「pipe flow」の検索結果

Run a structural estimation pipeline — routes to /workflows:work with estimation context from empirical-playbook

日本語の概要は準備中です。原文の説明を表示しています。

Orchestrator-only workflow for migrating/rewriting codebases with full TDD and agent delegation

日本語の概要は準備中です。原文の説明を表示しています。

Cloud-based quantum chemistry platform with Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Use when tasks involve quantum chemistry calculations, molecular property prediction, DFT or semiempirical methods, neural network potentials (AIMNet2), protein-ligand binding predictions, or automated computational chemistry pipelines. Provides cloud compute resources with no local setup required.

日本語の概要は準備中です。原文の説明を表示しています。

Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.

日本語の概要は準備中です。原文の説明を表示しています。

DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.

日本語の概要は準備中です。原文の説明を表示しています。

Automate CircleCI tasks via Rube MCP (Composio): trigger pipelines, monitor workflows/jobs, retrieve artifacts and test metadata. Always search tools first for current schemas.

日本語の概要は準備中です。原文の説明を表示しています。

Use when a user needs to get started with PAIDF Auto-Labeling, plan a scenario, run or debug a shipped cookbook, author prompts or cookbooks, migrate a pipeline, or configure a stage. Confirm critical inputs (data path, output path, endpoints) and ask when any are missing. This is a router: read the matching reference instead of inventing a workflow.

日本語の概要は準備中です。原文の説明を表示しています。

Convert existing Hugging Face Transformers Trainer or TRL SFTTrainer training code into an NVFLARE federated job using flare.patch(trainer), local validation, and job export; use when the user names Hugging Face or preliminary source inspection identifies one Hugging Face owner, and not for manual PyTorch loops, Lightning, inference-only pipelines, deployment, or experiment workflows.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill when the user is debugging any DOCA symptom — a build that won't compile, a link step that can't resolve a doca_* symbol, a runtime call returning DOCA_ERROR_*, a silent service or tool, or a stack trace / valgrind / core dump — and needs the layered ladder (install → version → build → link → runtime → program → driver), verbosity controls (--sdk-log-level, DOCA_LOG_LEVEL, the doca-{lib}-trace flavor), container-debug constraints, or how to capture state for a Developer Forum post. Trigger even when the user does not say "DOCA debug" — implicit phrasings include "undefined reference to doca_*", "how do I get more logs", "packets aren't reaching the wire", "doca_caps returned nothing", or "hugepages empty in the container". Refuse and route elsewhere for library-specific debug (Flow pipe trace, RDMA QP, Comch stats), env-class pkg-config or hugepages symptoms, the DOCA_ERROR_* taxonomy and lifecycle interpretation, and performance or incident-response work — those belong to other skills.

日本語の概要は準備中です。原文の説明を表示しています。

Golang concurrency design — goroutine lifecycle and leak prevention, channels and `select`, channel ownership and direction, `sync.Mutex`/`RWMutex`/`sync.Map`/`sync.Once`/atomics, `errgroup`, `singleflight`, worker pools, and fan-out/fan-in pipelines. Use when writing or reviewing concurrent Go code, when choosing between channels and mutexes, when protecting a shared map or counter, or when a goroutine has no clear exit. Not for defensive coding unrelated to concurrency such as nil panics, slice aliasing, or numeric overflow (→ See `samber/cc-skills-golang@golang-safety` skill), and not for debugging a specific hung, crashing, or racing program after the fact (→ See `samber/cc-skills-golang@golang-troubleshooting` skill).

日本語の概要は準備中です。原文の説明を表示しています。

Call copy number variants using GATK best practices workflow. Supports both somatic (tumor-normal) and germline CNV detection from WGS or WES data. Use when following GATK best practices or integrating CNV calling with other GATK variant pipelines.

日本語の概要は準備中です。原文の説明を表示しています。

Creates and automates custom image builds with EC2 Image Builder - Linux, Windows, and macOS AMIs, and container images to ECR. Covers the build IAM role, Amazon-managed and custom components, image recipes, infrastructure and distribution configuration (launch templates, SSM parameters, other Regions), one-off builds, recurring scheduled pipelines for golden AMI automation and OS patching, custom image workflows, and diagnosing failed builds. Applies when creating, automating, or scheduling AMI or container image builds with Image Builder, or when debugging a failed build. Not for launching instances from existing AMIs, AMI lifecycle/retirement, or general EC2 fleet management.

日本語の概要は準備中です。原文の説明を表示しています。

Configure CI/CD pipeline for automated Figma-to-code generation with Anima. Use when automating design-to-code in GitHub Actions, setting up PR-based component generation, or integrating Anima into design handoff workflows. Trigger with: "anima CI", "anima GitHub Actions", "anima automated generation".

日本語の概要は準備中です。原文の説明を表示しています。

Comprehensive OSINT methodology for external red-team operations and authorized attack-surface assessments. Covers the 6-stage recon pipeline (seed → asset expansion → enrichment → exposure analysis → convergence → operator-armed active validation) with connector-resilience and stage-vs-gating discipline, asset-graph discipline, severity rubric, confidence upgrade workflows, time budgeting, identity-fabric mapping, breach×identity correlation with per-person identity dossiers, detectability tagging, detection-aware probing, WAF/CDN bypass, vulnerability prioritization, phishing infrastructure planning, bug bounty submission, and client deliverable templates. Use when planning or executing reconnaissance against authorized targets, mapping an organization's external attack surface, investigating a person/entity, or producing client deliverables.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill whenever the user wants to generate detailed English prompts for AI image tools (NanoBanana / Gemini / DALL-E / Midjourney) to produce top-conference-quality academic figures — including framework diagrams, network architecture diagrams, pipeline flowcharts, module detail diagrams, comparison/ablation figures, and data pattern grids — especially when the user says "论文配图提示词", "生成论文配图", "学术论文生图", "架构图提示词", "框架图提示词", "顶会风格配图", "CVPR 风格图", "NeurIPS 风格图", "paper figure prompt", "academic diagram prompt", or provides a LaTeX/PDF/Word paper and asks for figure prompts. If the user has not specified a color scheme, present the 8 preset palette options and color tool links before generating any prompt.

日本語の概要は準備中です。原文の説明を表示しています。

Deployment workflows, CI/CD pipeline patterns, Docker containerization, health checks, rollback strategies, and production readiness checklists for web applications.

日本語の概要は準備中です。原文の説明を表示しています。

DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, process FASTQ/BAM/VCF, for developing and executing genomics pipelines.

日本語の概要は準備中です。原文の説明を表示しています。

A comprehensive toolbox for computational molecular biology; use it when you need programmatic sequence/structure parsing, batch bioinformatics pipelines, or automated NCBI/BLAST workflows.

日本語の概要は準備中です。原文の説明を表示しています。

Reverse-engineers the methods section of a biomedical paper into a structured, reproducible workflow. Use this skill when a user wants to understand how a study was actually executed, extract data sources, inclusion/exclusion logic, preprocessing, analytical sequence, software/tools, validation path, and critical parameters, or build a replication checklist from a paper, abstract, DOI, PMID, title, screenshot, or partial methods text. Do not treat this as generic summarization. Focus on reconstructing the operational method pipeline, surfacing missing reproducibility details, and distinguishing explicitly reported steps from inferred or unresolved ones. Never fabricate references, methods details, identifiers, software versions, parameters, datasets, or validation steps.

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Use when performing sample-level dimensionality reduction and visualization on abundance or OTU-style matrices with a companion group file, generating UMAP and/or t-SNE coordinates and plots for group separation assessment. NOT for: differential expression testing, single-cell workflows requiring dedicated embeddings pipelines, or analyses without a sample grouping file.

日本語の概要は準備中です。原文の説明を表示しています。

Design any process, idea, problem, or body of knowledge into an ICM (Interpretable Context Methodology) workspace — folder structure as agent architecture — or restructure an existing folder, repo, or vault into one. Use when the user wants to (1) turn a recurring workflow into an agent-runnable folder pipeline, (2) organize scattered notes, files, or knowledge into a library one AI agent can walk, (3) map a team or company as connected context ("context map", "second brain", "team brain", "knowledge base for AI"), (4) audit a codebase or mixed folder into a walkable edit map (objects, processes, change-impact) so later agents can change it without slurping the tree, (5) audit or restructure an existing workspace to ICM conventions, or (6) says "make this an ICM", "ICM this", "map this repo", "audit this folder", "what would a change hit", "build me a workspace", or "structure this for agents".

日本語の概要は準備中です。原文の説明を表示しています。

Parse raw radar I/Q captures (CW or FMCW mmWave) and produce a cleaned 1-D slow-time signal ready for motion or vital-signs analysis. Use when Claude needs to read interleaved I/Q binary, do a Range FFT on FMCW chirps, remove static clutter, pick a subject range bin, extract phase with unwrapping, or debug why a radar pipeline is returning garbage. Not for pulse/UWB range gating, MIMO beamforming, or Doppler-only gesture radar.

日本語の概要は準備中です。原文の説明を表示しています。

End-to-end pipeline for extracting heart rate (HR) and breathing rate (BR) from raw short-range radar I/Q captures — both continuous-wave (CW, 24 GHz clinical boards) and FMCW mmWave (60/77 GHz, TI IWR/AWR). Use when Claude needs to parse interleaved I/Q binary, do a Range FFT on FMCW chirps, remove static clutter, pick a subject range bin, extract phase with unwrapping, design HR/BR bandpass filters, pick a peak frequency via PSD, reject the HR second harmonic that often dominates the fundamental, or handle respiration-harmonic leakage on slow breathers. Not for pulse/UWB range gating, MIMO beamforming, Doppler-only gesture radar, arrhythmia detection, or multi-subject source separation.

日本語の概要は準備中です。原文の説明を表示しています。

Reference for the GRPO (Group Relative Policy Optimization) algorithm. Use when implementing, debugging, or verifying a GRPO training pipeline — covers the mathematical formulation (group-relative advantages, clipped surrogate loss, KL penalty), the training loop (generate → score → advantage → loss), log-probability computation, advantage estimation, and relationship to PPO/REINFORCE.

日本語の概要は準備中です。原文の説明を表示しています。