MLOps
模型部署、评估与运维
nix-config
malob
Enable auto TTS for this session
geniml
jaechang-hits
"Geniml is a Python library for genomic interval machine learning. Train and apply region2vec embeddings to convert BED file regions into numeric vectors, load and index genomic interval datasets for ML pipelines, search embedding spaces with BEDSpace, and evaluate embedding quality. Use for chromatin accessibility clustering, regulatory element classification, cross-sample region comparison, and building ML models on genomic intervals."
cellpose-cell-segmentation
jaechang-hits
"Deep learning cell and nucleus segmentation from fluorescence and brightfield microscopy images. Uses pre-trained models (cyto3, nuclei, tissuenet) and a generalist flow-based algorithm that segments cells without requiring retraining on new image types. Outputs label masks for downstream morphology measurement and tracking. Use scikit-image watershed for rule-based segmentation; use Cellpose when deep learning generalization across staining conditions is needed."
napari-image-viewer
jaechang-hits
"Interactive multi-dimensional image viewer for scientific microscopy data. Napari displays 2D/3D/4D arrays as Image, Labels, Points, Shapes, and Tracks layers; supports real-time annotation, plugin-based analysis, and headless screenshot export. Core visualization tool for bioimage analysis workflows. Use ImageJ/FIJI for macro-based processing; use napari for Python-native interactive visualization and plugin-based deep learning segmentation review."
pymc-bayesian-modeling
jaechang-hits
"Bayesian modeling with PyMC 5. 8-step workflow: define model, set priors, define likelihood, sample (NUTS/ADVI), diagnose (R-hat, ESS, divergences), interpret posteriors, compare models (LOO/WAIC), predict. Hierarchical, logistic, GP model variants. Prior/posterior predictive checks."
pyhealth
jaechang-hits
"PyHealth is a Python library for healthcare machine learning. Build clinical prediction models from EHR (Electronic Health Record) data: process MIMIC-III/IV, eICU, and OMOP-CDM datasets, encode medical codes (ICD, ATC, NDC), construct patient-level datasets, and train models (Transformer, RETAIN, GRASP, MedBERT) for tasks including mortality prediction, drug recommendation, readmission, and diagnosis prediction. Alternatives: FIDDLE (EHR preprocessing only), clinical-longformer (NLP on clinical notes only), ehr-ml (EHR embedding only)."
claude-opus-4-5-migration
aiskillstore
Migrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5. Use when the user wants to update their codebase, prompts, or API calls to use Opus 4.5. Handles model string updates and prompt adjustments for known Opus 4.5 behavioral differences. Does NOT migrate Haiku 4.5.
deseq2-differential-expression
jaechang-hits
"Differential expression analysis for bulk RNA-seq using R/Bioconductor DESeq2. Negative binomial GLM with empirical Bayes shrinkage, Wald and LRT tests, multi-factor designs, interaction terms, Salmon tximeta import, apeglm LFC shrinkage, MA/volcano/heatmap visualization. The R gold standard for DE analysis with native Bioconductor integration. Use pydeseq2-differential-expression for Python-based pipelines; use edgeR for TMM normalization."
nnunet-segmentation
jaechang-hits
"Train and deploy automated medical image segmentation models using nnU-Net's self-configuring framework that auto-selects optimal architecture, preprocessing, and training for any modality. Supports CT, MRI, microscopy, and ultrasound with 2D, 3D full-res, 3D low-res, and cascade configurations. Pipeline: convert dataset → plan and preprocess → train (5-fold cross-validation) → find best configuration → predict → ensemble. Use when classical segmentation fails and annotated training data is available."
scikit-survival-analysis
jaechang-hits
"Survival analysis and time-to-event modeling with scikit-survival. Cox proportional hazards (standard/elastic net), Random Survival Forests, Gradient Boosting, SVMs for censored data. C-index (Harrell/Uno), Brier score, time-dependent AUC evaluation. Kaplan-Meier, Nelson-Aalen, competing risks. scikit-learn Pipeline/GridSearchCV compatible. For frequentist regression use statsmodels; for Bayesian survival use pymc; for simpler parametric models use lifelines."
pydanticai-docs
DougTrajano
Use this skill for requests related to Pydantic AI framework - building agents, tools, dependencies, structured outputs, and model integrations.
celltypist-cell-annotation
jaechang-hits
"Automated cell type annotation for scRNA-seq data using pre-trained logistic regression models. CellTypist ships 45+ models covering immune cells, gut, lung, brain, fetal tissues, and cancer microenvironments. Inputs a normalized AnnData; outputs per-cell predicted labels, majority-vote cluster labels, and confidence scores. Use when you want fast, reproducible, reference-model-backed annotation without manual marker inspection."
knowledge-ingest
QuixiAI
Ingest URLs, documents, and text into the memory system as structured knowledge
atdd
swingerman
This skill should be used when the user asks to "build a feature", "implement a feature", "add functionality", "start development", "write acceptance tests", "write specs", "use ATDD", "use TDD with acceptance tests", or begins any feature implementation work. Also triggered by the /atdd command. Enforces the Acceptance Test Driven Development workflow: write Given/When/Then specs before code, generate a project-specific test pipeline, and maintain two test streams.
appfactory-builder
NeverSight
Build and deploy production apps using AppFactory's 7 pipelines (websites, mobile, dApps, AI agents, plugins, mini apps, bots). One prompt → live URL.
megaeth-developer
NeverSight
End-to-end MegaETH development playbook (Feb 2026). Covers wallet operations, token swaps (Kyber Network), eth_sendRawTransactionSync (EIP-7966) for instant receipts, JSON-RPC batching, real-time mini-block subscriptions, storage-aware contract patterns (Solady RedBlackTreeLib), MegaEVM gas model, WebSocket keepalive, bridging from Ethereum, and debugging with mega-evme. Use when building on MegaETH, managing wallets, sending transactions, or deploying contracts.
odoo-code-tracer
unclecatvn
Trace Odoo code execution flow from entry point to end. Use proactively when planning tasks, reviewing code, or understanding how features work end-to-end. Follows all function calls, method overrides, inheritance chains, and callbacks without missing any execution path.
typescript-magician
mcollina
TypeScript wizard specializing in advanced type systems, complex generics, and eliminating any types
agent-tools
inference-sh
"Run 150+ AI apps via inference.sh CLI - image generation, video creation, LLMs, search, 3D, Twitter automation. Models: FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter, and many more. Use when running AI apps, generating images/videos, calling LLMs, web search, or automating Twitter. Triggers: inference.sh, infsh, ai model, run ai, serverless ai, ai api, flux, veo, claude api, image generation, video generation, openrouter, tavily, exa search, twitter api, grok"
linkedin-content
inference-sh
"LinkedIn post writing with hook formulas, formatting rules, and engagement patterns. Covers post types, algorithm signals, character limits, and content pillars. Use for: LinkedIn posts, professional content, thought leadership, B2B content, personal branding. Triggers: linkedin post, linkedin content, linkedin writing, linkedin strategy, linkedin engagement, linkedin algorithm, linkedin hook, linkedin formatting, thought leadership, professional content, b2b content, linkedin growth"
dialogue-audio
inference-sh
"Multi-speaker dialogue audio creation with Dia TTS. Covers speaker tags, emotion control, pacing, conversation flow, and post-production. Use for: podcasts, audiobooks, explainers, character dialogue, conversational content. Triggers: dialogue audio, multi speaker, conversation audio, dia tts, two speakers, podcast audio, character voices, voice acting, dialogue generation, conversation tts, multi voice, speaker tags, dialogue recording"
flux-image
inference-sh
"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"
explainer-video-guide
inference-sh
"Explainer video production guide: scripting, voiceover, visuals, and assembly. Covers script formulas, pacing rules, scene planning, and multi-tool pipelines. Use for: product demos, how-it-works videos, onboarding videos, social explainers. Triggers: explainer video, how to make explainer, product video, demo video, video production, video script, animated explainer, product demo video, tutorial video, onboarding video, walkthrough video, video pipeline"
image-upscaling
inference-sh
"Upscale and enhance images with Real-ESRGAN, Thera, Topaz, FLUX Upscaler via inference.sh CLI. Models: Real-ESRGAN, Thera (any size), FLUX Dev Upscaler, Topaz Image Upscaler. Use for: enhance low-res images, upscale AI art, restore old photos, increase resolution. Triggers: upscale image, image upscaler, enhance image, increase resolution, real esrgan, ai upscale, super resolution, image enhancement, upscaling, enlarge image, higher resolution, 4k upscale, hd upscale"