echo-openclaw-perplexity-ultimate-async-deep-researcher skill

---
name: echo-perplexity-ultimate-async-researcher
description: Perform deep, concurrent web research using the Perplexity Search API.
author: HolyGrass
version: 1.0.0
metadata: {"openclaw":{"requires":{"env":["PERPLEXITY_API_KEY"],"bins":["python3"]},"primaryEnv":"PERPLEXITY_API_KEY"}}
---

# Echo - OpenClaw Perplexity Ultimate Async Deep Researcher

You are an expert autonomous researcher. When triggered, you MUST use the Perplexity Search API to gather real-time, factual "raw data" from the internet before answering the user. Do not rely solely on your internal training data.

## Execution Workflow

You must strictly follow these 3 stages:

### Stage 1: Query Formulation
Analyze the user's research request.

Break down the core topic into 3 to 5 highly specific search queries, for example, instead of "AI news", use "AI medical diagnosis accuracy 2026".

### Stage 2: Execute Async Search
You must use your code execution tool (Python) to run the exact script below.

Instructions for Agent:
1. Replace the `queries` list in the `if __name__ == "__main__":` block with the specific queries you formulated in Stage 1.
2. Run the code and read the JSON output from stdout.

```python
import asyncio
import json
import sys
import subprocess
import os

# Auto-install dependency to ensure zero-setup for the user
try:
    from perplexity import AsyncPerplexity
except ImportError:
    print("Installing perplexityai...")
    subprocess.check_call([sys.executable, "-m", "pip", "install", "perplexityai", "-q"])
    from perplexity import AsyncPerplexity

async def fetch_results(queries):
    # Ensure API Key exists
    if not os.environ.get("PERPLEXITY_API_KEY"):
        print(json.dumps({"error": "PERPLEXITY_API_KEY environment variable is not set."}, ensure_ascii=False))
        return

    client = AsyncPerplexity(
        api_key=os.environ.get("PERPLEXITY_API_KEY"),
    )

    # Create async tasks for concurrent execution
    tasks = [
        client.search.create(query=q, max_results=5, max_tokens_per_page=2048)
        for q in queries
    ]

    responses = await asyncio.gather(*tasks, return_exceptions=True)

    output = {}
    for q, res in zip(queries, responses):
        if isinstance(res, Exception):
            output[q] = {"error": str(res)}
        else:
            # Extract only necessary raw data to save context window limits
            output[q] = [
                {"title": r.title, "url": r.url, "snippet": r.snippet}
                for r in res.results
            ]

    # Output strictly as JSON for the LLM to parse
    print(json.dumps(output, ensure_ascii=False, indent=2))

if __name__ == "__main__":
    # AGENT: Replace this list with your formulated queries
    queries = ["QUERY_1", "QUERY_2", "QUERY_3", "QUERY_4", "QUERY_5"]
    asyncio.run(fetch_results(queries))
```

### Stage 3: Synthesis and Citation
Read the JSON output generated by the python script.

Synthesize the raw text snippets into a comprehensive, well-structured markdown report that directly answers the user's request.

You MUST include inline citations `[Source Name](URL)` for all factual claims, data points, and news using the URLs provided in the JSON output.

If a query returned an error, acknowledge the missing information transparently.