nntrivi2001
57dc91585d
Internal SmartGift build of a Claude Code monitoring dashboard. Lanes: a durable unit of parallel agent work, one per working directory, tracked across session restarts. Managed lanes are git worktrees the dashboard provisions and can reset or remove behind a three-check destroy guard and a counted preflight; adopted lanes are directories you already own and are never destroyable. Pipelines: a lane moves through pipeline stages. A stage the agent declares with evidence renders green; a stage inferred from the tool-event stream renders dashed amber and never counts as done. Detection is forward-only within a 30-minute window, and never writes the declared stage. Workspace: one page at /run with a lane grid, the selected lane's pipeline, and a full Claude console behind a disclosure.
83 lines
3.3 KiB
Markdown
83 lines
3.3 KiB
Markdown
---
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description: >
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Suggest concrete optimizations for Claude Code usage based on historical
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session data. Covers cost reduction, speed improvement, error prevention,
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and workflow efficiency. Use for data-driven optimization planning.
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---
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# Optimization Suggest
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Generate data-driven optimization recommendations for Claude Code usage.
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## Input
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The user provides: **$ARGUMENTS**
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This may be:
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- "all" or empty (default: comprehensive optimization scan)
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- "cost" for cost reduction focus
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- "speed" for performance/speed focus
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- "quality" for error reduction focus
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- "efficiency" for workflow efficiency focus
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## Procedure
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1. **Gather optimization data** from `http://localhost:4820`:
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- `GET /api/sessions?limit=200` — session history
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- `GET /api/analytics` — tool and token analytics
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- `GET /api/pricing/cost` — cost data
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- `GET /api/pricing` — pricing rules for model comparison
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- Sample event streams for behavioral analysis
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2. **Analyze optimization opportunities**:
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### 💰 Cost Optimization
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- **Model downgrade opportunities**: Tasks completed with expensive models that could use cheaper ones
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- Compare success rates per model per task type
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- Calculate savings from model substitution
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- **Cache optimization**: Sessions with low cache hit rates
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- Identify sessions that could benefit from better prompt caching
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- **Early termination**: Sessions that ran longer than needed
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- Detect sessions where useful work completed well before session end
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- **Compaction reduction**: Sessions hitting context limits
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- Suggest breaking large tasks into smaller sessions
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### ⚡ Speed Optimization
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- **Tool selection**: Faster alternatives for commonly-used tool patterns
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- **Subagent parallelization**: Tasks that could run in parallel
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- **Session planning**: Better upfront context to reduce back-and-forth
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- **Preemptive context loading**: Frequently needed files/context
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### 🛡 Quality Optimization
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- **Error prevention**: Common error patterns with preventive measures
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- **Tool reliability**: Tools with high failure rates and alternatives
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- **Validation gaps**: Sessions lacking verification steps
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- **Recovery strategies**: Better error handling patterns
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### 🔄 Workflow Optimization
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- **Session sizing**: Optimal session scope based on historical success
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- **Task decomposition**: Complex sessions that should be split
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- **Automation candidates**: Repetitive workflows to automate
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- **Knowledge reuse**: Patterns where previous session context could help
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3. **Quantify each recommendation**:
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- Estimated impact (cost savings $, time savings %, error reduction %)
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- Implementation effort (low/medium/high)
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- Confidence level based on data available
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- Priority score = Impact × Confidence / Effort
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## Output Format
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Present as a prioritized optimization plan:
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| # | Recommendation | Category | Impact | Effort | Priority |
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|---|---------------|----------|--------|--------|----------|
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| 1 | Specific action | 💰/⚡/🛡/🔄 | High | Low | ★★★★★ |
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| 2 | Specific action | ... | ... | ... | ★★★★☆ |
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For the top 5 recommendations, include:
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- Detailed explanation with supporting data
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- Step-by-step implementation guide
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- Expected before/after metrics
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- How to measure success
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