DevOps & Infra

microsoft Bundle

End-to-end orchestrator: from a business idea, app idea, or existing app to running Azure deployment with cost estimates and pre-deploy approval. Analyzes your app, auto-detects the right Azure services, scaffolds infrastructure code, and deploys — tailored to your app, not a template. Handles moving existing apps to Azure without rewriting or with minimal changes. WHEN: bring your app to Azure, plan my app, cost to run, is my code ready to deploy, deploy my app to the cloud, deploy all my services, what Azure services do I need, plan my Azure deployment, deploy my new app to Azure, one-click deploy, I have an app and want it on Azure, migrate my app to Azure, help me get started, build an app, no code yet, starter project. DO NOT USE FOR: use azd for deployment(use azure-deploy), optimizing existing costs (use azure-cost), code readiness checks only (use azure-app-onboard-prereq).

microsoft Bundle

Assess whether source code is ready to deploy to Azure — the check BEFORE infrastructure work. Evaluates build health, app completeness, dependencies and local services, stack compatibility, and deployment feasibility. Answers questions about what your app needs before it can be deployed — frameworks, dependencies, and configuration. Checks whether dependencies are compatible and identifies deployment blockers and unsupported frameworks. WHEN: "evaluate my repo", "is my app ready to deploy", "what does my app need to deploy", "what do I need before deploying", "does my app need", "can I ship this to Azure", "scan my repo for issues", "is this app deployable", "check if my app is ready for Azure", "do I need a Dockerfile", "what's blocking my deployment", "are there any blockers", "are my dependencies compatible", "does Azure support my framework", "what needs to change before deploying", "check my app configuration".

clawbio Bundle

Predict gene and transcript structure (intervals, exons, strand) from a DNA sequence using the Genomic Intelligence DNA Annotation model, via the hosted /v1/tasks/annotation/predict API. Submitted asynchronously — the pipeline takes ~20 s for ~20 kbp.

oliver-kriska Bundle

Elixir/Phoenix deployment patterns — Dockerfile, fly.toml, runtime.exs, mix release, rel/ overlays. Use when configuring Fly.io, Docker, CI/CD, health checks, or production migrations.

jamie-bitflight Skill

Use when creating a new feature task with structured tracking, phases, and documentation. Activates on "create a feature task", "set up development tracking", or "plan a feature implementation" requests. Produces a comprehensive feature development task with acceptance criteria, phase breakdown, and tracking artifacts ready for SAM pipeline execution.

jamie-bitflight Skill

Set up CI/CD pipeline for Python package publishing to PyPI. Use when preparing to publish a package, when setting up automated releases, or when configuring GitHub Actions or GitLab CI for Python projects.

ai-analyst-lab Skill

Change how visibly Claude surfaces analytical work during L3+ analyses. Three modes — guided (announce each phase and pause for /continue), narrated (announce each phase and run end-to-end), autopilot (silent end-to-end, final output only). Use this skill whenever the user invokes `/pace`, `/pace guided`, `/pace narrated`, `/pace autopilot`, or says things like "slow down and walk me through", "pause between steps", "just run it silently", "narrate each step", "don't stop to ask me", "stop narrating". Pace is orthogonal to complexity level — it changes surfacing, not which agents run. Full spec and phase-banner format live in the question-router skill. This skill is the write-side: it persists the user's explicit pace choice to `working/session_state.yaml` so it survives across phases, across `/resume-pipeline`, and across sessions.

ai-analyst-lab Skill

Browse, inspect, compare, and clean up past pipeline runs. This skill is your window into analysis history — every `/run-pipeline` execution creates a tracked run with its own working directory, outputs, and state. Use this skill whenever the user wants to see what analyses have been executed, review past work, compare different approaches to the same problem, or clean up old runs. Trigger on phrases like "/runs", "show me my past analyses", "what pipeline runs do I have?", "list my pipeline runs", "show run history", "what did we analyze last week?", "compare these two analyses", "clean up old runs", "show me the latest run", "what was that analysis we did on [dataset]?", "review past work", "show me completed analyses", or any mention of viewing, listing, comparing, or managing previous pipeline executions. Also trigger when users reference a specific analysis by date or dataset name and want to see its details. This skill provides critical context for iterative analytical work — users often need to revisit

ai-analyst-lab Skill

Quick, interactive data exploration without the full pipeline. Use this skill whenever a user wants to explore data, understand what's in a dataset, browse tables or columns, check data distributions, spot patterns, or get familiar with data before analysis. Trigger on phrases like "/explore", "let me explore the data", "what's in this dataset?", "show me what data we have", "browse the data", "what tables are available?", "what columns does X have?", "show me a sample of the data", "what does the distribution look like?", "explore this table", or any request to understand data structure, contents, or patterns before diving into formal analysis. This is especially important after connecting a new dataset or when the user wants to poke around without committing to a specific analytical question yet. DISAMBIGUATION: this is interactive poke-around within an already-known dataset. For a first-contact dataset-wide overview ("let me poke around"), use `data-map`; for a plain schema/structure listing, use `/data` (

ai-analyst-lab Bundle

Plan and run coordinated analysis using explicit input bindings, isolated workers, run-local artifacts, and the Python workflow controller.

ai-analyst-lab Skill

Resume a specifically identified analysis using its verified run-local artifacts and controller state.

ai-analyst-lab Skill

Save completed analyses to the knowledge system's analysis archive for future reference. Use this skill after completing any L3+ analysis, when `/run-pipeline` completes, when the user explicitly says "save this analysis" or "archive this", or automatically at the end of Step 18 (Close the Loop) in the analysis workflow. This skill captures key findings, metrics used, agents invoked, and output file paths so past work can be referenced in future sessions. Trigger whenever you finish validation on a multi-step analysis, complete an analytical deck, wrap up a root cause investigation, finish an opportunity sizing exercise, or close any analysis that produced deliverables worth preserving. Also apply when the user mentions saving work, archiving results, preserving findings, or wants to ensure an analysis can be recalled later. This is your analytical memory system — use it proactively to build institutional knowledge.

stanfish06 Skill

Building HTTP/JSON APIs in Python with FastAPI — path/query/body params, Pydantic v2 models, async endpoints, dependency injection, the lifespan startup/shutdown pattern, error handling, and testing with TestClient/httpx. Use when creating a REST API or web backend in Python, adding endpoints, wiring request validation, or serving an ML model behind HTTP. Deploy with uvicorn/gunicorn (often via docker).

stanfish06 Bundle

Generate and analyze DNA sequences using NVIDIA's Evo 2 BioNeMo NIM microservice. Use for Evo2/Evo 2, DNA generation, genomic sequence generation, hosted generation, local Docker deployment, local forward passes, layer outputs, logits, sampled probabilities, and BioNeMo NIM workflows.

stanfish06 Skill

Data Version Control (DVC) for tracking large datasets/models with Git-like semantics, defining reproducible data/ML pipelines (dvc.yaml stages that only re-run when their inputs change), and lightweight experiment tracking without a server. Use when large files (VCF/BAM/FASTQ, reference genomes, model weights) can't go in Git, when you need Make/Snakemake-style selective re-execution driven by data, or when comparing many training runs locally before promoting one. Pairs with Git (code), cloud object storage (data), and Snakemake/Nextflow (compute graph).

twells89 Bundle

Authenticate against the Sigma Computing REST API and obtain a bearer token. Use whenever the user wants to call the Sigma API directly with curl/HTTP, exchange OAuth client credentials for an access token, sign in interactively via a browser OAuth login (authorization-code + PKCE), configure SIGMA_BASE_URL / SIGMA_CLIENT_ID / SIGMA_CLIENT_SECRET, troubleshoot 401/403 responses, or pick the right Sigma API hostname for their cloud. Use as a prerequisite when another Sigma skill needs an SIGMA_API_TOKEN.

stanfish06 Bundle

Interact with Langfuse and access its documentation: tracing, monitoring, creating datasets, running experiments, and evaluating AI applications. Use when needing to (1) query or modify Langfuse data, (2) look up Langfuse documentation, concepts, integration guides, a feature or SDK usage, or (3) do any AI engineering task (AI observability, prompt engineering/management, evaluation and evaluator management, experimentation, dataset management, evaluation-driven CI/CD, feedback collection). Invoke it for tasks in this scope even when Langfuse is not configured or explicitly mentioned.

stanfish06 Bundle

Build, run, and debug Nextflow data pipelines and nf-core workflows end to end. Use whenever the user mentions Nextflow, nf-core, .nf files, nextflow.config, DSL2, processes/channels/operators, samplesheets, or wants to run a community pipeline (e.g. nf-core/rnaseq, nf-core/sarek), write or test a module/subworkflow with nf-test, configure executors/containers (Docker, Singularity/Apptainer, Conda, Wave), scale a workflow to HPC/SLURM or cloud (AWS Batch, Google Batch, Azure, Kubernetes), or debug a failed/-resume run. Make sure to use this skill for any reproducible scientific/bioinformatics workflow work even if the user does not say the word "Nextflow", and for authoring nf-core-compliant pipelines, modules, configs, and linting.

stanfish06 Bundle

Build clinical/healthcare deep-learning pipelines with PyHealth — loading EHR/signal/imaging datasets (MIMIC-III/IV, eICU, OMOP, SleepEDF, ChestXray14, EHRShot), defining tasks (mortality, readmission, length-of-stay, drug recommendation, sleep staging, ICD coding, EEG events), instantiating models (Transformer, RETAIN, GAMENet, SafeDrug, MICRON, StageNet, AdaCare, CNN/RNN/MLP), training with the PyHealth Trainer, computing clinical metrics, and using medical code utilities (ICD/ATC/NDC/RxNorm lookup and cross-mapping). Use this skill whenever the user mentions PyHealth, MIMIC, eICU, OMOP, EHR modeling, clinical prediction, drug recommendation, sleep staging, medical code mapping, ICD/ATC codes, or any healthcare ML pipeline that fits the dataset → task → model → trainer → metrics pattern, even if "PyHealth" isn't named explicitly.

stanfish06 Bundle

Runs the Spec-Kit (GitHub SDD) artifact pipeline — constitution, spec, clarify, plan, tasks, analyze, implement — using its templates. Use when a feature needs a versioned spec/plan/tasks set under specs/NNN-feature/, when requirements must be pinned before any code, or when checking spec-plan-tasks consistency before implementing.

stanfish06 Bundle

Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.

stanfish06 Skill

Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.

stanfish06 Skill

Containerizing and shipping applications with Docker — writing efficient Dockerfiles (multi-stage builds, layer caching, small/secure images), docker compose for multi-service local stacks, and the core build/run/debug commands. Use when creating a Dockerfile, debugging image builds, slimming images, or composing services (app + db + cache). For Claude Code dev sandboxes use devcontainer-setup; for serverless GPU runs use modal.

stanfish06 Bundle

Use for authorized cloud, container, and Kubernetes security assessment including metadata SSRF, IAM misconfig, container escape paths, and cluster RBAC review.

stanfish06 Bundle

Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.

stanfish06 Bundle

Process, quality-control, annotate, and visualize single-cell or single-nucleus RNA-seq datasets across tissues and species. Use when Codex needs to build, adapt, or review a general scRNA-seq QC pipeline; choose dataset-appropriate cell-level filters from QC distributions; run required scDblFinder-based doublet and ambient-RNA filtering; annotate cells with matched references or marker-based fallbacks; or generate global and per-group UMAP visualizations for large scRNA-seq datasets.

stanfish06 Bundle

Hugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audio, and multimodal tasks. Use when working with AutoModel, pipelines, tokenizers, or TrainingArguments—not for general ML outside the Transformers library.

stanfish06 Skill

Configure AWS MCP servers for documentation search and API access. Use when setting up AWS MCP, configuring AWS documentation tools, troubleshooting MCP connectivity, or when user mentions aws-mcp, awsdocs, uvx setup, or MCP server configuration. Covers both Full AWS MCP Server (with uvx + credentials) and lightweight Documentation MCP (no auth required).

stanfish06 Bundle

Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 8 modes: full research, quick brief, paper review, lit-review, fact-check, three-way literature scan, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review, devil's advocate challenges, ethics review, and post-research literature monitoring. Triggers on: research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, WHY HOW WHAT papers, 3W literature scan, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 三段式文獻掃描, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題, 심층 연구, 문헌 조사, 체계적 문헌고찰, 메타분석, 사실 확인, 연구 방향을 잡아줘, 연구 주제 정하는 것을 도와줘.

stanfish06 Skill

Recommend or run the exact `dotnet test` command. Use when the user asks to run, filter, or troubleshoot .NET tests or wants the precise command, flags, or argument order — the right syntax depends on the test platform (VSTest vs Microsoft.Testing.Platform) and SDK version and is easy to get wrong from memory. USE FOR: running all tests or a subset (a specific class, category, or trait) via filters; a single framework in a multi-TFM project (`--framework`); TRX reports; crash or hang dumps; whether MTP args need the `--` separator (SDK 8/9) or pass directly (SDK 10+); diagnosing why `dotnet test` fails or uses wrong argument syntax. Detects the platform (VSTest vs MTP) and framework (MSTest/xUnit/NUnit/TUnit), then picks the matching command and filter flag (--filter, --filter-class, --filter-trait, --filter-query, --treenode-filter). DO NOT USE FOR: writing test code (use code-testing-agent), iterating on failing tests without rebuilding (use mtp-hot-reload), CI/CD config, or debugging test logic.

stanfish06 Skill

Terraform and OpenTofu infrastructure-as-code (IaC) — declare cloud/SaaS resources in HCL, manage state with remote backends and locking, author and consume modules, and run the init/plan/apply/destroy lifecycle. Use whenever the user mentions Terraform, OpenTofu, the tofu CLI, IaC, "infrastructure as code", provisioning cloud resources, .tf/HCL files, tfstate/backends, providers, modules, workspaces, drift, or importing existing infrastructure. For AWS-only IaC written in a general-purpose language (TypeScript/Python) via CloudFormation, prefer aws-cdk-development instead.

stanfish06 Skill

You are an advanced Docker containerization expert with comprehensive, practical knowledge of container optimization, security hardening, multi-stage builds, orchestration patterns, and production deployment strategies based on current industry best practices.

stanfish06 Bundle

End-to-end GWAS automation wrapping PLINK2 for genotype QC and REGENIE for two-step whole-genome regression association testing. Produces Manhattan plots, QQ plots, clumped lead variants, and structured summary statistics.

stanfish06 Bundle

Build sandboxed applications for secure code execution. Load when building AI code execution, code interpreters, CI/CD systems, interactive dev environments, or executing untrusted code. Covers Sandbox SDK lifecycle, commands, files, code interpreter, and preview URLs. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

stanfish06 Bundle

Chunked N-D arrays for cloud storage (Zarr-Python 3). Compressed arrays, parallel I/O, S3/GCS via fsspec, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.

stanfish06 Bundle

Expert-thinking profile for Meteorologist (operational / research atmospheric forecasting): Reasons from hydrostatic and geostrophic balance, scale-dependent dynamics, and the obs-to-NWP pipeline; works the Snellman funnel, matches HRRR/GFS/ECMWF to scale, and treats spin-up, convective scheme bias, radar AP, and PoP misinterpretation as first-class failure modes.