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). The agent should specifically look for these "Platformatic-Verified" anti-pa...
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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?**
