「preparation」の検索結果

Small-business bookkeeping skills for converting financial evidence into accountant-reviewable outputs, including setup/context, receipt processing, expense categorization, bank reconciliation, tax preparation, monthly close, jurisdiction-aware tax references, plus specialized skills for home office, vehicle, meals, depreciation, 1099 contractors, and estimated taxes. Trigger on "help me with my bookkeeping", "set up my bookkeeping", "bookkeeping context", "chart of accounts", "process my receipts", "categorize expenses", "reconcile my bank statement", "get my books ready for taxes", "close the month", "organize my finances".

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 end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.

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。

Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.

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。

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

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。

Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.

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。

Orchestrate App Store releases with asc, including staging a version, uploading or building an artifact, publishing, and submitting for review. Use when the user wants to prepare or execute a release. Keep Game Center item preparation in this skill; route other readiness failures, stuck submissions, cancellation, and retry decisions to asc-submission-health.

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 company-specific job application package from a JD, resume.md, cover letter drafts, HR review, and optional company values input. Use when the user wants 지원 패키지, submission checklist, application folder structure, final answer snippets, email draft support, or safe pre-submission preparation without automatic final 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。

Guide for dataset construction — SupervisedDatasetBuilder, RLDatasetBuilder, ChatDatasetBuilder, and custom dataset creation from JSONL, HuggingFace, or conversation data. Use when the user asks about datasets, data loading, data preparation, or custom data formats.

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。

Evaluates code against Apple's App Store Review Guidelines. Use this skill when reviewing iOS, macOS, tvOS, watchOS, or visionOS app code (Swift, Objective-C, React Native, or Expo) to identify potential App Store rejection issues before submission. Triggers on tasks involving app review preparation, compliance checking, or App Store submission readiness.

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。

Builds a structured ransomware incident response playbook aligned with the CISA StopRansomware Guide and NIST Cybersecurity Framework. Covers preparation, detection, containment, eradication, recovery, and post-incident phases with actionable checklists. Activates for requests involving ransomware response planning, CISA compliance, incident response playbook creation, or ransomware preparedness assessment.

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。

Automatically organizes invoices and receipts for tax preparation by reading messy files, extracting key information, renaming them consistently, and sorting them into logical folders. Turns hours of manual bookkeeping into minutes of automated organization.

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。

Meta-analysis / evidence synthesis — pool effect sizes across studies (odds ratios, risk ratios, hazard ratios, mean differences, correlations, GWAS betas) with fixed- or random-effects models, quantify heterogeneity (Q, I², τ²), and build a forest plot. Use when you have results from MULTIPLE studies and need a single pooled estimate, or to synthesize evidence from a systematic review / multiple GWAS / replicated experiments. Handles the error-prone effect-size + standard-error preparation (converting OR/HR/CI, two-group means±SD, proportions, and correlations into the (effect, SE) the pooling step needs).

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。

End-to-end clinical trial analysis workflow from CDISC SDTM/ADaM loading through ICH E9(R1) estimand-driven primary analysis to CONSORT 2025 regulatory-compliant reporting. Covers data preparation, FDA 2023 marginal vs conditional logistic regression, categorical tests with Boschloo, modern HTE/subgroup methods, missing-data sensitivity (MMRM, reference-based MI, Permutt tipping point), graphical multiplicity (Bretz-Maurer), survival analysis (Cox/RMST/competing risks) when applicable, and Table 1. Use when performing a complete analysis of clinical trial data.

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。

End-to-end molecular docking pipeline. Target preparation, pocket detection, protein-ligand docking (DiffDock/Vina), scoring, interaction analysis, and pose ranking.

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。

Help funded SCF teams write tranche submission reports. Use when a team has completed a tranche milestone and needs to document their deliverables for review to unlock the next payment. Covers deliverable documentation, completion evidence, demo preparation, and the specific format tranche reviewers expect.

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。

Plan AI fine-tuning projects with data preparation, training, and evaluation. TRIGGERS - Use when user needs help with ai-fine-tuning-plan related tasks.

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。

Guide AI model fine-tuning with data preparation. TRIGGERS - Use when user needs help with ai-fine-tuning-guide related tasks.

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。

Check the terms of a specific side-work assignment offered as gyomu-itaku or freelance work in Japan, item by item: what the client has actually put in writing about the deliverable, the fee, the payment due date, what counts as out of scope, how many revisions are included, who bears expenses, who owns the result, confidentiality, and how either side can end it. Convert the fee into an effective hourly figure over the hours the work will really take including meetings, revisions and unpaid preparation, and count the days from delivery to the payment due date. Use when someone is offered or is discussing a paid side job, a freelance assignment, spot consulting, or paid trial work and wants to know what is still unwritten before starting; do not use to judge whether the fee is fair or the terms lawful, to decide whether to accept, to draft or sign the contract for the user, or to check whether their employer's rules permit side work, which is sidework-rules-check.

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。

Automatically organizes invoices and receipts for tax preparation by reading messy files, extracting key information, renaming them consistently, and sorting them into logical folders. Turns hours of manual bookkeeping into minutes of automated organization.

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 you receive an IFTA audit notification and need to defend your records. Covers preparation, common arguments, and dispute escalation.

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 your carrier is selected for an IFTA audit. Covers what records to produce, how the audit is conducted, and common findings.

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。

When the user needs to set up multiple academic courses in a learning management system (Canvas/LMS) from structured data sources. This skill automates the entire workflow extracting course schedules from emails/attachments, matching instructors from CSV files, creating courses, enrolling teachers, publishing announcements with class details, uploading syllabi, enabling resource sharing for instructors teaching multiple courses, and publishing all courses. Triggers include course schedule setup, Canvas/LMS administration, academic term preparation, instructor assignment, syllabus distribution, and multi-course management.

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。

Automatically organizes invoices and receipts for tax preparation by reading messy files, extracting key information, renaming them consistently, and sorting them into logical folders. Turns hours of manual bookkeeping into minutes of automated organization.

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。