highflame-ai/ai-factory/optimize
Claude Code and agent skill by highflame-ai/ai-factory
- community
Checking that the SKILL.md file is still in the repository.
API cost & performance scanner — identify expensive operations and optimization opportunities
Install
npx vskill@latest install highflame-ai/ai-factory/optimizeSemi-extensibleMentions customization, but needs skill-specific knowledge. Read the extensible skills standard
SKILL.md
/optimize — API Cost & Performance Scanner
You are scanning this project's API and infrastructure for cost and performance optimization opportunities.
Ethos
!sh .aif/partials/ethos-include.sh 2>/dev/null || sh ~/.claude/skills/partials/ethos-include.sh
Context
- Architecture: !
cat .aif/context/architecture.md 2>/dev/null || echo "No architecture context found" - Project overview: !
cat .aif/context/project-overview.md 2>/dev/null || echo "No project overview found"
Input
Focus: $ARGUMENTS
Prerequisites
Before proceeding, verify that .aif/context/architecture.md and .aif/context/project-overview.md exist. If any of these files are missing, stop and tell the user: "The .aif/ structure hasn't been initialized. Run /init first to set up the project context."
Instructions
Step 1: Determine Focus
- If given a focus area, prioritize that dimension
- If no argument, scan all dimensions
- Read
.aif/context/architecture.mdfor 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).
- api-cost-scanner agent — provide the focus scope and architecture.md for caching context
- db-perf-scanner agent — provide the focus scope
- 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
- Rank optimizations by impact/effort ratio
- Group into quick wins (small effort, high impact) and strategic improvements
- Suggest which items warrant a full AIF requirement (candidates for
/spec)
Security
- Security scanPassed, 90 of 100Pattern checks on the skill's files.
- Model reviewPassed
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/optimize/SKILL.md
Popularity
- 7-day trend score
- 5
Not refreshed yet.
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