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Found 110 Skills
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.
Designs structured benchmarks for comparing algorithms, models, or implementations. Selects appropriate metrics (latency, throughput, memory, accuracy), designs representative test cases, captures hardware/software context, produces comparison tables with tradeoff analysis, and includes reproduction instructions. Triggers on: "benchmark", "compare performance", "which is faster", "latency comparison", "memory comparison", "run benchmark", "design benchmark", "compare implementations", "evaluate algorithms", "performance comparison", "throughput test", "speed test". Use this skill when comparing two or more implementations, algorithms, or models.
Serverless GDS sessions on Neo4j Aura — covers GdsSessions, AuraAPICredentials, DbmsConnectionInfo, SessionMemory, get_or_create, remote graph projection, gds.graph.project.remote, gds.graph.construct, algorithm execution (mutate/stream/write), async job polling, result retrieval, and session lifecycle. Use when running graph algorithms on Aura Business Critical or VDC, processing graph data from Pandas/Spark, or using the graphdatascience Python client in AGA (serverless) mode. Covers all three data source three source modes (AuraDB-connected, self-managed Neo4j, standalone from DataFrames). Does NOT cover the embedded GDS plugin on Aura Pro or self-managed Neo4j — use neo4j-gds-skill. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover Snowflake Graph Analytics — use neo4j-snowflake-graph-analytics-skill.
Use this skill whenever reverse-engineering a Sketch file (or Figma export with similar shape) into pixel-perfect React + CSS — covers the iteration mental model, tree reconstruction, layout inference algorithms, geometry math, visual-regression diffing, and the style/typography/path conversions that make "improvement without regression" enforceable. Trigger even if the user doesn't explicitly mention "algorithms" but is converting a design source into web code, building a design-to-code pipeline, or struggling to make incremental fidelity improvements without breaking previously-converted output.
Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.
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.
Comprehensive quantum computing toolkit for building, optimizing, and executing quantum circuits. Use when working with quantum algorithms, simulations, or quantum hardware including (1) Building quantum circuits with gates and measurements, (2) Running quantum algorithms (VQE, QAOA, Grover), (3) Transpiling/optimizing circuits for hardware, (4) Executing on IBM Quantum or other providers, (5) Quantum chemistry and materials science, (6) Quantum machine learning, (7) Visualizing circuits and results, or (8) Any quantum computing development task.
Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.
Guidance for solving ARC-AGI style pattern recognition tasks that involve git operations (fetching bundles, merging branches) and implementing algorithmic transformations. This skill applies when tasks require merging git branches containing different implementations of pattern-based algorithms, analyzing input-output examples to discover transformation rules, and implementing correct solutions. (project)
Comprehensive cryptography guidance covering encryption algorithms, password hashing, TLS configuration, key management, and post-quantum considerations. Use when implementing encryption, choosing hashing algorithms, configuring TLS/SSL, managing cryptographic keys, or reviewing cryptographic implementations.
Design and document statistical algorithms with pseudocode and complexity analysis
Implements the Strategy pattern in Python backends. Run when the user mentions strategy pattern, or when you see or need a switch on type/method, multiple behaviors under the same contract, or interchangeable algorithms—apply this skill proactively without the user naming it.