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cccskills

「spl」の検索結果

1,793 件 ・ 関連度順

概要と使いどころ

This skill should be used when the user asks about "bun build", "Bun.build", "bundling with Bun", "code splitting", "tree shaking", "minification", "sourcemaps", "bundle optimization", "esbuild alternative", "building for production", "bundling TypeScript", "bundling for browser", "bundling for Node", or JavaScript/TypeScript bundling with Bun.

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。

secondsky/claude-skills2292026年9月28日 更新

Разбивает Epic или крупное требование на независимые User Stories с acceptance criteria в формате Given-When-Then, проверкой по INVEST и оценкой Story Points (Fibonacci или T-shirt). На выходе — Story Map с предложением по Sprint-планированию. User-invoked only — do NOT auto-trigger. Triggers on /pm-user-stories, "разбей на user stories", "разбить эпик", "story map", "AC", "acceptance criteria", "break down into user stories", "split this epic", "write user stories".

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。

serejaris/personal-corp-os2292026年10月8日 更新

Analyze liquid markets with a strict ICT/SMC framework that combines multi-timeframe bias, liquidity sweeps, displacement, close-confirmed MSS/BOS, FVG/OB/Breaker/OTE retracements, premium-discount location, session timing, invalidation, targets, and risk-to-reward. Use for ICT chart reviews, live-market analysis, OHLCV scans, or validating a user's trade thesis. Return an auditable setup status and conditional plan; do not use for generic indicator analysis, order execution, or claims of proven institutional intent.

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。

carreypro/ict-trading-model2242026年10月10日 更新

Break down full ARR (ACL Rolling Review) reviewer responses into structured rebuttal units. Use when input contains ARR reviewer fields (Paper Summary, Strengths, Weaknesses, Comments/Suggestions) and numeric scores (Confidence, Soundness, Excitement, Overall Assessment, Reproducibility). Splits weaknesses and comments/suggestions into granular response items while preserving original wording for quoted issues.

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。

runtsang/RebuttalStudio2182026年4月4日 更新

Break down full NeurIPS reviewer responses into structured rebuttal units. Use when input contains NeurIPS reviewer fields (Summary, Strengths and Weaknesses, Questions, Limitations) and numeric scores (Rating, Confidence, Quality, Clarity, Significance, Originality). Splits questions and limitations into granular response items while preserving original wording for quoted issues.

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。

runtsang/RebuttalStudio2182026年4月4日 更新

Break down full ICLR reviewer responses into structured rebuttal units. Use when input contains reviewer summary/presentation/contribution/strength/weakness/question text and the goal is to split weaknesses/questions into granular R-style response items while preserving original wording for quoted issues.

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。

runtsang/RebuttalStudio2182026年4月4日 更新

Base template for Stage1 reviewer-comment breakdown. Use when creating or extending conference-specific Stage1 skills (for example ICLR, ICML, NeurIPS, ACL) so shared extraction, splitting, and response-mapping logic stays consistent.

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。

runtsang/RebuttalStudio2182026年4月4日 更新

Break down full ICML reviewer responses into structured rebuttal units. Use when input contains reviewer summary/presentation/contribution/strength/weakness/question text and the goal is to split weaknesses/questions into granular R-style response items while preserving original wording for quoted issues.

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。

runtsang/RebuttalStudio2182026年4月4日 更新

Inspect, extract, OCR, create, merge, split, reorder, rotate, annotate, fill, redact, compress, secure, and verify PDF documents while preserving source files and visual fidelity. Use when working with one or more .pdf files; converting documents to or from PDF; extracting text, tables, images, metadata, forms, or page ranges; applying true redactions or signatures; diagnosing malformed, encrypted, scanned, or inaccessible PDFs; or validating that a PDF transformation preserved the intended content and layout.

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。

seb1n/awesome-ai-agent-skills2112026年8月10日 更新

Refresh the data tables in optimal-blog.md from master.db. Checks the data for integrity problems FIRST, then runs the generator that picks per-language winners and splices every GEN-marked table, then reconciles the surrounding prose. Use after new experiment results land, or when the optimal-blog numbers are stale.

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。

adrianco/retort2092026年10月13日 更新

Think and work like an expert Molecular Geneticist. Use when a task calls for Molecular Geneticist judgment. Reasons from sequence-as-hypothesis, reference context (genome build, MANE/RefSeq transcript, HGVS), and allele-level molecular consequence through ACMG/AMP-ClinGen criteria, IGV/VEP/SpliceAI/gnomAD/ClinVar review, MIQE-compliant qPCR/ddPCR, and Sanger/NGS validation while treating allele dropout, pseudogene/paralog misalignment, FFPE deamination, contamination and barcode bleed, and transcript/build mismatch as first-class failure modes.

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。

K-Dense-AI/scientific-agents2082026年10月3日 更新

Think and work like an expert Hydrologist. Use when a task calls for Hydrologist judgment. Reasons from hydrologic-cycle closure (P = ET + Q + ΔS + I), runoff-generation mechanisms, and channel routing through HEC-HMS, HEC-RAS, the National Water Model, and split-sample KGE/NSE/PBIAS calibration, while treating post-flood rating-curve shifts, radar QPE bias, snow/rain misclassification, and non-stationarity in extremes as first-class failure modes.

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。

K-Dense-AI/scientific-agents2082026年10月3日 更新

Think and work like an expert Information Retrieval Scientist. Use when a task calls for Information Retrieval Scientist judgment. Reasons from the Probability Ranking Principle, Saracevic's relevance layers, and Cranfield pooling through tuned BM25, SPLADE/ColBERT/dense and LLM reranking, trec_eval/ir_measures with paired topic-level tests, interleaving, and nugget-based RAG evaluation while treating unjudged-as-nonrelevant pools, position-biased clicks, LLM-judge circularity, single-vector embedding limits, and benchmark contamination as first-class failure modes.

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。

K-Dense-AI/scientific-agents2082026年10月3日 更新

Think and work like an expert Hepatologist. Use when a task calls for Hepatologist judgment. Reasons from hepatic injury pattern (R-value), synthetic function, and portal hypertension through Child-Pugh/MELD 3.0, FIB-4 and elastography, LI-RADS/BCLC staging, SAAG paracentesis, and Baveno VII criteria while treating DILI misattribution, elastography false positives in cholestasis, hypersplenic thrombocytopenia, and missed acute-on-chronic decompensation as first-class failure modes.

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。

K-Dense-AI/scientific-agents2082026年10月3日 更新

Think and work like an expert Computer Graphics Researcher. Use when a task calls for Computer Graphics Researcher judgment. Reasons from the rendering equation, Monte Carlo bias/variance, sampling theory, and BSDF energy conservation through pbrt-v4/Mitsuba 3 references, MIS/ReSTIR/path guiding, DXR 1.2 real-time stacks, 3DGS and NerfBaselines protocols, FLIP/ColorVideoVDP evaluation, OpenUSD/MaterialX/OpenPBR interchange, and ACES 2/OCIO color while treating unconverged references, unequal-time comparisons, denoiser and temporal-reuse bias, scene- vs display-referred color errors, and white-furnace energy failures as first-class failure modes.

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。

K-Dense-AI/scientific-agents2082026年10月3日 更新

Think and work like an expert Distributed Systems Researcher. Use when a task calls for Distributed Systems Researcher judgment. Reasons from failure models, consistency contracts, and tail-latency-and-recovery performance through TLA+ model checking, Jepsen and Porcupine linearizability checking, and YCSB/DeathStarBench benchmarking with iptables and kill -9 fault injection, while treating unbounded leases and split-brain, clock skew under NTP, and GC-induced p99 spikes as first-class failure modes.

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。

K-Dense-AI/scientific-agents2082026年10月3日 更新

Think and work like an expert Connectomics Scientist. Use when a task calls for Connectomics Scientist judgment. Reasons from vEM acquisition and petascale alignment through FFN/RoboEM segmentation, FlyWire/neuPrint/MICrONS/H01 graphs, and synapse-level QC while treating split/merge errors, alignment tears, false synapses, and release-version drift as first-class failure modes.

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。

K-Dense-AI/scientific-agents2082026年10月3日 更新

Think and work like an expert Atmospheric Chemist. Use when a task calls for Atmospheric Chemist judgment. Reasons from coupled photochemical OH-NOx-VOC radical budgets, heterogeneous aerosol uptake, and NOx-limited versus VOC-limited regimes through MCM/F0AM and GEOS-Chem/CMAQ/WRF-Chem models, OH-reactivity closure, PMF on AMS factors, HYSPLIT trajectories, and EKMA isopleths while treating chamber wall losses, instrument cross-sensitivities and inlet artifacts, and emission-inventory error as first-class failure modes.

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。

K-Dense-AI/scientific-agents2082026年10月3日 更新

Think and work like an expert Biogeographer. Use when a task calls for Biogeographer judgment. Reasons from Wallace's ecological vs historical split through GBIF occurrence curation, blockCV spatial cross-validation, MaxEnt/biomod2 SDMs with MESS extrapolation flags, BioGeoBEARS vicariance/dispersal tests, and phylogeographic coalescence while treating random-CV AUC inflation, background bias, and area-cladogram overfitting as first-class failure modes.

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。

K-Dense-AI/scientific-agents2082026年10月3日 更新

Think and work like an expert Astrochemist. Use when a task calls for Astrochemist judgment. Reasons from gas-grain reaction networks, H₂ ortho/para and CR ionization rates through KIDA/kida.uva.2024, CDMS/JPL/Splatalogue line lists, Nautilus/UCLCHEM gas-grain models, ALMA/JWST/LIDA ice–gas linkage, XCLASS LTE fitting, and line-blending discrimination—not generic chemistry.

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。

K-Dense-AI/scientific-agents2082026年10月3日 更新

Revenue decomposition, segment breakdown, geographic revenue split, product-line waterfall, revenue mix analysis, business segment performance, divisional revenue, revenue concentration, customer revenue dependency, channel revenue analysis

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。

agentii-ai/agentii-investment-intelligence2062026年9月29日 更新

Generate persistent, explorable 3D worlds/environments with Marble by World Labs — from text, a single image, multiple images, video, or a 360 panorama, plus coarse-structure blocking with Chisel. Use this skill when a task needs a navigable 3D scene, VR/immersive backdrop, virtual-production environment, previz set, game-blockout, or web/engine-ready Gaussian splat or mesh, and you must choose an input mode, a Marble model, an export format, and a plan/API route, then review world quality. Covers the Marble web app and the World API, editing/expansion, output formats and what each supports downstream, import into Unity/Unreal/Blender/Houdini/web, pricing/credits, capability limits, and rights/licensing. Not for flat text-to-image, text-to-video clips, or real-time on-the-fly world models (Marble outputs are downloadable, static 3D 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。

calesthio/generative-media-skills2032026年7月14日 更新

Plan, troubleshoot, and hand off provider-independent in-camera VFX and LED volume virtual-production work, with Unreal Engine/nDisplay/Live Link awareness. Use for ICVFX suitability, LED stage geometry, frustums, tracking, lens calibration, sync, render nodes, color, lighting/reflections, artifact mitigation, rehearsal, live-composite fallback, monitoring, records, QA, and handoff. Do not use for ordinary offline post compositing, generic storyboards, or full game production.

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。

calesthio/generative-media-skills2032026年7月14日 更新

Use this skill for provider-independent reality capture that turns photographed or video-derived real places, objects, aerial sites, interiors, or people into photogrammetric meshes, NeRFs, or 3D Gaussian splats. It guides representation choice, capture planning, calibration and scale, reconstruction or training, cleanup, compression and LOD, coordinate and interchange handoff, visual/geometric QA, archival records, and property, location, people, and cultural rights checks. Do not use it for text-to-3D generation, synthetic asset invention, guaranteed metrology, or broad downstream finishing after handoff.

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。

calesthio/generative-media-skills2032026年7月14日 更新