Skill UI

This guide provides minimal working examples demonstrating the three core PostHog functionalities: event capture, user identification, and feature flag evaluation. It covers both server-side (Node.js, Python) and client-side (JavaScript) SDK usage, as well as direct API calls, making it perfect for initial integration setup, testing, and learning basic product analytics patterns.

Provides GraphQL queries to the Open Targets Platform for target-disease associations, tractability, safety, drugs, and multi-omics evidence to guide therapeutic target identification, safety assessment, and repurposing research workflows.

Orchestrate Segment customer-data actions through Rube MCP and Composio, covering event tracking, user identification, grouping, page views, batch dispatches, alias merges, and source management while always refreshing schema info first.

This technique guides the identification of musicians by prioritizing unique personal life events over discography details (albums or songs). It instructs users to use highly distinctive biographical information—such as career turning points, specific personal relationships, or unusual life incidents—as the primary search anchor. This method significantly improves search accuracy when typical musical clues are insufficient.

This guide teaches advanced methodology for identifying target species using multiple biological features. Users learn to combine distinct clues such as habitat, morphology, behavior, and geographic range. A key technique involves first identifying the country through demographic clues (e.g., population growth, endemic status) to significantly narrow the search scope before searching for the specific species.

Python bindings to OpenMS for computational mass spectrometry. Analyze proteomics and metabolomics LC-MS/MS data: feature detection, peptide and protein identification, label-free and isobaric quantification, adduct and accurate-mass annotation, plus ready-to-run workflow scripts.

A comprehensive, advanced playbook for conducting deep network traffic analysis and PCAP forensics. Covers essential skills like PCAP repair, advanced Wireshark filtering (HTTP, DNS, TLS), data extraction (file carving, credential harvesting), and detecting covert channels. It guides users through sophisticated techniques, including TLS decryption workflows and DNS tunneling identification.

This guide provides comprehensive econometric methodologies for establishing causality in accounting and finance research. It covers advanced techniques such as Difference-in-Differences (DID), Propensity Score Matching (PSM), Heckman selection models, and Instrumental Variables (IV), emphasizing the correct handling of regulatory changes and external shocks to meet top-tier academic standards.

This skill guides authors through the rigorous requirements of American Economic Journal: Economic Policy. It helps evaluate whether an applied economics manuscript meets the journal's high bar, focusing specifically on the joint criteria of sound causal identification (DiD, IV, RDD) and clear, impactful policy relevance. Use it to re-frame arguments and check against best practices for policy evaluation.

This guide provides a rigorous framework for defending causal identification claims in empirical microeconomics, tailored for top-tier journals like AEJ: Applied. It covers modern techniques and anti-patterns for RCTs, Difference-in-Differences (DiD), Regression Discontinuity (RDD), and Instrumental Variables (IV), ensuring the mapping from variation to the causal estimand is explicit, defensible, and falsifiable.

A comprehensive guide for academic researchers responding to R&R or conditional acceptance decisions for the American Economic Journal: Applied Economics (AEJ: Applied). This skill structures the response-to-referees letter and revision plan, emphasizing the triage of reviewer comments, adherence to editor priorities (especially concerning identification and robustness), and maintaining a professional, evidence-based tone. It helps authors transform critical feedback into a concrete, actionable revision strategy.

This guide provides a methodology for integrating theoretical models into empirical economics papers, especially for journals emphasizing empirical findings (e.g., AEJ: Applied). It teaches researchers how to ensure that theoretical components—such as mechanisms, welfare statements, or counterfactuals—serve to interpret, sharpen, or guide the existing empirical estimates, rather than leading the paper or replacing the core identification provided by the design. Focus on sufficient statistics and minimal model assumptions is key.