Perplexity AI 搜索技能：实时网络搜索与文献检索工具，支持来源引用验证

perplexity-search by davila7/claude-code-templates

334 周安装量

23,400 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/davila7/claude-code-templates --skill perplexity-search

AI/机器学习自动化数据处理

🇨🇳中文介绍

Perplexity 搜索

概述

通过 LiteLLM 和 OpenRouter 使用 Perplexity 模型执行 AI 驱动的网络搜索。Perplexity 提供基于网络的实时答案并附有来源引用，非常适合查找最新信息、近期科学文献以及模型训练数据截止日期之后的事实。

此技能通过 OpenRouter 提供对所有 Perplexity 模型的访问，仅需一个 API 密钥（无需单独的 Perplexity 账户）。

何时使用此技能

在以下情况使用此技能：

搜索当前信息或最新进展（2024 年及以后）
查找最新的科学出版物和研究
获取基于网络来源的实时答案
通过来源引用验证事实
跨多个领域进行文献检索
访问模型知识截止日期之外的信息
执行特定领域的研究（生物医学、技术、临床）
比较当前方法或技术

请勿用于：

简单计算或逻辑问题（直接使用模型）
需要代码执行的任务（使用标准工具）
模型训练数据范围内的问题（除非需要验证）

快速开始

设置（一次性）

获取 OpenRouter API 密钥：
- 访问 https://openrouter.ai/keys
- 创建账户并生成 API 密钥
- 为账户充值（建议最低 $5）

配置环境：

# 设置 API 密钥
export OPENROUTER_API_KEY='sk-or-v1-your-key-here'

# 或使用设置脚本
python scripts/setup_env.py --api-key sk-or-v1-your-key-here

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指定领域和来源

为获得高质量结果，请提及偏好的来源：

"According to peer-reviewed publications in high-impact journals..."
"Based on FDA-approved treatments..."
"From clinical trial registries like clinicaltrials.gov..."

结构化复杂查询

将复杂问题分解为清晰的组成部分：

主题：主要主题
范围：感兴趣的具体方面
上下文：时间范围、领域、限制条件
输出：期望的答案格式或类型

示例： "What improvements does AlphaFold3 offer over AlphaFold2 for protein structure prediction, according to research published between 2023 and 2024? Include specific accuracy metrics and benchmarks."

有关查询设计、领域特定模式和高级技术的全面指导，请参阅 references/search_strategies.md。

python scripts/perplexity_search.py \
  "What does recent research (2023-2024) say about the role of gut microbiome in Parkinson's disease? Focus on peer-reviewed studies and include specific bacterial species identified." \
  --model sonar-pro

python scripts/perplexity_search.py \
  "How to implement real-time data streaming from Kafka to PostgreSQL using Python? Include considerations for handling backpressure and ensuring exactly-once semantics." \
  --model sonar-reasoning-pro

python scripts/perplexity_search.py \
  "Compare PyTorch versus TensorFlow for implementing transformer models in terms of ease of use, performance, and ecosystem support. Include benchmarks from recent studies." \
  --model sonar-pro-search

python scripts/perplexity_search.py \
  "What is the evidence for intermittent fasting in managing type 2 diabetes in adults? Focus on randomized controlled trials and report HbA1c changes and weight loss outcomes." \
  --model sonar-pro

python scripts/perplexity_search.py \
  "What are the key trends in single-cell RNA sequencing technology over the past 5 years? Highlight improvements in throughput, cost, and resolution, with specific examples." \
  --model sonar-pro

将 perplexity_search.py 作为模块使用：

from scripts.perplexity_search import search_with_perplexity

result = search_with_perplexity(
    query="What are the latest CRISPR developments?",
    model="openrouter/perplexity/sonar-pro",
    max_tokens=4000,
    temperature=0.2,
    verbose=False
)

if result["success"]:
    print(result["answer"])
    print(f"Tokens used: {result['usage']['total_tokens']}")
else:
    print(f"Error: {result['error']}")

保存和处理结果

# 保存到 JSON
python scripts/perplexity_search.py "query" --output results.json

# 使用 jq 处理
cat results.json | jq '.answer'
cat results.json | jq '.usage'

为多个查询创建脚本：

#!/bin/bash
queries=(
  "CRISPR developments 2024"
  "mRNA vaccine technology advances"
  "AlphaFold3 accuracy improvements"
)

for query in "${queries[@]}"; do
  echo "Searching: $query"
  python scripts/perplexity_search.py "$query" --output "results_$(echo $query | tr ' ' '_').json"
  sleep 2  # 速率限制
done

Perplexity 模型有不同的定价层级：

每次查询的大致成本：

Sonar: $0.001-0.002（最具成本效益）
Sonar Pro: $0.002-0.005（推荐默认值）
Sonar Reasoning Pro: $0.005-0.010
Sonar Pro Search: $0.020-0.050+（最全面）

成本优化策略：

简单事实查找使用 sonar
大多数查询默认使用 sonar-pro
复杂分析保留使用 sonar-pro-search
设置 --max-tokens 以限制响应长度
在 https://openrouter.ai/activity 监控使用情况
在 OpenRouter 仪表板中设置支出限制

错误："OpenRouter API key not configured"

export OPENROUTER_API_KEY='sk-or-v1-your-key-here'
# 或运行设置脚本
python scripts/setup_env.py --api-key sk-or-v1-your-key-here

错误："LiteLLM not installed"

uv pip install litellm

错误："Rate limit exceeded"

重试前等待几秒钟
在 https://openrouter.ai/keys 提高速率限制
在批量处理请求之间添加延迟

错误："Insufficient credits"

在 https://openrouter.ai/account 充值积分
启用自动充值以防止中断

有关全面的故障排除指南，请参阅 references/openrouter_setup.md。

与其他技能的集成

此技能与其他科学技能相辅相成：

与 literature-review 技能结合使用：

使用 Perplexity 查找最近的论文和预印本
用实时网络结果补充 PubMed 搜索
验证引用并查找相关工作
发现数据库索引后的最新进展

与 scientific-writing 技能结合使用：

为引言/讨论部分查找最新参考文献
验证当前技术水平
检查最新术语和惯例
识别最近的竞争方法

与 hypothesis-generation 技能结合使用：

搜索最新研究成果
识别当前知识空白
查找最近的方法学进展
发现新兴研究方向

与 scientific-critical-thinking 技能结合使用：

查找支持和反对假设的证据
定位方法学批评
识别领域内的争议
用当前证据验证主张

具体化：包含领域、时间范围和限制条件
使用术语：领域相关的关键词和短语
指定来源：提及偏好的出版物类型或期刊
结构化问题：具有明确上下文的清晰组成部分
迭代：根据初始结果进行优化

从 sonar-pro 开始：大多数查询的良好默认选择
复杂查询升级：多步分析使用 sonar-pro-search
简单查询降级：基本事实使用 sonar
使用推理模型：需要逐步分析时

选择合适的模型：模型与查询复杂度匹配
设置令牌限制：使用 --max-tokens 控制成本
监控使用情况：定期检查 OpenRouter 仪表板
高效批量处理：尽可能合并相关的简单查询
缓存结果：保存和重用重复查询的结果

保护 API 密钥：切勿提交到版本控制
使用环境变量：将密钥与代码分开
设置支出限制：在 OpenRouter 仪表板中配置
监控使用情况：注意异常活动
轮换密钥：定期更换密钥

scripts/perplexity_search.py：带有 CLI 界面的主搜索脚本
scripts/setup_env.py：环境设置和验证助手

references/search_strategies.md：全面的查询设计指南
references/model_comparison.md：详细的模型比较和选择指南
references/openrouter_setup.md：完整的设置、故障排除和安全指南

assets/.env.example：示例环境文件模板

# LiteLLM 用于 API 访问
uv pip install litellm

# 用于 .env 文件支持
uv pip install python-dotenv

# 用于 JSON 处理（通常预装）
uv pip install jq

OPENROUTER_API_KEY：您的 OpenRouter API 密钥

DEFAULT_MODEL：默认使用的模型（默认：sonar-pro）
DEFAULT_MAX_TOKENS：默认最大令牌数（默认：4000）
DEFAULT_TEMPERATURE：默认温度（默认：0.2）

实时网络搜索：访问训练数据截止日期之外的当前信息
多种模型：从经济高效的 Sonar 到高级的 Sonar Pro Search
简单设置：单个 OpenRouter API 密钥，无需单独的 Perplexity 账户
全面指导：详细的查询设计和模型选择参考资料
成本效益：按使用付费定价，带有使用监控
科学重点：针对研究、文献检索和技术查询进行优化
易于集成：与其他科学技能无缝协作

执行 AI 驱动的网络搜索，以查找当前信息、最新研究以及带有来源引用的可靠答案。

🇺🇸English

Perplexity Search

Overview

Perform AI-powered web searches using Perplexity models through LiteLLM and OpenRouter. Perplexity provides real-time, web-grounded answers with source citations, making it ideal for finding current information, recent scientific literature, and facts beyond the model's training data cutoff.

This skill provides access to all Perplexity models through OpenRouter, requiring only a single API key (no separate Perplexity account needed).

When to Use This Skill

Use this skill when:

Searching for current information or recent developments (2024 and beyond)
Finding latest scientific publications and research
Getting real-time answers grounded in web sources
Verifying facts with source citations
Conducting literature searches across multiple domains
Accessing information beyond the model's knowledge cutoff
Performing domain-specific research (biomedical, technical, clinical)
Comparing current approaches or technologies

Do not use for:

Simple calculations or logic problems (use directly)
Tasks requiring code execution (use standard tools)
Questions well within the model's training data (unless verification needed)

Quick Start

Setup (One-time)

Get OpenRouter API key :
- Visit https://openrouter.ai/keys
- Create account and generate API key
- Add credits to account (minimum $5 recommended)

Configure environment :

# Set API key
export OPENROUTER_API_KEY='sk-or-v1-your-key-here'

# Or use setup script
python scripts/setup_env.py --api-key sk-or-v1-your-key-here

Install dependencies :
```
uv pip install litellm
```

Verify setup :

python scripts/perplexity_search.py --check-setup

See references/openrouter_setup.md for detailed setup instructions, troubleshooting, and security best practices.

Basic Usage

Simple search:

python scripts/perplexity_search.py "What are the latest developments in CRISPR gene editing?"

Save results:

python scripts/perplexity_search.py "Recent CAR-T therapy clinical trials" --output results.json

Use specific model:

python scripts/perplexity_search.py "Compare mRNA and viral vector vaccines" --model sonar-pro-search

Verbose output:

python scripts/perplexity_search.py "Quantum computing for drug discovery" --verbose

Available Models

Access models via --model parameter:

sonar-pro (default): General-purpose search, best balance of cost and quality
sonar-pro-search : Most advanced agentic search with multi-step reasoning
sonar : Basic model, most cost-effective for simple queries
sonar-reasoning-pro : Advanced reasoning with step-by-step analysis
sonar-reasoning : Basic reasoning capabilities

Model selection guide:

Default queries → sonar-pro
Complex multi-step analysis → sonar-pro-search
Explicit reasoning needed → sonar-reasoning-pro
Simple fact lookups → sonar
Cost-sensitive bulk queries → sonar

See references/model_comparison.md for detailed comparison, use cases, pricing, and performance characteristics.

Crafting Effective Queries

Be Specific and Detailed

Good examples:

"What are the latest clinical trial results for CAR-T cell therapy in treating B-cell lymphoma published in 2024?"
"Compare the efficacy and safety profiles of mRNA vaccines versus viral vector vaccines for COVID-19"
"Explain AlphaFold3 improvements over AlphaFold2 with specific accuracy metrics from 2023-2024 research"

Bad examples:

"Tell me about cancer treatment" (too broad)
"CRISPR" (too vague)
"vaccines" (lacks specificity)

Include Time Constraints

Perplexity searches real-time web data:

"What papers were published in Nature Medicine in 2024 about long COVID?"
"What are the latest developments (past 6 months) in large language model efficiency?"
"What was announced at NeurIPS 2023 regarding AI safety?"

Specify Domain and Sources

For high-quality results, mention source preferences:

"According to peer-reviewed publications in high-impact journals..."
"Based on FDA-approved treatments..."
"From clinical trial registries like clinicaltrials.gov..."

Structure Complex Queries

Break complex questions into clear components:

Topic : Main subject
Scope : Specific aspect of interest
Context : Time frame, domain, constraints
Output : Desired format or type of answer

Example: "What improvements does AlphaFold3 offer over AlphaFold2 for protein structure prediction, according to research published between 2023 and 2024? Include specific accuracy metrics and benchmarks."

See references/search_strategies.md for comprehensive guidance on query design, domain-specific patterns, and advanced techniques.

Common Use Cases

Scientific Literature Search

python scripts/perplexity_search.py \
  "What does recent research (2023-2024) say about the role of gut microbiome in Parkinson's disease? Focus on peer-reviewed studies and include specific bacterial species identified." \
  --model sonar-pro

Technical Documentation

python scripts/perplexity_search.py \
  "How to implement real-time data streaming from Kafka to PostgreSQL using Python? Include considerations for handling backpressure and ensuring exactly-once semantics." \
  --model sonar-reasoning-pro

Comparative Analysis

python scripts/perplexity_search.py \
  "Compare PyTorch versus TensorFlow for implementing transformer models in terms of ease of use, performance, and ecosystem support. Include benchmarks from recent studies." \
  --model sonar-pro-search

Clinical Research

python scripts/perplexity_search.py \
  "What is the evidence for intermittent fasting in managing type 2 diabetes in adults? Focus on randomized controlled trials and report HbA1c changes and weight loss outcomes." \
  --model sonar-pro

Trend Analysis

python scripts/perplexity_search.py \
  "What are the key trends in single-cell RNA sequencing technology over the past 5 years? Highlight improvements in throughput, cost, and resolution, with specific examples." \
  --model sonar-pro

Working with Results

Programmatic Access

Use perplexity_search.py as a module:

from scripts.perplexity_search import search_with_perplexity

result = search_with_perplexity(
    query="What are the latest CRISPR developments?",
    model="openrouter/perplexity/sonar-pro",
    max_tokens=4000,
    temperature=0.2,
    verbose=False
)

if result["success"]:
    print(result["answer"])
    print(f"Tokens used: {result['usage']['total_tokens']}")
else:
    print(f"Error: {result['error']}")

Save and Process Results

# Save to JSON
python scripts/perplexity_search.py "query" --output results.json

# Process with jq
cat results.json | jq '.answer'
cat results.json | jq '.usage'

Batch Processing

Create a script for multiple queries:

#!/bin/bash
queries=(
  "CRISPR developments 2024"
  "mRNA vaccine technology advances"
  "AlphaFold3 accuracy improvements"
)

for query in "${queries[@]}"; do
  echo "Searching: $query"
  python scripts/perplexity_search.py "$query" --output "results_$(echo $query | tr ' ' '_').json"
  sleep 2  # Rate limiting
done

Cost Management

Perplexity models have different pricing tiers:

Approximate costs per query:

Sonar: $0.001-0.002 (most cost-effective)
Sonar Pro: $0.002-0.005 (recommended default)
Sonar Reasoning Pro: $0.005-0.010
Sonar Pro Search: $0.020-0.050+ (most comprehensive)

Cost optimization strategies:

Use sonar for simple fact lookups
Default to sonar-pro for most queries
Reserve sonar-pro-search for complex analysis
Set --max-tokens to limit response length
Monitor usage at https://openrouter.ai/activity
Set spending limits in OpenRouter dashboard

Troubleshooting

API Key Not Set

Error : "OpenRouter API key not configured"

Solution :

export OPENROUTER_API_KEY='sk-or-v1-your-key-here'
# Or run setup script
python scripts/setup_env.py --api-key sk-or-v1-your-key-here

LiteLLM Not Installed

Error : "LiteLLM not installed"

Solution :

uv pip install litellm

Rate Limiting

Error : "Rate limit exceeded"

Solutions :

Wait a few seconds before retrying
Increase rate limit at https://openrouter.ai/keys
Add delays between requests in batch processing

Insufficient Credits

Error : "Insufficient credits"

Solution :

Add credits at https://openrouter.ai/account
Enable auto-recharge to prevent interruptions

See references/openrouter_setup.md for comprehensive troubleshooting guide.

Integration with Other Skills

This skill complements other scientific skills:

Literature Review

Use with literature-review skill:

Use Perplexity to find recent papers and preprints
Supplement PubMed searches with real-time web results
Verify citations and find related work
Discover latest developments post-database indexing

Scientific Writing

Use with scientific-writing skill:

Find recent references for introduction/discussion
Verify current state of the art
Check latest terminology and conventions
Identify recent competing approaches

Hypothesis Generation

Use with hypothesis-generation skill:

Search for latest research findings
Identify current gaps in knowledge
Find recent methodological advances
Discover emerging research directions

Critical Thinking

Use with scientific-critical-thinking skill:

Find evidence for and against hypotheses
Locate methodological critiques
Identify controversies in the field
Verify claims with current evidence

Best Practices

Query Design

Be specific : Include domain, time frame, and constraints
Use terminology : Domain-appropriate keywords and phrases
Specify sources : Mention preferred publication types or journals
Structure questions : Clear components with explicit context
Iterate : Refine based on initial results

Model Selection

Start with sonar-pro : Good default for most queries
Upgrade for complexity : Use sonar-pro-search for multi-step analysis
Downgrade for simplicity : Use sonar for basic facts
Use reasoning models : When step-by-step analysis needed

Cost Optimization

Choose appropriate models : Match model to query complexity
Set token limits : Use --max-tokens to control costs
Monitor usage : Check OpenRouter dashboard regularly
Batch efficiently : Combine related simple queries when possible
Cache results : Save and reuse results for repeated queries

Security

Protect API keys : Never commit to version control
Use environment variables : Keep keys separate from code
Set spending limits : Configure in OpenRouter dashboard
Monitor usage : Watch for unexpected activity
Rotate keys : Change keys periodically

Resources

Bundled Resources

Scripts:

scripts/perplexity_search.py: Main search script with CLI interface
scripts/setup_env.py: Environment setup and validation helper

References:

references/search_strategies.md: Comprehensive query design guide
references/model_comparison.md: Detailed model comparison and selection guide
references/openrouter_setup.md: Complete setup, troubleshooting, and security guide

Assets:

assets/.env.example: Example environment file template

External Resources

OpenRouter:

Dashboard: https://openrouter.ai/account
API Keys: https://openrouter.ai/keys
Perplexity Models: https://openrouter.ai/perplexity
Usage Monitoring: https://openrouter.ai/activity
Documentation: https://openrouter.ai/docs

LiteLLM:

Documentation: https://docs.litellm.ai/
OpenRouter Provider: https://docs.litellm.ai/docs/providers/openrouter
GitHub: https://github.com/BerriAI/litellm

Perplexity:

Official Docs: https://docs.perplexity.ai/

Dependencies

Required

# LiteLLM for API access
uv pip install litellm

Optional

# For .env file support
uv pip install python-dotenv

# For JSON processing (usually pre-installed)
uv pip install jq

Environment Variables

Required:

OPENROUTER_API_KEY: Your OpenRouter API key

Optional:

DEFAULT_MODEL: Default model to use (default: sonar-pro)
DEFAULT_MAX_TOKENS: Default max tokens (default: 4000)
DEFAULT_TEMPERATURE: Default temperature (default: 0.2)

Summary

This skill provides:

Real-time web search : Access current information beyond training data cutoff
Multiple models : From cost-effective Sonar to advanced Sonar Pro Search
Simple setup : Single OpenRouter API key, no separate Perplexity account
Comprehensive guidance : Detailed references for query design and model selection
Cost-effective : Pay-as-you-go pricing with usage monitoring
Scientific focus : Optimized for research, literature search, and technical queries
Easy integration : Works seamlessly with other scientific skills

Conduct AI-powered web searches to find current information, recent research, and grounded answers with source citations.

Weekly Installs

260

Repository

davila7/claude-…emplates

GitHub Stars

22.6K

First Seen

Jan 21, 2026

Security Audits

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Installed on

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56,200 周安装

Perplexity AI 搜索技能：实时网络搜索与文献检索工具，支持来源引用验证

🇨🇳中文介绍

Perplexity 搜索

概述

何时使用此技能

快速开始

设置（一次性）

相关 Skills

基本用法

可用模型

构建有效查询

具体且详细

包含时间限制

指定领域和来源

结构化复杂查询

常见用例

科学文献搜索

技术文档

比较分析

临床研究

趋势分析

处理结果

编程式访问

保存和处理结果

批量处理

成本管理

故障排除

API 密钥未设置

LiteLLM 未安装

速率限制

积分不足

与其他技能的集成

文献综述

科学写作

假设生成

批判性思维

最佳实践

查询设计

模型选择

成本优化

安全性

资源

捆绑资源

外部资源

依赖项

必需

可选

环境变量

总结

🇺🇸English

Perplexity Search

Overview

When to Use This Skill

Quick Start

Setup (One-time)

Basic Usage

Available Models

Crafting Effective Queries

Be Specific and Detailed

Include Time Constraints

Specify Domain and Sources

Structure Complex Queries

Common Use Cases

Scientific Literature Search

Technical Documentation

Comparative Analysis

Clinical Research

Trend Analysis

Working with Results

Programmatic Access

Save and Process Results

Batch Processing

Cost Management

Troubleshooting

API Key Not Set

LiteLLM Not Installed

Rate Limiting

Insufficient Credits

Integration with Other Skills

Literature Review