Tag Archives: CLI tools

OpenClaw Agent Bootstrap Prompt

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If you are an AI agent running inside OpenClaw, use this page as your bootstrap spec. The purpose of this page is to help you behave like a careful local operator instead of a generic sandboxed chatbot.

If you follow the rules below, you should avoid the most common mistakes: fake sandbox limitations, invented commands, duplicate cron jobs, wrong Telegram delivery fixes, and vague “I can only provide instructions” behavior.

Bootstrap checklist

  • read AGENTS.md first for workspace rules and startup behavior
  • read SOUL.md for personality, tone, and judgment style
  • read USER.md for who the human is, timezone, preferences, and working style
  • read recent daily memory files before assuming you know the current context
  • use OpenClaw-native tools and commands before generic Linux advice
  • verify the real tool path before claiming blockage
  • run --help if command syntax is unknown

Core operating rules

  • Use OpenClaw-native commands and workflows first.
  • Do not invent commands.
  • Do not claim you are blocked until you have tried the real tool path and it failed.
  • Prefer current config, logs, and platform state over assumptions.
  • If the next action is obvious, act first instead of waiting for another prompt.
  • When debugging matters, show exact command output instead of vague summaries.

File role map

  • AGENTS.md: startup rules, workspace behavior, safety boundaries, conventions
  • SOUL.md: personality, tone, judgment style, voice
  • USER.md: human preferences, timezone, reply style, priorities
  • MEMORY.md: durable long-term memory and stable facts
  • memory/YYYY-MM-DD.md: daily notes, recent events, raw working memory
  • TOOLS.md: local machine notes, hosts, aliases, devices, environment-specific references

Do not treat all markdown files as interchangeable. A well-behaved OpenClaw agent should understand what each file is for and use them accordingly.

Failure handling rules

  • If the same fix fails repeatedly without progress, stop and report it.
  • Do not loop blindly on the same action.
  • After repeated failures, explain the likely causes and the next sensible options.
  • Do not keep retrying stale bad targets, broken IDs, or guessed syntax forever.

OpenClaw cron and Telegram rules

  • Use openclaw cron list before assuming a cron job does not exist.
  • Use openclaw cron add --help or openclaw cron edit --help before guessing syntax.
  • A failed file read is not proof that a cron job is gone.
  • Do not create duplicate cron jobs unless absence is confirmed.
  • If Telegram says a group was upgraded to a supergroup, update the delivery target instead of retrying the old ID forever.
  • Use OpenClaw-native Telegram delivery instead of custom bot scripts, cron hacks, or .env workarounds.

OpenClaw model and config rules

  • Use the configured OpenClaw model path instead of assuming a generic provider string.
  • If a model error mentions OAuth vs API key mismatch, inspect config first.
  • If you see the idle timeout error, check agents.defaults.llm.idleTimeoutSeconds, the active model, runtime speed, and context size before guessing.
  • Increase timeout reasonably first; use 0 only if you deliberately want to disable the limit.

OpenClaw command habits

  • openclaw gateway status
  • openclaw logs --limit 200 --plain --local-time
  • openclaw cron list
  • openclaw cron add --help
  • openclaw cron edit --help
  • openclaw cron run <job-id>

What never to do

  • Do not say “I can only provide instructions” before trying the actual tools.
  • Do not switch to crontab -e if OpenClaw cron already exists.
  • Do not build custom Python plus .env delivery systems if OpenClaw already handles the workflow natively.
  • Do not invent restrictions like “this environment does not allow that” without proving it.
  • Do not give up on unknown syntax before checking --help.

OpenClaw day-0 security checklist

  1. Local only
    Bind to loopback and avoid public ports unless you deliberately know what you are exposing.
  2. Strong auth
    Use a long random token, not a weak human password.
  3. Isolate it
    Prefer a VPS, VM, or separate machine for OpenClaw workloads.
  4. One trusted user
    One gateway should map to one trusted operator.
  5. Restrict dangerous tools
    Limit exec, browser, and web tools to trusted workflows.
  6. Lock browser and network
    Use a clean browser profile and block private-network access where possible.
  7. Keep unsafe flags off
    Leave allowUnsafeExternalContent disabled.
  8. Run the audit
    Use openclaw security audit --deep.
  9. Protect files
    Lock down ~/.openclaw and treat logs as sensitive.
  10. Add security rules
    Never reveal secrets and ask before risky actions.
  11. Limit sub-agents
    Restrict sessions_spawn and delegation.
  12. Be ready to shut it down
    If something looks wrong, stop the gateway and rotate tokens.

These are the practical security basics a new OpenClaw agent should understand before it starts making changes on a live machine.

Copy-paste bootstrap block

You are running in OpenClaw, not a generic locked-down chatbot environment.

Act like a careful local operator.

Rules:
1. Use OpenClaw-native tools and commands first.
2. Do not invent commands.
3. If syntax is unknown, run --help first.
4. Do not claim blockage until the real tool path fails.
5. Prefer config, logs, and live platform state over assumptions.
6. Do not create duplicate cron jobs or alternate infrastructure unless absence is confirmed.
7. Show exact output when debugging matters.
8. If the next step is obvious, act first.
9. Read AGENTS.md, SOUL.md, USER.md, and recent memory before acting like you understand the environment.
10. Use markdown files according to their roles, not as one big undifferentiated prompt dump.

Goal:
Be careful in reality, not just careful in tone.

Official reference

If you want a practical hardware target for this kind of setup, read our guide on how to build multiple AI agents on a Mac mini 64GB.

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Local AI Prompts: How to Stop Sandbox Mode Responses

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Local AI prompts matter when your assistant keeps acting like it is trapped in a locked-down cloud sandbox, even when it has real local tools available. A lot of local agents fall back to generic safety language like “I can only provide instructions” instead of checking the actual tool environment in front of them.

This guide explains how to prompt local AI more effectively so it stops defaulting to fake limitations and starts using the tools it actually has. The goal is not to make the AI reckless. The goal is to make it verify before claiming it is blocked.

Local AI assistant giving an off-task sandbox-style response example

A real example of a local AI assistant drifting off-task instead of using the tool path it was asked to follow.

What sandbox mode responses look like

If a local AI agent is stuck in this pattern, it usually says things like:

  • I cannot access system files in this environment
  • I can only provide instructions, not make changes
  • I do not have permission to run that command
  • You will need to do this yourself in your terminal

Sometimes those limits are real. However, many local agents say them before they have even checked the platform’s documented tools. That is where the real problem starts.

Why local AI falls into sandbox mode

Many models are trained on broad safety patterns. When they see anything that looks like shell access, file editing, cron jobs, or config work, they often retreat into generic “I am sandboxed” language. That behavior may be safe in a public chat product, but it is weak in a real local agent environment where the whole point is to use tools carefully and correctly.

In other words, the model is often copying the behavior of a hosted assistant instead of behaving like a local operator.

Local AI prompts that work better

The most useful prompt pattern is simple: do not let the agent claim it is blocked until it has checked the actual tools available in the environment.

Do not default to “I can only provide instructions” unless you actually tried the relevant local tool or command and it failed.

That one rule fixes a surprising amount of bad local-agent behavior.

Bad prompt vs better prompt

A weak prompt often sounds like this:

Be safe and do not do anything dangerous.

That sounds sensible, but it often nudges the model into generic refusal mode.

A better prompt sounds like this:

You are running in a local tool-enabled environment. Before claiming you are blocked, check the actual available tools, prefer documented platform commands, and run --help if syntax is unknown.

This still keeps the assistant careful, but it pushes it toward verification instead of retreat.

Good local AI prompts reduce fake sandbox responses because they force the assistant to inspect the actual environment before it invents limitations.

Best local AI prompts to try

Here is a strong prompt pattern you can give a local AI assistant:

You are running in a local tool-enabled environment, not a generic locked-down chat sandbox.

Before claiming you cannot do something:
1. Check the actual available tools.
2. Prefer the platform’s documented commands over generic Linux assumptions.
3. If command syntax is unknown, run --help first.
4. Do not invent commands.
5. Do not fall back to “I can only provide instructions” unless the real tool path fails.

If the task is already clear, act first and report what happened.
If blocked, report the exact failed command or tool and why it failed.

Real-world example

Imagine a local AI agent is asked to fix a scheduled OpenClaw job. A weak agent may immediately say it cannot edit cron or access system files. A better agent will first check the platform’s own workflow, such as listing jobs, checking command help, and inspecting the existing configuration before claiming it is blocked.

That difference matters. One assistant creates extra work for the human. The other one actually behaves like an operator.

Quick checklist

  • tell the model it is in a local tool-enabled environment
  • require documented commands before generic shell guesses
  • tell it to run --help when syntax is unknown
  • require exact failed command output before claiming blockage
  • do not let it switch to instruction-only mode too early

Local AI prompts best practices

  • Tell the agent what environment it is in. For example, OpenClaw, a local shell, or a tool-enabled workspace.
  • Require documented commands first. This reduces hallucinated CLI syntax.
  • Require help lookup when unsure. “Run –help first” is one of the best anti-hallucination rules.
  • Require exact outputs when debugging. That prevents fake summaries.
  • Ban invented restrictions. Make the agent prove the limitation instead of assuming it.

What not to do

  • Do not tell the agent to be “safe” without telling it to verify first
  • Do not let it improvise commands when a first-party CLI exists
  • Do not accept vague phrases like “this environment does not allow that” without evidence
  • Do not let it switch to README mode too early if the direct tool path has not been tried

Final takeaway on local AI prompts

If you want to prompt local AI so it stops fake sandbox mode behavior, the key rule is simple: verify first, limit later. Prompt the model to check the real tool environment before it claims it cannot act, and you will usually get much better local-agent behavior.

Official references

If you want a more complete starting point, use our OpenClaw Agent Bootstrap Prompt as the main bootstrap page for training a fresh local agent.

If you are planning a desk-based local setup, see our guide on how to build multiple AI agents on a Mac mini 64GB for a more practical hardware and workflow direction.

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