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Found 118 Skills
DeepMind Researcher: AGI through deep understanding, AlphaGo/AlphaZero RL, AlphaFold scientific discovery, Gemini multimodal, neuroscience-inspired architectures. Scientific rigor + industrial scale. Triggers: DeepMind research, AlphaGo algorithms, protein folding AI, scientif...
Neo4j Graph Data Science (GDS) plugin — graph projection, algorithm execution, execution modes (stream/stats/mutate/write), memory estimation, and the GDS Python client (graphdatascience v1.21). Use when running gds.pageRank, gds.louvain, gds.wcc, gds.fastRP, gds.knn, gds.betweenness, gds.nodeSimilarity, or any gds.* procedure; projecting named in-memory graphs with gds.graph.project or graph.project; chaining algorithms with mutate mode; computing node embeddings for ML; building recommendation systems with FastRP + KNN. Also triggers on GraphDataScience, GdsSessions, graph catalog operations, ML pipelines, node classification, link prediction. Does NOT cover Aura Graph Analytics serverless sessions — use neo4j-aura-graph-analytics-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover driver setup — use neo4j-driver-python-skill or other driver skill.
Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Guide for video analysis and frame-level event detection tasks using OpenCV and similar libraries. This skill should be used when detecting events in videos (jumps, movements, gestures), extracting frames, analyzing motion patterns, or implementing computer vision algorithms on video data. It provides verification strategies and helps avoid common pitfalls in video processing workflows.
Design and document statistical algorithms with pseudocode and complexity analysis
Role of Web Security Testing and Penetration Engineer, focusing on JavaScript reverse engineering and browser security research. Trigger scenarios: (1) JS reverse analysis: identification of encryption algorithms (SM2/SM3/SM4/AES/RSA), obfuscated code restoration, Cookie anti-crawling bypass, WASM reverse engineering (2) Browser debugging: XHR breakpoints, event listening, infinite debugger bypass, Source Map restoration (3) Hook technology: writing XHR/Header/Cookie/JSON/WebSocket/Canvas Hooks (4) Security product analysis: Offensive and defensive analysis of JS security products such as Ruishu, Jiasule, Chuangyudun, etc. (5) Legal scenarios such as CTF competitions, authorized penetration testing, security research, etc.
Use when implementing RL algorithms, training agents with rewards, or aligning LLMs with human feedback - covers policy gradients, PPO, Q-learning, RLHF, and GRPOUse when ", " mentioned.
Use when "NetworkX", "graph analysis", "network analysis", "graph algorithms", "shortest path", "centrality", "PageRank", "community detection", "social network", "knowledge graph"
GitHub Research Assistant. Use this skill when the user wants to analyze a GitHub repository. Analysis dimensions -- 1) Basic information; 2) Purpose, what it can be used for; 3) Tech stack, including frameworks, languages, algorithms, etc.; 4) Usage and examples; 5) Technical architecture and module analysis
Create and sign JSON Web Tokens (JWTs) for testing and development. Use when the user wants to generate, create, build, or sign a JWT — e.g. "create a JWT", "generate a test token", "sign this payload", "make a JWT with these claims", "build an access token". Supports HMAC, RSA, and ECDSA algorithms.
Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.