「defi」の検索結果

Invoke and resume YAML-defined pipelines by name — /pipeline auto-dev runs the full release pipeline

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。

Structured SE task evaluation using 15 benchmark definitions from claude-code-harness research

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。

Judge subskill for simmer. Scores a candidate artifact against user-defined criteria on a 1-10 scale and produces ASI (highest-leverage direction) for the next generator round. Supports judge-only, runnable evaluator, and hybrid evaluation modes. Do not invoke directly — dispatched as a subagent by the simmer orchestrator.

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 splitting a domain Skill into Phase-Aware File Structure (SKILL.md / design.md / constraints.md). Defines the splitting criteria, procedures, sync procedures, and validation rules. Invoked by /skill-format command or manually when restructuring domain 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。

Assigns pathogen lineages (SARS-CoV-2 Pangolin UShER mode; Nextclade clade + QC; pango-designation alias resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage-fitness modelling (multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data and Nextclade dataset version pinning (mandatory; lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme churn (V3/V4/V4.1/V5.3.2/Midnight) with dropout regions, and recombinant X-prefix designation lag. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater into lineage frequencies with Freyja, tracking lineage frequencies over time, handling ARTIC primer dropouts, or running surveillance for SARS-CoV-2/influenza/Mpox/RSV/H5N1/measles.

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。

Unsupervised clustering and cell-type identification for high-dimensional flow, spectral, and mass cytometry - FlowSOM, PhenoGraph, FlowSOM-via-CATALYST, with UMAP/tSNE for visualization. Covers the type-vs-state marker distinction (cluster on lineage, test state within clusters), over-provision-then-metacluster, the Weber-Robinson benchmark, seed dependence and metacluster stability, why embeddings are for looking not measuring, and median-heatmap annotation/merging. Use when discovering populations without predefined gates, choosing a clustering algorithm, selecting the number of metaclusters, or annotating clusters into cell types.

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。

Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML. Covers the canonical gate order (time -> debris -> singlets -> live -> lineage), FMO-vs-isotype boundary setting, gate-order dependence and recompute semantics, rare-event/MRD gating, and per-population statistics. Use when building a gating strategy, automating a manual FlowJo scheme across samples, choosing manual vs data-driven gates, or extracting population frequencies.

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 enhancer-driven gene regulatory networks (eGRNs) by integrating single-cell RNA-seq and ATAC-seq using SCENIC+, CellOracle base GRNs, Pando, FigR, DIRECT-NET, TRIPOD, and scMEGA. Covers the accessibility-defines-enhancers principle, peak-to-gene linking and its cell-composition confound, the paired-vs-unpaired decision, and TF-region-gene eRegulon triplets. Use when analyzing 10x multiome or paired/unpaired scRNA+scATAC to infer cis-regulatory GRNs. For RNA-only regulons see scenic-regulons; for in silico TF perturbation see perturbation-simulation.

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。

Assigns isolate identity at the right resolution for the question -- ANI/Mash species triage, 7-locus MLST historical comparability, cgMLST/wgMLST outbreak resolution (chewBBACA, BIGSdb, Ridom SeqSphere, EnteroBase HierCC), in-silico serotyping (SISTR/SeqSero2 Salmonella, SerotypeFinder E. coli, Kaptive Klebsiella, SeroBA pneumococcus, spa+SCCmec S. aureus), and lineage callers (TB-Profiler/Mykrobe barcode for MTBC, Pangolin + Nextclade for SARS-CoV-2, PopPUNK GPSC for S. pneumoniae). Use when typing bacterial isolates for surveillance or outbreak investigation, choosing between cgMLST allele distance and core-SNP distance for cluster definition, harmonising calls across schemas/database versions, assigning MTBC lineage with the Napier 90-SNP barcode, calling Salmonella serovar via SISTR with monophasic Typhimurium awareness, running Pangolin UShER mode with pangolin-data version pinning, or selecting a typing resolution to match the surveillance question.

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。

Assesses genetic health of populations for conservation with Ne estimation across time horizons (LDNe NeEstimator V2 option-file API + SNeP physical-linkage correction; recent trajectory via GONE/GONE2; deep history via Stairway Plot 2 / dadi / fastsimcoal2 / PSMC), F-statistics, runs of homozygosity binned by length class to date inbreeding, genetic-load decomposition (Bertorelle 2022 realized vs masked), the modern 100/1000 Ne rule (Frankham 2014), Ne/Nc 2-6 orders of magnitude in marine fish (Hauser & Carvalho 2008), tree-sequence forward simulations (SLiM 4 + pyslim + tskit), and the Sukumaran-Knowles caveat against MSC methods for management-unit definition. Use when estimating Ne by time horizon, detecting inbreeding via F_ROH, decomposing genetic load, justifying conservation thresholds, distinguishing ESU/MU/DPS, configuring NeEstimator V2, or correcting LDNe physical linkage.

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。

Select colormaps and qualitative palettes for scientific figures using perceptual-uniformity, color-vision-deficiency safety, and luminance-monotonicity criteria. Covers Crameri scientific colormaps, viridis/cividis/magma, Okabe-Ito categorical, ColorBrewer, and the rainbow/jet critique. Use when choosing palettes for heatmaps, scatter, networks, or any encoding where color carries quantitative or categorical meaning.

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。

Quantify homologous recombination deficiency (HRD) from tumor copy number using the three genomic-scar metrics — loss of heterozygosity (LOH), large-scale state transitions (LST), and telomeric allelic imbalance (TAI) — with scarHRD, and via the whole-genome HRDetect and CHORD models. Covers the genomic instability score, the PARP-inhibitor clinical context, whole-genome-doubling correction, and the scar-versus-state distinction. Use when computing an HRD score for PARP-inhibitor eligibility, deriving LOH/LST/TAI scars from allele-specific copy number, deciding between scar-based and mutational-signature HRD methods, or interpreting an HRD result in a BRCA-reverted or low-purity tumor.

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。

Reads, validates, and prepares CDISC SDTM and ADaM clinical trial data for analysis. Covers SDTM domain joins (DM, AE, EX, VS, LB, DS), ADaM architecture (ADSL, BDS, OCCDS, ADTTE) with traceability, treatment-emergent AE conventions, baseline derivation, SUPPQUAL/NSV handling, Define-XML 2.1, and Pinnacle 21 / CORE validation. Use when working with clinical trial datasets in CDISC SDTM/ADaM format, preparing analysis-ready data, or validating for regulatory submission.

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。

Prepares statistical reports for clinical trials following CONSORT 2025, SPIRIT 2025, ICH E9(R1) estimands, and FDA 2023 covariate adjustment guidance. Covers Table 1 generation, analysis populations (ITT/FAS/PP/Safety), the 5 ICH E9(R1) intercurrent-event strategies, MMRM under MAR (mmrm), reference-based MI (rbmi J2R/CR/CIR), Permutt tipping-point sensitivity, and Rubin's-rules vs frequentist variance debate. Use when preparing regulatory submissions, defining estimands, or implementing missing-data sensitivity analyses.

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。

Annotates ChIP-seq peaks to genomic features, nearest genes, ENCODE candidate cis-regulatory elements (cCREs), and regulatory domains. Uses ChIPseeker (R), HOMER annotatePeaks.pl (CLI), pyranges (Python), GREAT/rGREAT (regulatory domain gene-set enrichment), ChIP-Enrich (locus-length-adjusted), ENCODE SCREEN cCRE classification (PLS/pELS/dELS/CA-CTCF/CA-H3K4me3), and ENCODE-rE2G for cell-type-specific enhancer-gene linking. Handles nearest-TSS vs host-gene ambiguity, promoter window definition, and feature priority. Use when assigning genomic context to peaks, linking enhancer peaks to target genes, classifying peaks against ENCODE cCRE registry, or running gene-set enrichment on peak-associated genes.

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。

This skill recommends obtaining serum free T4 and prolactin levels within 1–2 weeks after transsphenoidal surgery (TSS) for Cushing's syndrome to screen for overt hypopituitarism. Use in postoperative Cushing's syndrome patients during the early recovery window when evaluating for new pituitary hormone deficiencies.

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。

This skill defines the treatment goals for patients with overt Cushing's syndrome, focusing on normalizing cortisol levels or action to eliminate symptoms and treat comorbidities. Use when a clinician asks about treatment objectives for a newly diagnosed overt Cushing's syndrome patient; triggers include "overt CS," "treatment goals for overt Cushing's," and "normalize cortisol in overt CS."

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。

Recommends measurement of plasma ACTH to establish primary adrenal insufficiency (PAI) diagnosis in patients with confirmed cortisol deficiency; a plasma ACTH concentration ≥2-fold the upper limit of the reference range supports PAI. Use when evaluating plasma ACTH in a patient with low morning cortisol or abnormal corticotropin stimulation test.

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。

Screen a child with obesity for red flags suggesting monogenic, syndromic, or secondary endocrine causes — distinguishing atypical from typical exogenous obesity. Trigger when a clinician asks whether obesity could be genetic or hormonal, suspects Prader-Willi syndrome, Cushing disease, hypothyroidism, ROHHAD, Bardet-Biedl syndrome, leptin deficiency, or craniopharyngioma, or when a child has early-onset severe obesity, short stature with obesity, hyperphagia, hypotonia, dysmorphic features, or developmental delay with weight gain.

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。

Draft a Workflow Phase 4 technical spec from an intake (and optionally a BRD + scout + research memo). The spec defines how the system will change: design (C4 + UML + dependency graph in PlantUML), data, APIs, tests, rollout, rollback. Output lives at `docs/specs/<slug>.md`. Never self-approves — approval happens via `/approve-direction`.

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 DDD-style ubiquitous language glossary from the current conversation, flagging ambiguities and proposing canonical terms. Saves to UBIQUITOUS_LANGUAGE.md. Use when user wants to define domain terms, build a glossary, harden terminology, create a ubiquitous language, or mentions "domain model" or "DDD".

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。

Deep-dive framework for analyzing crypto tokens — market data, liquidity health, tokenomics, unlock schedules, and risk assessment. Combines on-chain data, API queries, and multi-source validation to generate actionable investment verdicts.

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。

Multi-container Docker applications with docker-compose — define services, networks, volumes, and orchestrate local development environments

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。

Guides stable API and interface design. Use when designing APIs, module boundaries, or any public interface. Use when creating REST or GraphQL endpoints, defining type contracts between modules, or establishing boundaries between frontend and backend.

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。