Usage Optimization Skill

Version: 1.0.0 Category: Optimization Triggers: High usage alerts, efficiency improvements, batch operations

Quick Reference

Effectiveness Ratings

Approach	Rating	Time Saved
Script + AI Input + AI Command	⭐⭐⭐⭐⭐	90%
Git Operations (Claude)	⭐⭐⭐⭐⭐	80%
Script + Input File	⭐⭐⭐⭐	70%
Preparing Input Files	⭐⭐⭐⭐	75%
Script Only (no input)	⭐⭐⭐	40%
LLM Descriptions	⭐	-20%

Best Practice: Execution Over Description

❌ BAD: "Can you describe what analyze_data.py does?"
    Result: Long description, no actionable output

✅ GOOD: "Prepare input file for data analysis and provide command"
    Result: Working configuration + executable command + actual results

Optimal Workflow Pattern

1. ⭐⭐⭐⭐⭐ AI prepares input YAML file
   └─ Following template in templates/input_config.yaml
   └─ Validated against schema
   └─ Version controlled in config/input/

2. ⭐⭐⭐⭐⭐ AI provides exact bash command
   └─ Points to correct script in scripts/
   └─ References prepared input file
   └─ Includes all necessary flags

3. ⭐⭐⭐⭐⭐ User executes command
   └─ Copy/paste provided command
   └─ Review output and results
   └─ Version control any changes

4. ⭐⭐⭐⭐⭐ Use Claude for git operations
   └─ Commit results
   └─ Create meaningful commit messages
   └─ Manage branches and PRs

Prompt Optimization

Context-First Prompts

## Task Context
- Repository: digitalmodel (Work)
- Complexity: Medium
- Time sensitivity: Production hotfix
- Dependencies: None
- Testing required: Yes

## Specifications
[Full specifications here]

## Output Format
[Exact format needed]

## Constraints
[Any limitations]

Generate [specific deliverable] following this context.

Batch Operations Template

I need to perform the following operations across multiple repositories:

## Scope
- Repositories: [list or "all work" or "all personal"]
- Operation type: [commit/sync/test/build/deploy]

## Configuration
```yaml
operation: batch_commit
scope: work_repositories
config:
  message: "Update dependencies to latest"
  auto_push: true
  run_tests: true

Expected Output

• Status report per repository
• Aggregate success/failure metrics
• Next actions if any failures

## Anti-Patterns to Avoid

### ❌ Description-Only Requests

BAD: "Describe what this script does" Result: No actionable output, wasted tokens

### ❌ Skipping Questions

BAD: Directly generating from vague requirements GOOD: "Before generating, I need to understand: [list]"

### ❌ Making Assumptions

BAD: "I'll assume we want JWT authentication" GOOD: "Should we use JWT, sessions, or OAuth?"

## Usage Monitoring Commands

```bash
# Check usage
./scripts/monitoring/check_claude_usage.sh check

# View today's summary
./scripts/monitoring/check_claude_usage.sh today

# View recommendations
./scripts/monitoring/check_claude_usage.sh rec

# Log a task
./scripts/monitoring/check_claude_usage.sh log sonnet digitalmodel "Feature work"

Daily Checklist

Before Starting Work:

• Check usage at https://claude.ai/settings/usage
• Note Sonnet percentage
• Plan model distribution for session
• Batch similar tasks together

During Work:

• Use Haiku for quick queries
• Reserve Sonnet for standard implementations
• Use Opus only for complex decisions
• Batch related questions

End of Session:

• Review usage increase
• Update usage log
• Plan next session if approaching limits

Target Metrics

Metric	Current	Target
Sonnet usage	79%	<60%
Overall usage	52%	<70%
Model distribution	Unbalanced	30/40/30

Full Reference

See: @docs/AI_AGENT_USAGE_OPTIMIZATION_PLAN.md See: @docs/modules/ai/AI_USAGE_GUIDELINES.md

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Use this when optimizing AI usage, improving efficiency, or managing usage limits.