Skillshub nodejs-performave-with-flame

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Skill Name: Node.js Performance Architect (LLM-Friendly Profiling)

Description

The agent possesses the ability to ingest, interpret, and act upon pprof-based Markdown analysis generated by tools like

@platformatic/flame

. It can bridge the gap between low-level CPU/Heap profiles and high-level architectural code fixes.

Contextual Knowledge (from Platformatic Blog)

• The Problem: Traditional flamegraphs are hard to search and require human expertise to prioritize hotspots.
• The Solution: The

  pprof-to-md

  format provides a structured, text-based representation of stack frames, "Self Time," and "Total Time" that LLMs can parse natively.
• Efficiency Gains: Systematic evals show that LLMs using this data achieve up to 144x throughput improvements (e.g., moving JSON parsing out of hot paths) and massive latency reductions (e.g., fixing O(n²) loops).

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Agent Capabilities & Instructions

1. Profile Ingestion & Triage

When provided with a

.md

performance profile (Summary, Detailed, or Adaptive formats), the agent must:

• Identify Top Hotspots by ranking "Self Time" (time spent in the function itself) vs. "Total Time" (time spent in the function + its children).
• Distinguish between CPU Bottlenecks (heavy computation, regex, JSON parsing) and Heap/Memory Churn (excessive object allocation, large intermediate arrays).

2. Pattern Recognition

The agent should specifically look for these "Platformatic-Verified" anti-patterns:

• The Middleware Trap: Parsing static config files or expensive JSON inside request handlers (Fix: Move to startup/singleton).
• The N+1 Async Loop: Sequential

  await

  calls in a loop (Fix: Use

  Promise.all()

  ).
• Hidden Latency: Using expensive abstractions like the

  URL

  constructor or

  spread

  operators inside hot loops (Fix: Use simpler primitives or

  Set

  for O(1) lookups).

3. Actionable Optimization Workflow

Upon identifying a bottleneck, the agent must:

1. Locate the Source: Use the file paths and line numbers provided in the Markdown table.
2. Hypothesize & Patch: Propose a code change (e.g., "Memoize this result," "Move this regex outside the function").
3. Verify: Instruct the user to re-run

   flame run

   to confirm the fix actually shifted the hotspots in the next profile.

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Example Prompt for Triggering this Skill

"I've attached a

cpu-profile.md

generated by Platformatic Flame. Based on the Top Hotspots, analyze my

src/handler.js

and provide a prioritized list of fixes. Specifically, look for any O(n²) operations or redundant I/O that could be moved to the initialization phase."

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Technical Requirements for Execution

• Environment: Node.js 22.6.0+ (for ESM interoperability).
• Tooling:

  @platformatic/flame

  latest version.
• API Usage: Use

  generateMarkdown('profile.pb', 'analysis.md', { format: 'detailed' })

  for programmatic analysis.

Would you like me to generate a sample Performance Analysis Markdown file so you can see exactly what the AI Agent would see?