data-engineering
332
nw-deliver
Orchestrates the full DELIVER wave end-to-end (roadmap > execute-all > finalize). Use when all prior waves are complete and the feature is ready for implementation.
nWave-ai
data-ai
sorting
stars
current ordering strategy
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data-engineering
332
nw-der-review-criteria
Evaluation criteria and scoring for data engineering artifact reviews
nWave-ai
data-ai
data-engineering
332
nw-divio-framework
DIVIO/Diataxis four-quadrant documentation framework - type definitions, classification decision tree, and signal catalog
nWave-ai
data-ai
data-engineering
332
nw-research-methodology
Research output templates, distillation workflow, and quality standards for evidence-driven research
nWave-ai
data-ai
machine-learning
332
nw-five-whys-methodology
Toyota 5 Whys methodology with multi-causal branching, evidence requirements, and validation techniques
nWave-ai
data-ai
machine-learning
332
nw-fp-clojure
Clojure language-specific patterns, data-first modeling, REPL-driven development, and spec
nWave-ai
data-ai
machine-learning
332
nw-fp-fsharp
F# language-specific patterns, Railway-Oriented Programming, and Computation Expressions
nWave-ai
data-ai
machine-learning
332
nw-pbt-stateful
Stateful property-based testing patterns, model-based testing, and anti-patterns
nWave-ai
data-ai
machine-learning
332
nw-sd-framework
4-step system design framework with back-of-envelope estimation, scaling ladder, and common pitfalls
nWave-ai
data-ai
machine-learning
332
orbiter-tx-simulate
Preflight a tx via eth_estimateGas with optional revert reason.
Orbiter-Finance
data-ai
data-analysis
331
reading-withholding
源泉徴収票の画像を読み取り構造化データを返す。 他のスキルから呼び出されるほか、直接ユーザーが呼び出すことも可能。
kazukinagata
data-ai
data-engineering
325
differential-expression
Bulk transcriptomics differential expression with count-aware modeling, design validation, contrast handling, thresholded exports, and publication-ready DE figures.
Runchuan-BU
data-ai
machine-learning
324
ff-develop
Feature development with cross-module impact analysis. Covers trainer hierarchy, model adapters, reward pipeline, config system, sample dataclasses, and distributed training paths. Trigger: 'add feature', 'implement', 'refactor', 'reorganize', 'new capability'.
X-GenGroup
data-ai
machine-learning
324
ff-new-algorithm
Complete workflow for adding a new RL training algorithm. Covers paradigm selection, TrainingArguments subclass, trainer implementation, registry, example config, and verification. Trigger: 'add algorithm', 'new trainer', 'new training method', 'implement algorithm'.
X-GenGroup
data-ai
machine-learning
324
ff-new-model
Complete workflow for adding a new model adapter. Covers analysis, sample dataclass, adapter implementation (7 abstract methods), registry, example YAML, and verification. Trigger: 'add model', 'support new model', 'integrate model', 'new adapter'.
X-GenGroup
data-ai
machine-learning
324
ff-new-reward
Complete workflow for adding a new reward model. Covers pointwise vs groupwise design, __call__ contract, registration, YAML config, multi-reward setup, and verification. Trigger: 'add reward', 'new reward model', 'custom reward', 'scoring function'.
X-GenGroup
data-ai
data-analysis
322
data-visualization
Data visualization with chart selection, color theory, and annotation best practices. Covers chart types (bar, line, scatter, heatmap), axes rules, and storytelling with data. Use for: charts, graphs, dashboards, reports, presentations, infographics, data stories. Triggers: data visualization, chart, graph, data chart, bar chart, line chart, scatter plot, data viz, visualization, dashboard chart, infographic data, data presentation, chart design, plot, heatmap, pie chart alternative
inference-sh
data-ai
machine-learning
322
flux-image
Generate images with FLUX models (Black Forest Labs) via inference.sh CLI. Models: FLUX Dev LoRA, FLUX.2 Klein LoRA with custom style adaptation. Capabilities: text-to-image, image-to-image, LoRA fine-tuning, custom styles. Triggers: flux, flux.2, flux dev, flux schnell, flux pro, black forest labs, flux image, flux ai, flux model, flux lora
inference-sh
data-ai
machine-learning
322
p-image
Generate images with Pruna P-Image models via inference.sh CLI. Models: P-Image, P-Image-LoRA, P-Image-Edit, P-Image-Edit-LoRA. Capabilities: text-to-image, image editing, LoRA styles, multi-image compositing, fast inference. Pruna optimizes models for speed without quality loss. Triggers: pruna, p-image, pruna image, fast image generation, optimized flux, pruna ai, p image, fast ai image, economic image generation, cheap image generation
inference-sh
data-ai
machine-learning
322
qwen-image-2
Generate and edit images with Alibaba Qwen-Image-2.0 models via inference.sh CLI. Models: Qwen-Image-2.0 (fast), Qwen-Image-2.0-Pro (professional text rendering). Capabilities: text-to-image, multi-image editing, complex text rendering. Triggers: qwen image, qwen-image, alibaba image, dashscope image, qwen image 2, qwen image pro
inference-sh
data-ai
machine-learning
320
storyboard-provider-model-extension
用于 Storyboard Copilot 中“新增 AI 供应商”或“在现有供应商下新增模型”的工程化实现与校验。 当需求涉及 `src/features/canvas/models/**`、`src/features/canvas/models/providers/**`、`src-tauri/src/ai/providers/**`、`build_default_providers()`、`resolveRequest`、`model_aliases`、`inventory::submit!`、设置页 API Key 适配、默认模型切换或模型别名兼容时触发此技能。
henjicc
data-ai
data-engineering
319
meeting-transcript
Process meeting recordings and notes into structured decisions, action items, and team dynamics with intelligent noise filtering
huytieu
data-ai