automateIdentify tasks that waste tokens. Scripts don't hallucinate, don't cost per-run, and don't fail randomly. Spot automation opportunities and build them.
Install via ClawdBot CLI:
clawdbot install automateLLMs are expensive, slow, and probabilistic. Scripts are free, fast, and deterministic.
Every time you do something twice that could be scripted, you're wasting:
Check signals.md for detection patterns. Check templates.md for common script patterns.
Before doing any task, ask:
If yes to all three β script it, don't LLM it.
| Task type | Script | LLM |
|-----------|--------|-----|
| Format conversion (JSONβYAML) | β | β |
| Text transformation (regex) | β | β |
| File operations (rename, move) | β | β |
| Data validation | β | β |
| API calls with fixed logic | β | β |
| Git workflows | β | β |
| Judgement calls | β | β |
| Creative content | β | β |
| Ambiguous inputs | β | β |
| One-time unique tasks | β | β |
When you notice yourself:
When you spot an opportunity:
π§ Automation opportunity
Task: [what you keep doing]
Frequency: [how often]
Current cost: [tokens/time per run]
Proposed script:
- Language: [bash/python/node]
- Input: [what it takes]
- Output: [what it produces]
- Location: [where to save it]
Estimated savings: [tokens/time saved per month]
Should I write it?
When writing automation:
Document what you've built:
### Active Scripts
- scripts/format-json.sh β JSON prettifier [saved ~2k tokens/week]
- scripts/deploy-staging.sh β one-command deploy [saved 5min/deploy]
- scripts/sync-env.sh β env file sync [eliminated manual errors]
### Candidates
- Weekly report generation β repetitive formatting
- Log parsing β same grep patterns every time
If you do something 3 times, it must become a script.
| Don't | Do instead |
|-------|------------|
| Re-prompt for same transformation | Write a script once |
| Use LLM for data validation | Write validation rules |
| Burn tokens on formatting | Use formatters (prettier, jq, etc.) |
| Ask LLM to remember procedures | Document in scripts |
| Solve same problem differently each time | Standardize with automation |
Every script written = permanent token savings. Compound your efficiency.
Generated Mar 1, 2026
A data engineering team frequently converts JSON logs to YAML for configuration files. Using an LLM for each conversion wastes tokens and time. This skill helps identify the repetitive, rule-based nature and script it with tools like jq or Python scripts.
A support team validates incoming ticket data against fixed rules, such as email format or priority levels. Instead of querying an LLM repeatedly, this skill guides them to write a validation script, saving tokens and ensuring consistency.
A marketing agency formats social media posts from raw text to specific templates. The repetitive task involves adding hashtags and character limits. This skill automates the formatting with scripts, freeing up LLM usage for creative copywriting.
A DevOps team performs routine Git operations and API calls for staging deployments. The skill identifies these as deterministic tasks, prompting script creation to automate steps like branch merging and environment syncing, reducing manual errors.
Offer a platform that scans user workflows to detect automation opportunities, generating scripts on-demand. Revenue comes from subscription tiers based on script complexity and usage analytics, targeting teams looking to cut LLM costs.
Provide services to audit companies' AI usage, identify token-wasting tasks, and build custom automation scripts. Revenue is project-based or retainer fees, ideal for enterprises with high LLM expenditures seeking efficiency gains.
Create a marketplace where users can buy and sell pre-built automation scripts for common tasks like data formatting or API integrations. Revenue is generated through transaction fees and premium listings, catering to developers and small businesses.
π¬ Integration Tip
Integrate this skill into existing CI/CD pipelines or workflow tools by adding automation checks before LLM calls, using the decision matrix to route tasks appropriately.
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