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