1.6천건 ・ 연동 도구: Docker · 인기순
Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
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Runs and configures curated nf-core community Nextflow pipelines (rnaseq, sarek, atacseq, methylseq, ampliseq, taxprofiler, fetchngs) reproducibly, pinning the pipeline revision with -r and selecting a container engine and institutional config via -profile. Use when deciding to adopt a community pipeline versus author one from scratch; picking a pipeline and pinning its -r revision; selecting -profile test/docker/singularity/conda plus an institutional config from nf-core/configs; building and validating a samplesheet CSV against the pipeline schema (nf-schema); choosing --genome/iGenomes versus custom references; configuring resources and max_memory for SLURM/AWS Batch; using -resume and -stub; and reading MultiQC outputs.
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Authors bioinformatics pipelines in WDL (Workflow Description Language) run by Cromwell or miniwdl, targeting the GATK/Broad and Terra/AnVIL/BioData Catalyst cloud ecosystem, with tasks, workflows, scatter-gather parallelism, structs, and a runtime block that sizes the cloud VM. Use when deciding to target Terra/AnVIL/GATK/WARP (chosen for the ecosystem, not the language); sizing runtime disks dynamically for a fresh-per-task cloud VM (ceil(size(f)*factor)+buffer); choosing preemptible vs on-demand VMs by task length and idempotency; picking Cromwell (production, cloud, call-caching) vs miniwdl (local dev, miniwdl check linting, readable errors); enabling and debugging call-caching silent-miss modes; pinning Docker by digest for reproducibility and cache stability; or scattering an array for parallel fan-out.
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Stellar standards, ecosystem, and reference. Covers SEPs (Stellar Ecosystem Proposals), CAPs (Core Advancement Proposals), and a quick map for picking the right standard for wallets, anchors, payments, deposits/withdrawals, federation, deep links, and KYC. Also bundles ecosystem references (DeFi protocols, dev tools, wallets, infra, community projects), curated documentation links, and MCP servers (live tools such as Raven). Use when you need to know which SEP applies, or want a starting point for ecosystem integrations, official docs, or live MCP tooling.
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Downloads CARLA so nothing has to be fetched by hand — an official release package for any published version (resolved live from the GitHub releases, with AdditionalMaps), the rolling nightly Dev build, a shallow source checkout of a branch or tag, or the carlasim/carla Docker image. Reports exactly what was obtained and where, plus the environment variables the other skills read. Use when the user asks to "download CARLA", "get CARLA 0.9.16", "install CARLA", "clone the carla repo", "fetch the latest CARLA", or has no CARLA at all yet.
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Adds a new ROS 2 message type to CARLA's native ROS 2 interface — the hand-written POD struct under LibCarla/source/carla/ros2/types/msg, its Fast-CDR serialize/deserialize pair, the RIHS01 type hash computed in Docker, the CdrTopicInfo registration and the type-hash test. Use when the user asks to "add a ROS message type", "support sensor_msgs/X in CARLA", "add a carla_msgs type", "why is my type hash warning appearing", or needs a message CARLA does not publish yet. There is no IDL codegen — every step is manual.
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Inspects and visualises a ROS-2-enabled CARLA server from Docker containers, with no local ROS 2 install — list/echo/rate-check topics (the "are the topics actually on the wire" check), run the bundled map-and-lidar demo stack (hero vehicle with camera/lidar/GNSS/IMU on autopilot), and open RViz2 with the lane-network preset. Use when the user asks to "check the ROS topics", "echo /carla/map", "is CARLA publishing to ROS", "visualise the lidar in RViz", "open rviz", or "run the ROS 2 demo".
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Supabase (オープンソース Firebase 代替・BaaS) リファレンス。 PostgreSQL ベース、Auth (Email / OAuth / Magic Link / Phone / SSO)、Storage、 Edge Functions (Deno)、Realtime (subscriptions / presence / broadcast)、Vector embeddings、 supabase-js、@supabase/server (withSupabase)、supabase CLI (supabase stack)、 Supabase MCP サーバー、RLS (Row Level Security)、migrations。 SQL Server / node-mssql は別スキル mssql。
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Hermes Agent (Nous Research の AI CLI エージェント)。インストール、 CLI コマンド (chat, model, gateway, config, cron, webhook, mcp, skills, profile, hermes pm)、 Configuration (Terminal Backends, Memory, TTS / STT, Auxiliary Models, Compression, Display, Local Models llama.cpp, Egress / Secrets)、 Features (Tools, Skills, Hooks, Delegation, Kanban, Cron, Voice Mode, Personality, Context Files)、 Messaging Gateway (Platform Support, Bot Mode, Connectors)、Security、Architecture、 Hermes Desktop、Plugin Catalog、Profiles、Developer Guide、Nous Portal (Tool Gateway)。
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TanStack Start (TanStack Router ベースのフルスタック React フレームワーク、 @tanstack/react-start) の API リファレンス。Next.js とは別フレームワーク。 createServerFn (validator / handler), createMiddleware, server routes, createServerOnlyFn / createIsomorphicFn、SSR / selective SSR / SPA mode / static prerendering / ISR、streaming、hydration、server entry / client entry、 TanStack Query 連携 (setupRouterSsrQueryIntegration)、RC → stable 移行、 hosting (Cloudflare Workers / Netlify / Vercel / Node / Bun / Docker)、 production checklist、authentication / session、Tailwind、SEO、 environment variables。ルーティングは tanstack-router スキルを参照。
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最適化モデルの運用設計。自動化・監視・フォールバックを定義する
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依存関係更新のローカル差分やPRをレビューする。人間・エージェントによる更新、Dependabot・Renovateの更新PRを対象に、upstreamの変更と利用箇所を照合し、根拠付きの判定を返す。
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Philosophy-first writing partner for Medium with tactical depth. Combines human storytelling values with production-ready technical content tools. Six content modes based on staff pick analysis, mode-specific quality gates, evidence requirements, annotated examples, and comprehensive scripts integration. Designed for sustainable practice, not algorithm gaming.
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Gate a build on browser fingerprint regressions with liarjs - save a baseline scan as JSON, diff later runs against it, and fail the job when the consistency score falls below a floor. Use when asked to add a fingerprint or headless-detection check to GitHub Actions, GitLab CI or another pipeline, to catch a regression in a Chromium build or scraping harness before it ships, or to track how a fingerprint score changes across commits.
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Cut a release of the Lyrion Dashboard. Use this whenever a task is about shipping a new version — bumping the version, tagging, the Release workflow, or how the Android APK and the Docker image get built and published. Covers the single source of truth for the version, the manual `Release` → publish → `android.yml` + `docker.yml` chain, the versionCode packing, and the F-Droid constraint that keeps versions static in source. Reach for it before touching `VERSION`, `release.yml`, or the Android `versionName`/`versionCode`.
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Designs pegRNAs and nicking guides for prime editing (PE) -- choosing the nick/strand, tuning the primer-binding site (PBS) and reverse-transcription template (RTT) as a per-locus panel, selecting the PE system (PE2/PE3/PE3b/PE4/PE5/PEmax/PE7), adding MMR-evading and PAM-disrupting silent edits, appending epegRNA 3' motifs (tevopreQ1/mpknot), and ranking with PRIDICT/DeepPrime. Covers twinPE/PASTE for large insertions and the prime-vs-base-editing decision. Use when designing a scarless point mutation, small insertion/deletion, or any of the 12 base conversions without a double-strand break, when efficiency is low and MMR inhibition or pegRNA stabilization is needed, or when routing a large insertion to an integrase method. Generic guide scoring and base editing are separate skills.
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Estimate SNP heritability and partition it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LDAK enrichment estimates, computing local heritability with HESS, estimating genetic correlation between traits, or producing publication-grade enrichment with calibrated sensitivity to model assumptions.
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Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.
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Designs Bayesian clinical trials including Phase I dose-finding (BOIN, CRM, EWOC, mTPI-2), meta-analytic-predictive (MAP) priors with robust mixtures for external data borrowing, EXNEX for basket trials, hierarchical models for safety AE (Berry-Berry), Bayesian platform trials (I-SPY 2, GBM AGILE, REMAP-CAP), and posterior probability stopping rules. Covers FDA Bayesian Devices Guidance (2010), FDA Bayesian Methodology in Drugs Draft (January 2026), BOIN Fit-for-Purpose qualification (December 2021), and Project Optimus dose-optimisation. Use when designing dose-finding studies, platform trials, or sensitivity analyses with informative priors.
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Estimate bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L. Use when quantifying shared genetic architecture between two traits, screening MR validity before causal inference, distinguishing global from locus-level rg, estimating trans-ancestry rg, separating partial from full causation via LCV gcp, or producing a STROBE-MR-compliant cross-trait sensitivity battery. Cross-trait LDSC intercept absorbs sample overlap and is NOT a bias; HDL is biased under sample overlap above ~5%. High rg between exposure and outcome motivates CHP-aware MR sensitivity (CAUSE, LHC-MR).
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Provides comprehensive testing patterns for Spring Boot applications covering unit, integration, slice, and container-based testing with JUnit 5, Mockito, Testcontainers, and performance optimization. Use when writing tests, @Test methods, @MockBean mocks, or implementing test suites for Spring Boot applications.
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Debug Spring Boot issues systematically. Use when encountering bean errors like NoSuchBeanDefinitionException, circular dependency issues, application startup failures, JPA/Hibernate problems including LazyInitializationException and N+1 queries, security misconfigurations causing 403 Forbidden errors, property binding failures, CSRF token issues, or any Spring Boot application requiring diagnosis with Actuator endpoints and JVM debugging.
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Optimize Bazel builds for large-scale monorepos. Use when configuring Bazel, implementing remote execution, or optimizing build performance for enterprise codebases.
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Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.
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