575 skills · sorted by relevance
Analyze time series with aeon, a Python toolkit for data that changes over time. Supports classification, regression, clustering, forecasting, anomaly detection, and pattern search, with guidance on preprocessing, algorithm selection, and evaluation.
Research uncertain design choices, algorithms, or API usage through approved web searches or optional ChatGPT consultation. Get concise options with sources, with separate approval required before sending text externally and before implementing a chosen approach.
Design recommendation and ranking systems that select the best items for a user or situation. Organize retrieval, enrichment, filtering, scoring, selection, and background actions into six stages, then generate a runnable scaffold for your chosen stack.
Approach engineering problems by checking assumptions, defining performance constraints, and choosing suitable tools before implementation. The skill guides algorithm analysis, complete code, performance optimization, and correctness checks through tests, assertions, or formal verification, meaning mathematical checks of correctness.
Improve writing, prompts, or skills through repeated review cycles that carry forward lessons and rejected approaches. Human feedback guides each pass by default, while saved history supports resuming work and tracking progress.
Turn technical ideas into detailed, interactive SVG illustrations that resemble real machines or instruments. Moving parts and live readings share the same model, creating diagrams for articles, documentation, or landing pages that readers can explore.
Model distributed systems and concurrent algorithms in TLA+ or PlusCal, then check possible states with TLC. Use the results to investigate deadlocks, broken rules, and differences between specifications and implementation code.
Assess API business workflows for flaws that automated scanners can miss. The skill examines skipped steps, simultaneous transactions, authentication tokens, GraphQL, and webhooks, helping testers document weaknesses and their financial or operational impact during authorized assessments.
Choose automatic layouts for diagrams made of connected nodes. Match algorithms and tools to the graph structure, then plan connector routes, overlap handling, and stable placement so readers can follow relationships and edits remain predictable.
Test whether an application accepts forged JWTs, signed tokens that carry identity and role information. The skill covers signature bypasses, algorithm confusion, and untrusted keys, then guides verification of access to another user's data or protected functions.
Build Python machine learning workflows for classification, numerical prediction, and grouping similar records. Use scikit-learn guidance and examples to prepare data, compare algorithms, tune model settings, and evaluate results with repeatable processing steps.
Guides authorized testing of cryptographic weaknesses, including weak algorithms, key management errors, and predictable randomness. Defines testing boundaries, alternative areas to examine, and evidence needed to document verified findings or report unsuccessful checks.
Use pymoo in Python to optimize competing goals such as cost and performance under constraints. Find and visualize trade-off solutions, compare algorithms, and select candidates according to preferred priorities.
Port quantum circuits from Qiskit or other frameworks into CUDA-Q while preserving the original algorithm. Guides gate conversion, measurement ordering, precision checks, and small test comparisons to produce code that runs without the source framework.
Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility based on how the recommendation system ranks content.
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An overview of the python package for running the GaMMA earthquake phase association algorithm. The algorithm expects phase picks data and station data as input and produces (through unsupervised clustering) earthquake events with source information like earthquake location, origin time and magnitude. The skill explains commonly used functions and the expected input/output format.
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Implement mHC (Manifold-Constrained Hyper-Connections) for stabilizing deep network training. Use when implementing residual connection improvements with doubly stochastic matrices via Sinkhorn-Knopp algorithm. Based on DeepSeek's 2025 paper (arXiv:2512.24880).
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Match string problems to appropriate algorithms
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Select optimal graph algorithm based on problem constraints
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Implement computational geometry algorithms
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Generate visual representations of algorithm execution
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Production pipeline for mathematical and technical animations using Manim Community Edition. Creates 3Blue1Brown-style explainer videos, algorithm visualizations, equation derivations, architecture diagrams, and data stories. Use when users request: animated explanations, math animations, concept visualizations, algorithm walkthroughs, technical explainers, 3Blue1Brown style videos, or any programmatic animation with geometric/mathematical content.
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**UTILITY SKILL** - Explain code snippets, functions, and algorithms in plain language. USE FOR: explain code, what does this code do, break down this function, help me understand this, walk through this algorithm, clarify this logic, explain this snippet, describe what happens here. DO NOT USE FOR: writing new code (use code generation), fixing bugs (use debugging), refactoring (use refactoring skills), code review with action items. INVOKES: file reading tools to access code, language detection for tailored explanations. FOR SINGLE OPERATIONS: If the user just needs to see file contents, use file reading tools directly.
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Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.
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