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Statistical arbitrage tool for identifying and analyzing pair trading opportunities. Detects cointegrated stock pairs within sectors, analyzes spread behavior, calculates z-scores, and provides entry/exit recommendations for market-neutral strategies. Use when user requests pair trading opportunities, statistical arbitrage screening, mean-reversion strategies, or market-neutral portfolio construction. Supports correlation analysis, cointegration testing, and spread backtesting.

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Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.

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Analyze codebases to identify reusable code patterns, duplications, and implementation patterns for future development. Use when refactoring code, identifying technical debt, finding opportunities for abstraction, or documenting common patterns in a directory or module. Outputs pattern catalogs, refactoring suggestions, and reusable template code.

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Build maintainable test suites with reusable code patterns

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Extract and consolidate reusable components, design tokens, and patterns into your design system. Identifies opportunities for systematic reuse and enriches your component library.

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Classify incoming issues into the live GitHub label queue, reproduce where cheap, and write an auditable QUEUE.md snapshot. Use when triaging a backlog, running the scheduled triage routine, or deciding what the factory should pick up next.

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Offline-first mobile apps with local storage, sync queues, conflict resolution. Use for offline functionality, data sync, connectivity handling, or encountering sync conflicts, queue management, storage limits, network transition errors.

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Refine a Stage2 NeurIPS rebuttal draft into polished, reviewer-facing prose in the author's style; preserve factual grounding, optionally prepend a courteous opening phrase, and normalize tables/code/formulas into Markdown.

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Think and work like an expert Condensed Matter Physicist. Use when a task calls for Condensed Matter Physicist judgment. Reasons from Bloch bands, quasiparticles, and symmetry through ARPES/STM/neutron/transport workflows, VASP/QE/Wannier90/DMFT, and Materials Project/ICSD/MPDS while treating matrix-element artifacts, Mott vs. DFT gaps, pseudogap vs. SC gap, and Planckian bad-metal transport as first-class failure modes.

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Think and work like an expert Clinical Microbiologist. Use when a task calls for Clinical Microbiologist judgment. Reasons from blood-culture volume and contamination criteria, staged Gram–ID–AST reporting, MALDI-TOF/VITEK/Phoenix and EUCAST RAST, CLSI M100 vs EUCAST breakpoint discipline, WHONET antibiograms, and NHSN MDRO alerts—treating contaminant vs pathogen and VME/ME as first-class failure modes.

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Think and work like an expert Astroparticle Physicist. Use when a task calls for Astroparticle Physicist judgment. Reasons from flux times cross section times acceptance, Poisson counting over structured backgrounds, and Cherenkov photoelectron budgets through SkyLLH unbinned likelihoods, Geant4/CORSIKA chains validated on through-going-muon and calibration samples, and Feldman-Cousins/CLs limits, while treating atmospheric-neutrino and downgoing-muon contamination, ER/NR leakage and the neutrino fog, and look-elsewhere trials as first-class failure modes.

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long short equity strategy, pair trade construction, market neutral portfolio, net exposure management, factor neutrality, equity long short, hedge fund strategy, alpha generation, stock picking portfolio construction, long short position management

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Read, write, and create single-cell data objects using Seurat (R) and Scanpy (Python). Use for loading 10X Genomics data, importing/exporting h5ad and RDS files, creating Seurat objects and AnnData objects, and converting between formats. Use when loading, saving, or converting single-cell data formats.

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Query OpenTargets for drug targets, disease associations, and therapeutic evidence. Use when user asks about drug targets, disease mechanisms, target validation, or drug-disease associations. Triggers on "opentarget", "drug target", "target validation", "disease association", "therapeutic target", "drug for disease".

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Identifies reusable UI patterns, components, and design tokens, then extracts and consolidates them into a design system for systematic reuse.

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Use when working with Aws Batch — aWS Batch compute environment management, job queue analysis, scheduling policy review, and array job monitoring. Covers compute resource utilization, job status tracking, failure investigation, Fargate vs EC2 comparison, and queue priority analysis.

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Design and execute structured exploratory testing sessions. Covers Session-Based Test Management (SBTM), charter writing, heuristic-based exploration (HICCUPS, FEW HICCUPS), bug discovery patterns, note-taking templates, and conversion of findings to automated tests. Use when: "exploratory testing," "SBTM," "manual testing," "bug hunting," "test charter," "heuristic testing." Not for: an AI browser agent autonomously exploring the app from a natural-language goal — use agentic-browser-testing. Not for: testing your product's own AI/LLM features — use ai-system-testing. Related: test-planning, ai-bug-triage, risk-based-testing, agentic-browser-testing.

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Make technology decisions for every product capability by discovering existing assets, evaluating vendor-aligned options, and categorizing as Reuse/Extend/Build/Buy/Integrate/Research during PRD v0.5 Red Team Review. Handles both greenfield and brownfield contexts. Triggers on "tech stack", "build or buy?", "what technologies?", "technical decisions", "what do we reuse?", "existing stack", "vendor constraint", "IBM-first", "what tools do we need?", "evaluate solutions", "select tech stack". Consumes FEA- (features), SCR- (screens), RISK- (constraints). Outputs TECH- entries with decisions, rationale, and cross-references. Feeds v0.6 Architecture Design.

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Analyze indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection, auto-merge candidates, labeling). Also covers creating, inspecting, and repairing a CodeScope index. Use for: code structure, who calls what, why something changed, similar functions, module boundaries, bug tracing, class relationships, PR risk/conflicts, or any question benefiting from a code knowledge graph. Applies when a `.codegraph` index exists in the workspace, or when the user wants to create one.

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Run background tasks in Python with Celery. Use when a user asks to process tasks asynchronously, schedule periodic jobs, run background workers, build task queues in Python, or offload heavy processing from web requests.

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Use when computing orbits, planning maneuvers, propagating trajectories, or analyzing orbital perturbations for spacecraft or celestial bodies. Use when "orbit, trajectory, maneuver, delta-v, Hohmann, Keplerian, perturbation, J2, TLE, orbital elements, semi-major axis, eccentricity, inclination, RAAN, spacecraft propagation, Lambert solver, interplanetary, " mentioned.

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Use when automating model architecture design, optimizing hyperparameters, or exploring neural network configurations - covers NAS algorithms, search spaces, Bayesian optimization, and AutoML toolsUse when ", " mentioned.

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Expert in Ethereum L2 solutions - Optimism, Arbitrum, zkSync, Base, and rollup architecture for scalable dApp developmentUse when "layer 2, l2, optimism, arbitrum, zksync, base, rollup, op stack, scaling, layer2, optimism, arbitrum, zksync, base, rollup, scaling, ethereum" mentioned.

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Expert in Ethereum Virtual Machine internals - gas optimization, assembly/Yul, opcode-level optimization, and low-level EVM patternsUse when "evm, gas optimization, yul, inline assembly, opcodes, solidity optimization, evm bytecode, storage layout, evm, ethereum, solidity, yul, assembly, gas-optimization, opcodes, smart-contracts" mentioned.

日本語の概要は準備中です。原文の説明を表示しています。