phx-perf

name	phx-perf
description	Analyze Elixir/Phoenix performance — N+1 queries, assign bloat, ecto optimization, genserver bottlenecks. Use when slowness, timeouts, or high memory reported.

Performance Analysis

Analyze code for performance issues across Ecto, LiveView, and OTP layers. Prioritize findings by impact and effort.

Usage

/skill:phx-perf                           # Analyze full project
/skill:phx-perf lib/my_app/accounts.ex    # Analyze specific module
/skill:phx-perf --focus ecto              # Ecto queries only
/skill:phx-perf --focus liveview          # LiveView memory only
/skill:phx-perf --focus otp               # OTP bottlenecks only

Arguments

$ARGUMENTS = Optional module/context path and --focus flag.

Iron Laws

1. MEASURE BEFORE OPTIMIZING — Never optimize without evidence of a problem
2. DATABASE FIRST — 90% of Elixir performance issues are query-related
3. ONE CHANGE AT A TIME — Isolate optimizations to measure impact
4. NEVER benchmark in dev mode — Always use MIX_ENV=prod for performance measurements; dev mode includes code reloading, debug logging, and unoptimized compilation that invalidate results

Workflow

Step 1: Identify Scope

Check specific file if provided. Otherwise scan full project:

# Find hot paths: contexts, LiveViews, workers
find lib/ -name "*.ex" | head -50

Step 2: Run Analysis Tracks

Spawn analysis agents in parallel based on focus:

Ecto Track (default or --focus ecto):

Spawn phx:elixir-reviewer with prompt: "Analyze for N+1 queries, missing preloads, unindexed queries, and inefficient patterns. Check: Repo.all in loops, Enum.map with Repo calls, missing preload, queries without indexes on WHERE/JOIN columns."

LiveView Track (default or --focus liveview):

Spawn phx:elixir-reviewer with prompt: "Analyze LiveViews for memory issues: large assigns, missing streams for lists, assigns that grow unbounded, heavy handle_info processing, missing assign_async for slow ops."

OTP Track (only with --focus otp):

Spawn phx:otp-advisor with prompt: "Analyze for OTP bottlenecks: GenServer mailbox growth, synchronous calls in hot paths, missing Task.async for parallel work, ETS opportunities for read-heavy state."

Step 3: Prioritize Findings

Score each finding on a 2x2 matrix:

	Low Effort	High Effort
High Impact	DO FIRST	PLAN
Low Impact	QUICK WIN	SKIP

High impact = affects response time, memory per user, or query count. Low effort = single file change, no migration needed.

Step 4: Present Top 5

Present findings sorted by priority:

## Performance Analysis: {scope}

### 1. {Finding} — DO FIRST
**Impact**: {what improves}
**Location**: {file}:{line}
**Current**: {problematic pattern}
**Fix**: {optimized pattern}
**Estimated gain**: {e.g., "eliminates N+1, reduces queries from O(n) to O(1)"}

### 2. {Finding} — PLAN
...

Step 5: Offer Next Steps

Always end with actionable next steps — findings without follow-up get lost. Present options based on severity:

How would you like to proceed?

- `/skill:phx-plan` — Create a plan from these findings (recommended for 3+ fixes)
- `/skill:phx-quick` — Apply top priority fix directly (1-2 simple fixes)
- `/skill:phx-investigate` — Deep-dive into a specific finding

Tidewave Integration

If Tidewave MCP is available:

• Use mcp__tidewave__project_eval to run Repo.query!("EXPLAIN ANALYZE ...") on suspicious queries
• Use mcp__tidewave__project_eval to check Process.info(pid, :message_queue_len) for GenServer bottlenecks
• Use mcp__tidewave__execute_sql_query to check missing indexes

References

• references/benchmarking.md — Benchee patterns, profiling, flame graphs