analyzing conversation flow and timing. Covers session.json metadata, history JSON format, message structure, tool call/result correlation, and common failure patterns.