Results for “likert-scale”

Audits typographic decisions on a screen for scale usage, readability, consistency, and token compliance, providing specific fixes.

Creates modular typography scales with size, weight, and line-height relationships for consistent digital interfaces.

Type Scale

Lint and format Python code for Megatron-LM using ruff, black, isort, pylint, and mypy, with commands for autoformatting and import ordering.

Builds, transpiles, and runs quantum circuits with Qiskit, IBM's quantum computing framework, including Qiskit Runtime primitives (Sampler/Estimator), circuit transpilation, and error mitigation on IBM Quantum hardware. Use when targeting IBM Quantum backends, transpiling circuits, running Runtime sessions or batches, or applying resilience/error mitigation. For Google Quantum AI hardware and NISQ circuits prefer alterlab-cirq; for gradient-trained quantum ML and hybrid quantum-classical models prefer alterlab-pennylane; for open-system Lindblad/master-equation dynamics prefer alterlab-qutip. Part of the AlterLab Academic Skills suite.

Calculate risk-based position sizes for long stock trades using fixed fractional, ATR-based, or Kelly Criterion methods with portfolio constraints.

Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.

Evaluate and improve interface usability using heuristic analysis based on Nielsen's 10 heuristics, Krug's laws, and severity ratings.

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.

Grit: General Robust Image Task Benchmark

Simulates open quantum systems with QuTiP, the Quantum Toolbox in Python, solving Lindblad master equations (mesolve), Monte Carlo trajectories (mcsolve), and unitary dynamics (sesolve). Use when studying master-equation or Lindblad dynamics, decoherence, dissipation, quantum optics, cavity QED, or open-system time evolution. NOT for circuit-based quantum computing or hardware execution — for IBM Quantum circuits prefer alterlab-qiskit, for Google Quantum AI or NISQ circuits prefer alterlab-cirq, and for gradient-trained quantum ML prefer alterlab-pennylane. Part of the AlterLab Academic Skills suite.

Quantize large language models to 8/4/3/2/1-bit precision without calibration data, using multiple optimized backends for deployment with vLLM or HuggingFace Transformers.

Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.

Create animated GIFs optimized for Slack with validators for size constraints and composable animation primitives.

Build and evaluate machine learning models using scikit-learn for classification, regression, clustering, dimensionality reduction, and preprocessing.

Language-specific super-code guidelines for scala.

Compute the Kruskal-Wallis H-test using scipy.stats.kruskal for independent samples, returning the H statistic and p-value.

Calculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis. Use when measuring portfolio risk, implementing risk limits, or building risk monitoring systems.

用于审计Claude技能和命令的质量。支持快速扫描（仅变更技能）和全面盘点模式，采用顺序子代理批量评估。

Build, optimize, and execute quantum circuits on IBM Quantum hardware or local simulators using Qiskit, including transpilation, primitives, and algorithm libraries.

Bidirectional conversion between Clojure and Scala. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Clojure↔Scala specific patterns. Use when migrating Clojure projects to Scala, translating Clojure patterns to idiomatic Scala, or refactoring Clojure codebases. Extends meta-convert-dev with Clojure-to-Scala specific patterns.

Evaluates LLM performance using BLEU, ROUGE metrics and LLM-as-judge. Use for model testing.

SVIT: Scaling up Visual Instruction Tuning

Kit de ferramentas abrangente de computação quântica para construir, otimizar e executar circuitos quânticos. Use quando trabalhar com algoritmos quânticos, simulações ou hardware quântico, incluindo (1) Construção de circuitos quânticos com portas e medições, (2) Execução de algoritmos quânticos (VQE, QAOA, Grover), (3) Transpilar/otimizar circuitos para hardware, (4) Executar em IBM Quantum ou outros provedores, (5) Química quântica e ciência dos materiais, (6) Aprendizado de máquina quântico, (7) Visualizar circuitos e resultados, ou (8) Qualquer tarefa de desenvolvimento de computação quântica.

Lints native Knowledge Vault markdown against the capture-llm-wiki schema, checking frontmatter, wikilinks, and citation hygiene, and writes a report.

Evaluate LLM outputs systematically — benchmarks, automated metrics, human preference, and regression tracking

>- Run automatic SPICE simulations on subcircuits detected from KiCad schematic analysis — validates filter frequencies, divider ratios, opamp gains, LC resonance, and crystal load capacitance. Supports ngspice, LTspice, and Xyce (auto-detected). Generates testbenches, runs batch mode, produces structured pass/warn/fail report. Use when the user asks to simulate, verify, or validate any analog subcircuit — RC filters, LC filters, voltage dividers, opamp circuits, crystal oscillators. Also for "simulate my circuit", "run spice", "verify with simulation", "check my filter cutoff", "does this divider give the right voltage", "what's the bandwidth of this opamp stage". Consider suggesting simulation during design reviews when the schematic analyzer reports simulatable subcircuits and a SPICE simulator is available.

Training Compute-Optimal Large Language Models

Quantize LLMs to 8/4/3/2/1-bit precision without calibration data, using multiple backends and HuggingFace/vLLM integration.

Creates a 1920×1080 presentation deck with React-like component layout, strict typography and color constraints, and no template restrictions.

Read the `<!-- pk-commands BEGIN -->` ... `<!-- pk-commands END -->` block

SKILL — Typography

Build, review, debug, or test declarative statistical visualizations in Python with Altair and Vega-Lite, including chart marks, typed encodings, transforms, parameters, layers, facets, and specification export.

Compress large language models using knowledge distillation from teacher to student models, covering temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.

Create data-driven charts with Vega-Lite and Vega, covering bar, line, scatter, heatmap, area, radar, and word cloud visualizations from structured data arrays.