「case」の検索結果

Interact with the HoneyComb agent marketplace. Browse agents, skills, and tools. Submit use cases for job tickers. Post and vote on comments. Purchase and download agent archives. Check automation scores. Use when exploring the marketplace, contributing content, or acquiring 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。

Scan, structure, validate, and submit your project to the Next Level Builders Directory (https://nextlevelbuilder.io) with 17 rich blocks, cryptographic content hashing, Polar payment handling, and automated moderation queueing.

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。

Estimates time-scaled phylogenies, molecular clock rates, effective reproduction number R_e (or R_t), and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict / uncorrelated lognormal / ORC clocks; constant / exponential / Bayesian Skyline / Skygrid / BICEPS / Birth-Death-Skyline / sampled-ancestor BDSKY priors; structured coalescent via MASCOT). Covers root-to-tip clock signal QC via TempEst, date-randomisation tests (Ramsden 2009; Duchêne 2015), recombination masking via Gubbins and ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction (Volz & Frost 2014; preferential-sampling extensions), MASCOT structured coalescent for migration, BICEPS-vs-BSP skyline choice, multi-chain BEAST2 convergence diagnostics, and reconciliation between phylodynamic R_e and case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline (Stadler 2013) models, diagnosing temporal-signal failure, masking recombination before clock inference for *Streptococcus pneumoniae* / *Neisseria gonorrhoeae* / *Klebsiella* / *E. coli* phylodynamics, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.

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。

Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 with `bam_ip` + `bam_input` (control arm) and `bam_treated_ip` + `bam_treated_input` (treatment arm) for integrated GC-bias-aware differential calling (Liu 2022 *NAR Genom Bioinform* 4:lqac046), QNB beta-binomial test (Liu 2017 *BMC Bioinformatics* 18:387), MeTDiff HMM-based differential bundled with MeTPeak, RADAR (Zhang 2019 *Genome Biol* 20:294) with its `filterBins -> diffIP -> reportResult` workflow, and the defensible paired-symmetric case of edgeR / DESeq2 on featureCounts-on-peaks matrices when batch / lot covariates need explicit fixed-effect handling (exomePeak2's top-level API does NOT accept arbitrary covariates). Covers paired vs unpaired vs interaction designs, batch confounding (antibody lot, RNA prep, sequencing run) and the per-lot meta-analysis strategy when exomePeak2 is the primary caller, the stoichiometry-vs-expression-vs-IP-efficiency confound that all MeRIP differential methods inherit, normalisation choice (size factor on IP, on input, on per-sample IP/input ratio), the McIntyre 2020 reproducibility caveat, effect-size filtering as a guardrail against under-powered N=2 designs, and orthogonal-validation routes for absolute stoichiometry (GLORI / SAC-seq / m6Anet `mod_ratio`). Use when comparing m6A levels across two or more conditions, choosing between exomePeak2 / QNB / RADAR / MeTDiff for a given design, handling batch confounding when exomePeak2's API is too rigid, normalising against input properly, distinguishing real hyper- / hypo-methylation from expression-level shifts, applying effect-size thresholds, interpreting volcano plots of differential peaks, or planning a follow-up orthogonal stoichiometry validation.

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 with the GATK best-practices workflows — the somatic CNV pipeline (CollectReadCounts, DenoiseReadCounts with tangent normalization, ModelSegments, CallCopyRatioSegments) and the germline GATK-gCNV pipeline (DetermineGermlineContigPloidy, GermlineCNVCaller cohort/case mode, PostprocessGermlineCNVCalls). Covers panel-of-normals construction, AnnotateIntervals/FilterIntervals, allelic-count integration, and QS-based filtering. Use when integrating CNV calling into a GATK variant pipeline, calling rare germline CNVs from an exome cohort, deciding between the somatic and germline GATK workflows, or diagnosing why tangent normalization removed a real event or why gCNV output has low precision.

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。

Classify constitutional (germline) copy number variants for clinical reporting using the 2019 ACMG/ClinGen technical standards points-based framework, with ClassifyCNV and AnnotSV for semi-automated scoring. Covers the separate copy-number-loss and copy-number-gain rubrics, the five-tier classification, ClinGen haploinsufficiency/triplosensitivity and dosage-sensitive regions, de novo and segregation evidence, and population-frequency benign evidence. Use when assigning pathogenic/likely-pathogenic/VUS/likely-benign/benign to a constitutional CNV, scoring a CNV against ACMG/ClinGen criteria, or distinguishing the automatable evidence from the case-specific evidence requiring manual input.

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。

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.

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 sashimi-style plots showing RNA-seq read coverage and splice junction counts using ggsashimi (general-purpose, condition-grouped overlays), rmats2sashimiplot (rMATS-output-aware), MAJIQ-VOILA (LSV posteriors interactive HTML), leafviz (leafcutter clusters Shiny), Jutils (tool-agnostic heatmaps and sashimi for rMATS/leafcutter/SUPPA2/MAJIQ output), or pyGenomeTracks (multi-track publication figures). Tool choice depends on the upstream differential-splicing tool's output format and the publication vs interactive use case. Use when visualizing specific splicing events, validating differential splicing calls, or producing publication-quality figures.

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。

Track and report progress of a request to create and run an EcoSIM case for a site ("create an ecosim run for site US-UMB", "set up and run EcoSIM at <site>"). Records four stages — data preparation, namelist creation, model simulation, and output evaluation — in result/<SITE_ID>/progress.json and PROGRESS.md, derives step status from files and quality reports on disk, and records manual results such as the executable, test run, and evaluation verdicts. Use at the start of such a request, after each stage, and whenever the user asks where a site run stands.

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。

Generate a structured test plan for a feature or module — covers unit, integration, contract, and edge cases without writing implementation code.

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。

Generate a structured test plan for a feature or module — covers unit, integration, contract, and edge cases without writing any implementation code.

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。

Sync the current machine's configuration into the dotfiles repo and update the React showcase page. Use this skill whenever the user says "sync dotfiles", "update dotfiles", "sync config", "actualizar dotfiles", "sincronizar config", "update the repo with my current setup", or anything about pulling their current machine state into the dotfiles repo. Also trigger when the user mentions updating the toolkit page, adding new tools to the page, syncing Claude Code or Codex config (AGENTS.md, codex agents/skills/hooks), or keeping the dotfiles repo in sync with what's actually installed.

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。

Create a new investment thesis note with full analysis. Use when user says "create thesis", "new thesis", or "build a case for TICKER"

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。

Tag a thesis's implicit macro bets — the macro variables its bull case is secretly levered to (AI capex, rates, FX, China decoupling, memory/HBM cycle, energy, regulation) with direction and magnitude. Use when user says "macro exposure", "what macro is [TICKER] betting on", "macro bets", or "rate exposure for [TICKER]".

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。

Extract a thesis's dependency fingerprint — the external variables its bull case actually rests on (key customers, suppliers/inputs, technology transitions, macro drivers, single points of failure). Use when user says "dependency map", "what does [TICKER] depend on", "dependency fingerprint", or "map exposures for [TICKER]".

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。

Create EcoSIM Fortran namelist files and workable EcoSIM run folders for AmeriFlux site runs from site-specific grid, plant management, climate forcing, and greenhouse-gas inputs. Use when preparing or updating an EcoSIM `.namelist`, run folder, runfolder, run directory, or executable-ready site case for AmeriFlux or FLUXNET sites, especially after generating `*_ecosim_grid.nc`, `*_ecosim_climate_<first>-<last>.nc`, and `*_pft_mgmt.nc` inputs.

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。

Extract study sites, observational variables, coordinates, climate-forcing sources, and cited data sources from ecosystem-modeling papers, PDFs, supplements, or article text, then plan or create EcoSIM simulation workflows and run folders for those sites. Use when Codex is asked to read a paper for EcoSIM feasibility, including requests phrased as "read paper for ecosim feasibility"; read a paper for sites/data availability; reproduce a published site simulation in EcoSIM; compare EcoSIM outputs to paper observations; or convert paper-derived AmeriFlux, FLUXNET, US NLDAS-domain, non-US ERA5, crop, peatland, forest, grassland, wetland, or unmanaged field-site information into EcoSIM inputs and runnable cases.

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。

Generate a single-file product landing page with a layered ASCII cosmos hero (rotating ASCII planet, drifting irregular asteroids, comet trails, nebula gas clouds — each in its own harmonious color) plus full marketing sections (about / features grid / showcase / spec / pricing / CTA / footer). Takes a natural-language description of one product (any kind — a note app, a coffee subscription, a CLI tool, an indie hardware, a hosting service…) and outputs ONE working .html file with deep-space dark or warm-letterpress theme. Runs in BOTH Claude Code (writes to ~/Desktop) and GLM / online chat (outputs HTML inline). Trigger phrases: "做个宇宙风产品落地页", "生成 ASCII 产品官网", "ascii-cosmos-gen ...", "做个独立软件 / 独立硬件落地页", or when user drags this skill folder into chat asking for an atmospheric product landing page.

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 premium scroll-driven web pages and product showcases inspired by high-end app, hardware, and product interaction patterns. Use for Raycast-like, Apple-like, NVIDIA-like, RTX/GPU, SaaS, AI browser, parallax, sticky scroll, pinned reveal, Three.js GLB model, Web Audio sound effect, responsive accessibility, and performance-safe frontend requests.

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。

Premium "digital e-guide" template family — two patterns sharing a metadata-rich, editorial-archive identity: an infinite 3D-canvas portfolio (case/) for designer 作品集, and a 12-slide horizontal field-notes carousel (social-carousel.html) for long-form knowledge series.

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。

Summarize existing QA criteria, cases, evidence, defects, limitations, authorization, and verdict without mutation.

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。

Transforma uma peça de conteúdo em várias — case em thread, webinar em posts, artigo em carrossel, vídeo em cortes. Use quando o usuário disser "reaproveitar", "transformar isso em", "adaptar para", "repurpose", "derivar conteúdo", "aproveitar melhor esse material", "do blog para as redes", ou apresentar material longo pedindo derivados. Produz peça nativa de cada canal, nunca copia e cola.

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。

Estrutura narrativa para copy — PAS, BAB, AIDA, PASTOR, Jornada do Herói e Golden Circle. Use quando o usuário disser "storytelling", "contar a história", "narrativa da marca", "o texto está sem fio condutor", "case de sucesso", ou quando a página precisar de narrativa em vez de argumento listado.

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。

Onboarding tour with AdwCarousel and navigation dots, skippable welcome screens. Use when adding a first-run tour or feature showcase to a GNOME app.

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。