「config-driven」の検索結果

Use this skill for internal architecture work around config-driven, registry-driven, and pattern-driven feature design in the nuxt-ui-tools repository. Covers how to organize variant-heavy features, where to put implementations, and how to keep both internals and public APIs standardized.

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Command-line interface for CloudAnalyzer — Agent-friendly harness for CloudAnalyzer, a QA platform for mapping, localization, and perception outputs. Supports 27 commands across 8 groups: point cloud evaluation, trajectory evaluation, ground segmentation QA, config-driven quality gates, baseline evolution, processing, visualization, and interactive REPL.

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。

Migrate a legacy-template SGLang cookbook page (monolithic per-model generator under docs/src/snippets/autoregressive/) onto the config-driven template (shared _deployment.jsx / _playground.jsx engines + per-model config). Use when asked to migrate, convert, or port an existing cookbook page — NOT for brand-new models (use cookbook-add-model for those). Run with /cookbook-migrate-model <Model page name, e.g. GLM-5.1>.

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。

Review a pull request against the SGLang Cookbook (docs/, Mintlify) contribution checklist — the config-driven format (per-model config + benchmarks JSX consumed by the shared _deployment.jsx / _playground.jsx engines). Run with /cookbook-review-pr <PR number>.

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。

Add a new model to the SGLang Cookbook (docs/, Mintlify), config-driven format — instantiate the model-agnostic template into a per-model config (+ benchmarks) JSX under src/snippets/configs/, an MDX page, the docs.json nav entry, NEW-tag hygiene, and the homepage vendor card. Interactive, multi-phase. Run with /cookbook-add-model.

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 the deterministic PIL pill overlays for a 'perfect-score + proof-points' UGC video ad (white 10/10 score header with a medal + orange sub with a finger-down + 3-4 green-check proof pills) and composite them onto a base clip in a diagonal L->R->L->R cascade, then mux the music bed into master-final.mp4. Config-driven (config.json), 1080x1920 9:16, FREE and deterministic (no paid calls, text stays pixel-crisp). Use for the overlay-proof-points format; the base clip + music come from separate paid capabilities.

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。

Calls significant loops from protein-directed and targeted 3C assays (HiChIP, PLAC-seq, Capture Hi-C/PCHi-C, ChIA-PET) where the contact background is peak-anchored and coverage-biased, so generic Hi-C loop callers (cooltools dots, Juicer HiCCUPS) use the wrong null. Covers FitHiChIP (config-driven coverage+distance-decay spline regression, peak-to-peak vs peak-to-all foreground, loose vs stringent background, coverage vs ICE bias), MAPS (positive Poisson regression on bias factors for PLAC-seq/HiChIP), hichipper (restriction-site-distance bias model + library QC), CHiCAGO (Delaporte two-component Brownian+technical background for asymmetric bait x other-end Capture Hi-C), the with/without separate-ChIP anchor decision, and differential loops via diffloop. Use when calling loops from HiChIP/PLAC-seq/Capture Hi-C, choosing FitHiChIP/MAPS/CHiCAGO, picking peak-to-all vs peak-to-peak, setting the loop FDR, supplying ChIP peaks as anchors, QCing a HiChIP library, or comparing loops between conditions.

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 genome-browser-style multi-track figures with pyGenomeTracks (config-driven), Gviz (R), and IGV batch screenshotting. Covers BigWig coverage tracks, BED/peak overlays, gene-model rendering, Hi-C matrix tracks, BedPE link arcs, spike-in-aware normalization, and the bamCoverage --normalizeUsing trap. Use when producing publication figures of genomic loci with stacked aligned tracks (coverage, peaks, genes, interactions) for ChIP-seq, ATAC-seq, RNA-seq, Hi-C, or generic locus visualization.

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。

DeepPavlov package routing for config-driven NLP workflows, model-family selection, and serving.

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。

Routes agents working with Axolotl config-driven LLM fine-tuning, datasets, SFT, preference tuning, GRPO/EBFT, model adapters, distributed training, CLI operations, and troubleshooting.

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 creating or updating the project skill discovery config — generates or regenerates .dh/skill_discovery.yaml by scanning the repo to infer tech stack, inventorying installed skills via npx skills list, loading candidate skill content before suggesting, and writing a config-driven skill injection file. Triggers on /dh:setup-skill-discovery invocations and programmatic --auto calls from add-new-feature Phase 3.

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。

Self-learning SEO content pipeline: dashboard, multi-agent queue, token budgets, research → write → audit → publish. StoryBrand/Cialdini/JTBD-style frameworks; config-driven. Use for content factory, batch articles, or scaled publishing.

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 whenever the user asks to "create a module", "scaffold a feature", "add a Vue domain", "new module called X", or starts work on a brand-new vertical (views + components + store + router entry + tests). Duplicates the canonical `src/modules/tasks` template, applies kebab/Pascal/camel/ UPPER renames, and wires config-driven values. Module stays self-contained.

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 investigating, adding, validating, or debugging external website profiles for the 99idea Playwright browser demo. It teaches how to probe selectors, classify failure modes, add config-driven profiles, and validate both heuristic and Gemini flows.

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 genome-browser-style multi-track figures with pyGenomeTracks (config-driven), Gviz (R), and IGV batch screenshotting. Covers BigWig coverage tracks, BED/peak overlays, gene-model rendering, Hi-C matrix tracks, BedPE link arcs, spike-in-aware normalization, and the bamCoverage --normalizeUsing trap. Use when producing publication figures of genomic loci with stacked aligned tracks (coverage, peaks, genes, interactions) for ChIP-seq, ATAC-seq, RNA-seq, Hi-C, or generic locus visualization.

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。

Erkennt Nutzeraufträge der Form "hole aus einem Ordner immer bestimmte Dateien und sammle sie an einem Ziel" und richtet dafür das Modul file-collect-sort-action (CLI fcsa) ein — ein konfigurationsgetriebener Agent, der Ordner scannt, Dateien per Schablone kategorisiert und gestufte Aktionen anwendet (move/copy/duplicate-check/delete/OCR/place-information). Nutze diesen Skill bei Sätzen wie "hole aus <Ordner> immer <Dateiart> und sammle sie in <Ziel>", "sortiere den Ordner X automatisch nach ...", "sammle eingehende Dateien aus ... in ...", "räume diesen Ordner regelbasiert auf" oder "verschiebe alle <Dateiart> aus <Ordner> automatisch". Richtet IMMER zuerst einen Dry-Run ein und schaltet erst nach ausdrücklichem Nutzer-Go scharf; Löschen bleibt grundsätzlich im _trash-Modus.

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 genome-browser-style multi-track figures with pyGenomeTracks (config-driven), Gviz (R), and IGV batch screenshotting. Covers BigWig coverage tracks, BED/peak overlays, gene-model rendering, Hi-C matrix tracks, BedPE link arcs, spike-in-aware normalization, and the bamCoverage --normalizeUsing trap. Use when producing publication figures of genomic loci with stacked aligned tracks (coverage, peaks, genes, interactions) for ChIP-seq, ATAC-seq, RNA-seq, Hi-C, or generic locus visualization.

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。

Calls significant loops from protein-directed and targeted 3C assays (HiChIP, PLAC-seq, Capture Hi-C/PCHi-C, ChIA-PET) where the contact background is peak-anchored and coverage-biased, so generic Hi-C loop callers (cooltools dots, Juicer HiCCUPS) use the wrong null. Covers FitHiChIP (config-driven coverage+distance-decay spline regression, peak-to-peak vs peak-to-all foreground, loose vs stringent background, coverage vs ICE bias), MAPS (positive Poisson regression on bias factors for PLAC-seq/HiChIP), hichipper (restriction-site-distance bias model + library QC), CHiCAGO (Delaporte two-component Brownian+technical background for asymmetric bait x other-end Capture Hi-C), the with/without separate-ChIP anchor decision, and differential loops via diffloop. Use when calling loops from HiChIP/PLAC-seq/Capture Hi-C, choosing FitHiChIP/MAPS/CHiCAGO, picking peak-to-all vs peak-to-peak, setting the loop FDR, supplying ChIP peaks as anchors, QCing a HiChIP library, or comparing loops between conditions.

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 genome-browser-style multi-track figures with pyGenomeTracks (config-driven), Gviz (R), and IGV batch screenshotting. Covers BigWig coverage tracks, BED/peak overlays, gene-model rendering, Hi-C matrix tracks, BedPE link arcs, spike-in-aware normalization, and the bamCoverage --normalizeUsing trap. Use when producing publication figures of genomic loci with stacked aligned tracks (coverage, peaks, genes, interactions) for ChIP-seq, ATAC-seq, RNA-seq, Hi-C, or generic locus visualization.

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 OpenCode docs when implementing skills, plugins, MCPs, or config-driven behavior.

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。

Calls significant loops from protein-directed and targeted 3C assays (HiChIP, PLAC-seq, Capture Hi-C/PCHi-C, ChIA-PET) where the contact background is peak-anchored and coverage-biased, so generic Hi-C loop callers (cooltools dots, Juicer HiCCUPS) use the wrong null. Covers FitHiChIP (config-driven coverage+distance-decay spline regression, peak-to-peak vs peak-to-all foreground, loose vs stringent background, coverage vs ICE bias), MAPS (positive Poisson regression on bias factors for PLAC-seq/HiChIP), hichipper (restriction-site-distance bias model + library QC), CHiCAGO (Delaporte two-component Brownian+technical background for asymmetric bait x other-end Capture Hi-C), the with/without separate-ChIP anchor decision, and differential loops via diffloop. Use when calling loops from HiChIP/PLAC-seq/Capture Hi-C, choosing FitHiChIP/MAPS/CHiCAGO, picking peak-to-all vs peak-to-peak, setting the loop FDR, supplying ChIP peaks as anchors, QCing a HiChIP library, or comparing loops between conditions.

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 genome-browser-style multi-track figures with pyGenomeTracks (config-driven), Gviz (R), and IGV batch screenshotting. Covers BigWig coverage tracks, BED/peak overlays, gene-model rendering, Hi-C matrix tracks, BedPE link arcs, spike-in-aware normalization, and the bamCoverage --normalizeUsing trap. Use when producing publication figures of genomic loci with stacked aligned tracks (coverage, peaks, genes, interactions) for ChIP-seq, ATAC-seq, RNA-seq, Hi-C, or generic locus visualization.

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。

Score raw leads as HOT/WARM/COOL based on config-driven weights from agency.config.json

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。