Blum45

Good Stake -> Productive Agent
many believe that an AI agent is just a well-written prompt
beyond that, it is very important to select the proper agent pieces:
> LLM
> Tools
> Memory
> Triggers
> Feedback loop
not a single point - the agent is just an empty talker
1. LLM: the reasoning engine
this part defines objectives, course of action, and design of execution.
but LLM itself doesn't auto-access your systems, retain stable context, or act in the real world
that is why “just using GPT” is not the same as building an agent
2. Tools: the execution layer
it's hands for agent, this layer convert

how to сreate OpenClaw skill in 3 min
skills are a key part of a quality AI agent
u can't do without them and custom skills are often essential
safety first: always test locally before production use
1. creating a directory
example (entered in terminal):
| mkdir -p ~/.openclaw/workspace/skills/skill_name
this is where all necessary data for the skill will be stored
2. SKILL·md (instruction for LLM)
the key factor is correctly formulating the essence of desired LLM action
this file uses YAML frontmatter for metadata and Markdown for instructions
important:
> make it executable, specific, and co

just sold macbook to buy another dip
headphones and desk are next
got any smarter ways to survive this market?