The Foundation: Clear Communication
Unlike cloud AI that only responds, clawbot executes. The difference is profound: vague instructions that generate interesting conversation with ChatGPT become destructive commands when given system access. Precision matters.
Principles of Effective Instructions
Define exactly what should be affected. AI won't ask for clarification—it will make assumptions based on context.
"Archive all emails from newsletters@*.com in my inbox from the last 7 days."
"Clean up my inbox." (Which emails? What action? How old?)
For ongoing tasks, define when the action should execute and what conditions must be true.
"Every weekday at 9 AM, if there are unread emails labeled 'urgent', send me a Telegram summary."
"Let me know about urgent emails." (When? How? What defines urgent?)
Tell the AI how to know the task succeeded. This prevents infinite loops and allows meaningful error reporting.
"Run tests until they all pass or 3 attempts fail, then report results to #dev-channel."
"Keep running tests until they work." (How many attempts? What if they never pass?)
For operations that delete, modify, or publish data, build in approval steps.
"Draft a response to this customer complaint and send it to me for approval before sending."
"Respond to customer complaints automatically." (What if AI misunderstands?)
Six Task Delegation Patterns
Every automation falls into one of six patterns. Understanding these helps you design effective workflows and set appropriate expectations for what AI can handle autonomously.
AI watches for changes and notifies you.
The AI continuously observes a data source (logs, APIs, files, dashboards) and alerts when specific conditions occur. No changes are made—purely observational.
"Watch the Prometheus /api/v1/alerts endpoint every 60 seconds. If any alert enters 'firing' state with severity=critical, send details to #ops-emergency Slack channel."
"Check competitor.com/pricing daily at 2 AM. If prices change by more than 10%, screenshot the page and email me the comparison."
Best Practice
Set check intervals based on urgency: critical systems every 30-60s, business metrics hourly, trend monitoring daily.
AI sends time-based or event-triggered notifications.
Unlike simple calendar alerts, AI reminders can include context, preparation steps, or pre-fetched information to make the reminder actionable.
"30 minutes before any calendar event tagged 'client-meeting', send me via WhatsApp: (1) attendee names, (2) summary of last conversation from Notion, (3) open action items from Jira."
"7 days before any Jira ticket deadline where I'm the assignee, remind me via Telegram. If still incomplete 1 day before, escalate to urgent."
Best Practice
Include actionable context with reminders. "Meeting in 30 min" is useless; "Client call with Acme Corp in 30 min - they're asking about API limits" is valuable.
AI performs actions on your behalf.
The AI actively changes system state: files, databases, APIs, services. This is where self-hosted infrastructure becomes essential—cloud AI can never have this level of access.
"When a new tag matching v*.*.* is pushed to the main branch, run CI tests. If all pass, deploy to staging. After 10 minutes with no errors in Sentry, promote to production."
"Every Sunday at 3 AM, archive Slack messages older than 90 days in #random channel. Export to JSON in ~/archives/slack/ before deleting."
Best Practice
Always test execution workflows in non-production environments first. Use --dry-run flags when available. Build in rollback mechanisms.
AI processes data and extracts insights.
The AI reads large volumes of data—logs, metrics, documents, conversations—and summarizes, identifies patterns, or answers questions. No system changes, but generates valuable intelligence.
"Analyze the last 1000 lines of /var/log/nginx/error.log. Identify the 5 most common error patterns, estimate impact (requests affected), and suggest fixes."
"Read transcripts from all Zoom meetings tagged 'product-review' from the past month. What are the top 5 feature requests by frequency? Who mentioned each one?"
Best Practice
Pair analysis with action. Don't just generate reports—configure AI to escalate findings that need immediate attention.
AI connects multiple services and keeps them synchronized.
The AI acts as middleware, moving data between systems that don't natively integrate. This replaces Zapier-style automation but with natural language logic instead of drag-and-drop workflows.
"When a new ticket is created in Zendesk, check if customer exists in HubSpot. If yes, add ticket link to their timeline. If no, create contact record with ticket metadata."
"When I close a Jira ticket, calculate time from 'In Progress' to 'Done', log to Clockify under the project matching the Jira project key."
Best Practice
Design integrations to be idempotent (safe to run multiple times) and handle failures gracefully. Log all sync operations for audit trails.
AI chains multiple actions based on conditional logic.
This combines all previous patterns into sophisticated workflows with branching logic, retry mechanisms, and decision trees. The AI orchestrates complex sequences autonomously.
"When Prometheus fires a 'HighMemoryUsage' alert: (1) Check if this service restarted in the last hour. If yes, ignore. (2) If no, capture heap dump. (3) Analyze with gdb. (4) If memory leak detected, restart service and file Jira bug. (5) Post incident report to #ops Slack."
"When a new markdown file appears in ~/blog/drafts/: (1) Check spelling and grammar. (2) Generate SEO metadata. (3) Optimize images in the post. (4) Move to ~/blog/ready/. (5) Create PR to website repo. (6) Post preview link to #content Slack."
Best Practice
Break complex automation into stages with checkpoints. Log state at each step so failures can be debugged and workflows can resume mid-process.
Multi-Channel Strategy
clawbot supports 15+ communication channels. The key to effective use isn't connecting them all—it's choosing the right channel for each task type based on context, urgency, and workflow integration.
WhatsApp: Personal, High-Priority
- Critical alerts (production down, security incidents)
- Time-sensitive reminders (meeting prep, travel check-in)
- Personal automation (smart home, family tech support)
- Voice commands via voice messages
Why: Always with you, native mobile notifications, voice input.
Slack/Discord: Team Collaboration
- Deployment notifications visible to the team
- CI/CD status updates in project channels
- Shared automation (bot commands anyone can trigger)
- Incident response coordination
Why: Transparency, team context, threaded discussions.
Telegram: Reliable, Fast, Flexible
- High-frequency updates (monitoring dashboards)
- Daily digests and reports
- File sharing (screenshots, logs, exports)
- Bot API for custom integrations
Why: No rate limits, inline buttons, powerful bot API.
Email: Formal, Archival, External
- Weekly reports for stakeholders
- Formal notifications (legal, compliance)
- Communication with external parties
- Long-form content with formatting
Why: Universal, formal record, rich formatting.
Pro Tip: Channel Routing Rules
Configure clawbot to automatically route different message types to appropriate channels: errors to Slack #ops, summaries to Telegram, critical alerts to WhatsApp. Define routing logic in your IDENTITY.md or channel-specific configuration.
Skills Ecosystem: Extending Capabilities
clawbot's 565+ community skills (ClawdHub) follow the AgentSkills standard. Skills provide pre-built integrations and domain-specific capabilities you can install, customize, and combine.
Discovering Skills
Skills are distributed via ClawdHub (GitHub-based marketplace). Browse by category or search by keyword:
clawbot skills search kubernetes
"Show me skills for monitoring PostgreSQL databases."
Popular categories: DevOps (kubectl, docker, terraform), Cloud (AWS, Azure, GCP), Productivity (calendar, email, note-taking), Development (GitHub, GitLab, CI/CD), Smart Home (Home Assistant, IoT), Data (databases, analytics, ETL).
Installing Skills
Skills install as Node.js packages with declarative capability definitions:
clawbot skills install kubectl
clawbot skills install https://github.com/user/custom-skill
Security Note
Skills run with your clawbot's permissions. Review skill source code before installing, especially for skills that execute system commands or access credentials. Prefer skills with high GitHub stars and recent updates.
Customizing Skills
Most skills expose configuration via environment variables or config files. Common customizations:
Store in ~/.clawbot/secrets.env with encryption enabled.
GITHUB_TOKEN=ghp_xxxxx
SLACK_BOT_TOKEN=xoxb-xxxxx
Override defaults in ~/.clawbot/skills/{skill-name}/config.json
{"default_namespace": "production", "timeout": 30}
Restrict which actions the skill can perform via tool allowlists.
Allow "kubectl get" and "kubectl describe", deny "kubectl delete"
10 Expert-Level Usage Patterns
Advanced techniques from clawbot power users that transform it from "helpful assistant" to "indispensable infrastructure."
Store your preferences, work context, and domain knowledge in ~/.clawbot/IDENTITY.md. The AI reads this before every interaction, personalizing responses without repeated explanations.
• Your role and tech stack
• Project structure and naming conventions
• Preferred communication style
• Time zone and working hours
• Key contacts and their roles
• Decision-making principles
For operations with consequences (deployments, data changes, external communications), configure approval gates. AI drafts the action, you approve via chat.
tools.exec.approval: "ask" for commands matching "rm -rf", "kubectl delete", "git push --force"
After automation runs, explicitly tell the AI what worked and what didn't. It remembers in session memory and adjusts future behavior.
"That email draft was too formal—use a friendlier tone next time."
"The log analysis missed errors in stderr—check both stdout and stderr."
For recurring automation, use clawbot Gateway's cron scheduling instead of system cron. AI-generated cron jobs understand natural language and handle failures intelligently.
"Every weekday at 9 AM except holidays" → Gateway translates to cron expression and handles holiday calendar.
Combine multiple skills into sequences. The AI coordinates data flow between skills without you writing integration code.
"Use 'github' skill to fetch latest release notes, then 'slack' skill to post summary to #announcements, then 'twitter' skill to draft a tweet."
Enable comprehensive logging to track every AI action. Essential for debugging, security audits, and understanding what happened during incidents.
~/.clawbot/logs/gateway.log (all messages)
~/.clawbot/logs/exec.log (system commands)
~/.clawbot/logs/skills.log (skill invocations)
Before deploying automation to production systems, test in isolated environments. Use Docker containers, staging servers, or --dry-run modes.
1. Test command manually
2. Run AI automation in dry-run mode
3. Deploy to staging with real data
4. Monitor for 24-48 hours
5. Promote to production
Restrict certain skills to specific channels. Example: allow destructive operations only via WhatsApp (where you're authenticated), never via public Discord.
WhatsApp: all skills enabled
Slack #ops: DevOps skills only
Discord: read-only skills only
Telegram: monitoring and reporting
For complex tasks requiring back-and-forth, start a dedicated session. The AI maintains context across messages and remembers intermediate results.
"Start a debugging session for the payment service errors."
→ AI creates session, asks clarifying questions
→ You provide logs, AI analyzes
→ AI proposes fixes, you approve
→ AI implements and verifies
"End session and summarize what we fixed."
Different AI models excel at different tasks. Configure clawbot to route intelligently: Claude for complex reasoning, GPT-4 for code, Ollama for offline/private operations.
Code reviews: GPT-4 Turbo (best at code)
Data analysis: Claude Sonnet (best at reasoning)
Simple automation: Ollama llama3 (free, local)
Sensitive data: Ollama only (never cloud)
Common Problems and Solutions
Real-world challenges encountered by clawbot users and how to solve them.
Cause: Ambiguous instructions or insufficient context.
Solution: Be more explicit. Use approval workflows for risky commands. Add examples of correct behavior to IDENTITY.md.
"Delete old logs"
"Delete log files in /var/log/myapp/ older than 30 days with names matching *.log. Preserve the most recent 10 files regardless of age. Ask for confirmation before deleting."
Cause: External API rate limits, credential expiration, or transient network failures.
Solution: Build in retry logic with exponential backoff. Monitor ~/.clawbot/logs/ for errors. Set up alerts when automation fails.
Cause: Monitoring thresholds set too sensitive or no noise filtering.
Solution: Add debouncing ("Only alert if condition persists for 5 minutes") and deduplication ("Don't alert more than once per hour for same issue").
Cause: Trying to describe entire workflow in one message.
Solution: Break into stages. Configure one stage, test, then add the next. Use session continuity for incremental refinement.
Cause: AI logging full command outputs including credentials or private data.
Solution: Configure log sanitization rules to redact patterns (API keys, tokens, passwords). Review logs periodically and add new redaction rules as needed.
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