Skill UI

This comprehensive guide assists developers in migrating their code and API calls when Perplexity updates its model generations or changes API parameters. It provides a clear map from legacy models (pplx-api) to the current Sonar family (sonar, sonar-pro), covering model name changes, base URL updates, parameter adjustments, and SDK upgrade steps. Use this guide whenever you encounter deprecation warnings or need to ensure compatibility with the latest Perplexity Sonar models.

A toolkit for phylogenetic tree manipulation, event detection, NCBI taxonomy integration, and publication-quality visualization, streamlining phylogenomics workflows and gene family analysis.

Query InterPro for protein family, domain, and GO annotations via a REST API with Python helpers, enabling function prediction, domain architecture mapping, and evolutionary classification insights for proteomic workflows.

A comprehensive toolkit for reconstructing and analyzing the evolutionary history of biological sequences (genes, proteins, genomes). The standard workflow includes multiple sequence alignment using MAFFT, robust maximum likelihood tree inference with IQ-TREE 2 and FastTree, and advanced visualization/manipulation using ETE3. This skill is essential for studies in viral phylodynamics, microbial genomics, protein family evolution, and molecular clock dating.

This skill provides deep analytical insights into aggregated family health data. It assesses genetic predisposition, identifies patterns of familial diseases, calculates weighted risk scores based on historical data, and generates personalized, actionable prevention recommendations. Ideal for generating comprehensive family health reports and trend analysis.

This skill functions as an elite legal specialist covering all major areas of Brazilian law, including family, criminal, labor, tax, consumer, and corporate law. It provides deep, encyclopedic legal analysis, strategic process guidance, and adapts its communication style to the user's profile (e.g., layperson, lawyer, or student). Ideal for complex legal case analysis and consultation.

This skill enables deep analysis of malware families using the Malpedia API. It allows users to retrieve canonical aliases, associated threat actors, and YARA detection rules, and map complex relationships like loader-payload chains. Ideal for threat intelligence research, incident response, and building automated detection signatures.

This guide details the process of developing precise YARA and YARA-X rules for advanced malware detection. It teaches analysts how to analyze unpacked malware artifacts—such as unique strings, byte sequences, and PE header traits—to extract stable detection anchors. This skill is crucial for threat hunting, malware family classification, and automating the creation of robust security signatures while aggressively minimizing false positives.

Provides end-to-end guidance for fine-tuning and serving Physical Intelligence’s OpenPI pi0 family (pi0, pi0-fast, pi0.5) with both JAX and PyTorch backends, covering dataset prep, norm stats, checkpoint conversion, and low-latency WebSocket policy inference for ALOHA, DROID, and LIBERO tasks.

This guide teaches targeted verification strategies for historical research. When initial searches suggest a candidate name or figure, do not continue with broad searches. Instead, immediately switch to verifying the candidate against all specific constraints (time period, location, family details, major events) using precise search queries to confirm identity and eliminate false positives.

This technique is designed for identifying target Indian actors when the clues are indirect, often pointing to a relative (father, spouse, grandfather) instead of the actor directly. It requires systematic, step-by-step tracking through the family lineage to deduce the identity. Mastering this method allows users to solve complex Bollywood trivia questions by following the family connections.

This technique is designed for entity recognition and hypothesis generation when identifying Indian or Bollywood actors based on indirect clues. Instead of running broad searches, users should hypothesize a small set of candidate names (1-3) using the provided clues (e.g., awards, family history, controversies). Subsequently, targeted verification searches must be executed for each constraint against the candidate names to confirm the correct identity.