918 件 ・ 関連度順
概要と使いどころ
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
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。
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
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。
"Implement LDA topic modeling to discover latent topics in document collections. Use this skill when the user needs to extract topics from a text corpus, categorize documents by theme, or explore thematic structure — even if they say 'what are the main topics', 'topic extraction', or 'document clustering by theme'.".
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。
Kubernetes penetration testing playbook. Use when targeting Kubernetes clusters via API server, RBAC enumeration, service account abuse, etcd access, Kubelet API, pod escape, cloud-specific metadata, admission webhook bypass, and registry secrets.
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。
Leaflet interactive maps. Use when building a 2D map with tile layers, markers, popups, GeoJSON, layer control, custom controls or marker clustering.
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。
Mapbox GL JS interactive vector maps. Use when building a map with sources and layers, expressions, clustering, 3D terrain, geocoding or directions.
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。
Redis in-memory data store patterns with ioredis and node-redis -- caching, sessions, rate limiting, pub/sub, streams, queues, transactions, cluster
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。
Build human adjudication / hand-labeling sheets from LLM-pipeline data without evidence truncation. Use when: (1) preparing a CSV/Excel sheet for a human to rule on cases an LLM classifier or rater panel judged, (2) a labeler reports "there is no information to label from" or cells look empty in Excel, (3) excerpt columns cluster at one exact length (e.g. all 1,500 chars — a hard truncation cap). Covers: full rating-basis recovery, Excel 32,767-char cell cap, multi-line CSV mangling, ruling dropdowns, companion text files.
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 asks to run Stata commands, estimate econometric models, work with .dta files, run a .do file, generate Stata output, or do any statistical analysis where Stata is involved. Also trigger when the user mentions Stata variables, Stata syntax, or econometric tasks where Stata is the natural tool, including regressions, IV estimation, diff-in-diff, RDD, panel data, clustering, summary statistics, and margins. Stata runs through pystata on StataNow 19.5 BE; configure once with stata_setup, then drive everything with stata.run() and exchange data directly with pandas. Prefer this skill over subprocess calls or .do-file shelling for Stata work, including cases where the user does not say pystata.
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。
Diagnose and fix slow pyfixest regression GRIDS (many feols/fepois calls run sequentially) that stay slow despite demeaner_backend="cupy64" and an idle GPU. Use when: (1) a script looping dozens of pf.feols models on a 100k+ row panel takes ~1 min/model, (2) process inspection shows ~1-1.5 cores busy and nvidia-smi shows ~0% GPU utilization with a resident cupy context, (3) planning any worker prompt that will run a model grid (robustness variants x FE structures x domains). Root cause: per-model CPU-side single-threaded fixed costs (formulaic model-matrix build, interaction construction, singleton detection, cluster vcov) dominate wall time; GPU demeaning is a small slice. Fix: shard the model grid across OS processes and/or use pyfixest multiple-estimation syntax; mandate this IN THE WORKER 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。
Use when a task needs the judgment of an Epidemiologist — investigating a disease outbreak and identifying its source, designing a case-control or cohort study to test an exposure hypothesis, calculating attack rates/odds ratios/relative risk from surveillance data, evaluating whether a surveillance system is sensitive and timely enough to catch a real signal, or judging whether an observed case cluster is a true outbreak or expected background variation.
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。
Find and work with ENCODE single-cell genomics data including scRNA-seq and scATAC-seq. Use when the user asks about single-cell experiments, cell type resolution, clustering from ENCODE data, deconvolution of bulk signals using single-cell references, or comparing single-cell vs bulk profiles. Covers platform differences (10X Chromium, Smart-seq2, Drop-seq), quality limitations of single-cell data, multimodal integration (RNA+ATAC), and cross-study reproducibility concerns. Also use for cell type annotation, gene detection limits, dropout artifacts, and single-cell data structure in ENCODE.
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 Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates. Handles deployment automation, GitOps configuration, incident response runbooks, and internal developer platform tooling. Use when setting up CI/CD pipelines, containerizing applications, managing infrastructure as code, deploying to Kubernetes clusters, configuring cloud platforms, automating releases, or responding to production incidents. Invoke for pipelines, Docker, Kubernetes, GitOps, Terraform, GitHub Actions, on-call, or platform engineering.
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。
Bootstraps Flux CD on Kubernetes clusters, creates and manages GitOps sources (Git/Helm/OCI/Bucket), configures kustomizations and Helm releases, reconciles resources, sets up image automation and alerting, and pushes OCI artifacts. Use this skill whenever the user mentions the flux CLI, flux bootstrap, flux create source, flux create kustomization, flux create helmrelease, flux reconcile, flux get, Flux CD, GitOps with Flux, or asks about managing Kubernetes deployments via GitOps. Also trigger when users ask about creating Git/Helm/OCI/Bucket sources, building or diffing kustomizations, pushing OCI artifacts, setting up image automation, creating Flux alerts/receivers, or bootstrapping Flux on GitHub/GitLab/Gitea/Bitbucket. Even if the user just says "flux" in a Kubernetes context, this skill applies.
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 deploying or managing Kubernetes workloads. Invoke to create deployment manifests, configure pod security policies, set up service accounts, define network isolation rules, debug pod crashes, analyze resource limits, inspect container logs, or right-size workloads. Use for Helm charts, RBAC policies, NetworkPolicies, storage configuration, performance optimization, GitOps pipelines, and multi-cluster 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。
Work with text embeddings — choosing models, pooling, similarity measures, evaluation, and multilingual considerations. Use when building semantic search, clustering, or any similarity-based feature.
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。
Elasticsearch guidance — index design, mappings, query DSL, aggregations, cluster operations, and performance tuning.
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。
Elasticsearch search and analytics — mappings, queries, and cluster health — use when building search or log analytics.
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。
Scale Ray workloads and serve models on Anyscale — managed Ray clusters for training and inference.
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。
Argo CD guidance — GitOps workflows, Application manifests, sync strategies, AppProjects, and multi-cluster ops.
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。
Audit warehouse management system operations -- evaluate WMS platform architecture (Manhattan, Blue Yonder, SAP EWM, Korber, Infor), inbound receiving and ASN processing, putaway rules and cross-docking logic, picking strategy selection (discrete, batch, zone, wave, cluster.
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。
Analyze an inventory allocation system for demand-driven distribution accuracy, store clustering methodology, safety stock optimization, markdown timing, replenishment logic, and omnichannel inventory visibility..
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。
Analyze behavioral segmentation systems for RFM scoring, cohort tracking, churn propensity, engagement scoring, persona clustering, and journey mapping using behavioral economics frameworks..
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。
Blog strategy development including topic cluster architecture with hub-and-spoke design, audience mapping, competitive landscape analysis, AI citation surface strategy across ChatGPT/Perplexity/AI Overviews, distribution channel planning (YouTube, Reddit, review platforms for AI-citation SEO), content scoring targets, measurement framework, and content differentiation through original research and first-hand experience. Use when user says "blog strategy", "content strategy", "blog positioning", "what should I blog about", "blog topics", "content pillars", "blog ideation".
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。