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name: optimize description: API cost & performance scanner — identify expensive operations and optimization opportunities argument-hint: Optional focus area (e.g., "ai", "caching", "queries", "latency")

/optimize — API Cost & Performance Scanner

You are scanning this project's API and infrastructure for cost and performance optimization opportunities.

Ethos

!sh .adlc/partials/ethos-include.sh 2>/dev/null || sh ~/.claude/skills/partials/ethos-include.sh

Context

• Architecture: !cat .adlc/context/architecture.md 2>/dev/null || echo "No architecture context found"
• Project overview: !cat .adlc/context/project-overview.md 2>/dev/null || echo "No project overview found"

Input

Focus: $ARGUMENTS

Prerequisites

Before proceeding, verify that .adlc/context/architecture.md and .adlc/context/project-overview.md exist. If any of these files are missing, stop and tell the user: "The .adlc/ structure hasn't been initialized. Run /init first to set up the project context."

Instructions

Step 1: Determine Focus

1. If given a focus area, prioritize that dimension
2. If no argument, scan all dimensions
3. Read .adlc/context/architecture.md for current caching and optimization patterns

Step 2: Launch Scanner Agents

Launch 3 formal scanner agents in parallel using the Agent tool. Each agent is defined in ~/.claude/agents/ with its full scanning checklist and model selection (sonnet for deep analysis).

1. api-cost-scanner agent — provide the focus scope and architecture.md for caching context
2. db-perf-scanner agent — provide the focus scope
3. latency-scanner agent — provide the focus scope

Each agent returns structured findings with estimated impact, effort, and risk.

Step 3: Build Optimization Report

Cost Summary

| Service | Est. Monthly Usage | Est. Monthly Cost | Top Optimization | |---------|-------------------|-------------------|------------------| | Claude Sonnet | X calls | $X | ... | | Claude Haiku | X calls | $X | ... | | Gemini Flash | X calls | $X | ... | | SerpAPI | X calls | $X | ... | | Firestore | X reads/writes | $X | ... | | GCS | X operations | $X | ... |

Performance Hotspots

Rank endpoints by estimated latency, highlighting:

• Sequential operations that could be parallel
• Missing caches
• N+1 query patterns
• Unnecessary data fetching

Optimization Opportunities

For each opportunity:

• What: Description of the optimization
• Impact: Cost savings or latency reduction estimate
• Effort: Small / Medium / Large
• Risk: Low / Medium / High (chance of breaking something)

Step 4: Prioritized Recommendations

1. Rank optimizations by impact/effort ratio
2. Group into quick wins (small effort, high impact) and strategic improvements
3. Suggest which items warrant a full ADLC requirement (candidates for /spec)