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perf

Use when anything in a Phoenix app is slow, times out, or uses too much memory, even if the cause looks obvious. Load it before analyzing or fixing: it runs measured Ecto, LiveView and OTP checks and ranks fixes.

Install / Use

npx skills add oliver-kriska/claude-elixir-phoenix --skill perf

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

87/100

Supported Platforms

Universal

Tags

Our assessment of perf

perf scores 87/100 on our quality scale, 1741st of 4,634 Development & Engineering skills we index (top 38%).

Its SKILL.md is 3.8 KB long, well organised into 16 sections with 4 code examples: a solid amount of guidance for an agent.

It has 560 GitHub stars, a meaningful sign that others use it.

Substance
26/30
Structure
20/20
Description
15/15
Adoption
12/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 2 days ago, so perf is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

perf compared with similar skills

All 4 of these similar skills score higher than perf; compare them before choosing.

SkillScoreStarsUpdatedFormat
perf (this skill)by oliver-kriska875602d agoSKILL.md
ai-job-searchby MadsLorentzen10044.9ktodayCLAUDE.md
claude-howtoby luongnv8910041.7k3d agoCLAUDE.md
algorithmic-artby anthropics100177.9k11d agoSKILL.md
pptxby anthropics100177.9k11d agoSKILL.md

Frequently asked questions

How do I install perf?
Run npx skills add oliver-kriska/claude-elixir-phoenix --skill perf. The install tabs above show the steps for each supported agent.
Which AI agents does perf work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is perf safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is perf still maintained?
The repository was last updated 2 days ago, so perf is actively maintained.

name: perf description: "Use when anything in a Phoenix app is slow, times out, or uses too much memory, even if the cause looks obvious. Load it before analyzing or fixing: it runs measured Ecto, LiveView and OTP checks and ranks fixes." effort: high argument-hint: "[page|context|module] [--focus ecto|liveview|otp]"

Performance Analysis

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

Usage

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

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

  • ${CLAUDE_SKILL_DIR}/references/benchmarking.md — Benchee patterns, profiling, flame graphs

Related Skills

View on GitHub
GitHub Stars560
CategoryDevelopment
Updated2d ago
Forks44

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions