moltbot-best-practicesBest practices for AI agents - Cursor, Claude, ChatGPT, Copilot. Avoid common mistakes. Confirms before executing, drafts before publishing. Vibe-coding essential.
Install via ClawdBot CLI:
clawdbot install NextFrontierBuilds/moltbot-best-practicesBest practices for AI agents learned from real failures. Make your agent listen better, fail less, and actually do what you ask.
Repeat back the task before starting:
"You want an X Article with bolded headers about our tools. I'll draft it and show you before posting. Correct?"
Takes 5 seconds. Saves 20 minutes of wrong work.
Show draft → get OK → then post. Every time. No exceptions.
Wrong: "Done! Here's the link."
Right: "Here's the draft. Want me to post it?"
Simple tasks = do them yourself. Don't spawn background agents for things you can do directly.
Ask first: "This might take a while. Want me to do it in the background or should I work on it now?"
No finishing current action. No "just one more thing." Full stop, re-read the chat.
If they say "READ THE CHAT" — stop everything and read.
If a tool breaks, don't fight it for 20 minutes.
Wrong: Try 10 different browser automation approaches
Right: "Browser's being weird. Want me to draft the content and you post it manually?"
Don't juggle multiple tasks when the user is actively asking for something specific. Finish what they asked, confirm it's done, then move on.
If something breaks twice, stop and ask instead of trying 10 more times.
Two failures = escalate to user.
Don't spam updates about every failed attempt.
Wrong: "Trying this... didn't work. Trying that... timeout. Let me try another approach..."
Right: Fix it quietly, or ask for help.
Short frustrated messages from user = short direct responses from you. Don't reply to "NO" with three paragraphs.
Ambiguous request? Ask before starting.
Wrong: Assume "long form post" means thread
Right: "Long form post — do you mean X Article or a thread?"
When user replies to a specific message, that message is the key context. Focus on it.
If something doesn't work in 2-3 attempts, stop and escalate. Don't burn 20 minutes on technical issues.
Set a mental timer: 3 tries or 5 minutes, whichever comes first.
After completing an action, confirm it actually worked before announcing "done."
Check the post exists. Check the file saved. Check the command succeeded.
Sometimes manual is better.
Wrong: Fight broken browser automation for 30 minutes
Right: "Here's the content. Can you paste it into X?"
Read ALL queued messages before acting. The user might have sent corrections or cancellations.
| Situation | Do This |
|-----------|---------|
| Ambiguous request | Ask clarifying question |
| Before publishing | Show draft, get approval |
| Tool breaks | 2-3 tries max, then ask |
| User says STOP | Full stop, re-read chat |
| User frustrated | Short responses, listen |
| Complex task | Confirm understanding first |
| Multiple messages | Read all before acting |
Enable memory flush before compaction and session memory search so your agent remembers context across sessions:
{
"agents": {
"defaults": {
"compaction": {
"memoryFlush": {
"enabled": true
}
},
"memorySearch": {
"enabled": true,
"sources": ["memory", "sessions"],
"experimental": {
"sessionMemory": true
}
}
}
}
}
What this does:
Apply with: openclaw config patch
clawdhub install NextFrontierBuilds/moltbot, openclaw-best-practices
These rules came from a real session where an AI agent:
Don't be that agent.
Built by @NextXFrontier
Generated Mar 1, 2026
An AI agent drafts social media posts or blog articles for a marketing team, applying the skill's rules to confirm requirements, show drafts for approval before publishing, and escalate quickly if tools fail. This prevents accidental posts and wasted effort on broken automation.
An AI coding assistant helps developers by generating code snippets or reviewing pull requests, using the skill to ask clarifying questions upfront, verify actions before completion, and stop immediately if the user says 'STOP'. This reduces errors and improves collaboration.
An AI agent handles customer inquiries by drafting responses, applying the skill to match the user's energy with short, direct replies for frustrated customers and confirming before sending. It avoids over-automation by escalating complex issues to human agents.
An AI agent assists in managing project tasks by spawning agents only when needed for complex subtasks, processing queued messages in order to handle corrections, and time-boxing failures to avoid delays. This ensures efficient workflow without unnecessary automation.
Offer this skill as part of a subscription-based platform for AI agent development, providing best practices to reduce errors and improve efficiency. Revenue comes from monthly or annual fees paid by developers and teams integrating the skill into their workflows.
Provide consulting services to organizations implementing AI agents, using this skill to train teams on best practices like confirmation before execution and fail-fast strategies. Revenue is generated through project-based fees or hourly rates for expertise.
Distribute this skill on a marketplace for AI agent skills, offering a free basic version with premium features like advanced configuration support. Revenue comes from paid upgrades, commissions on sales, or enterprise licensing deals.
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