「import」の検索結果

Lesson-plan generation and compliance-check desk (Busabase App-in-Skill) for a dean of studies or training-program lead. Use when the user invokes $kelly-lesson or /kelly-lesson, mentions lesson plan, 备课, 教案, teaching plan compliance, curriculum template, dean of studies, teacher training, wants lesson plans drafted from curriculum materials and the school template, teacher drafts imported and checked against quality standards, a review queue for approving plans, or a library of approved plans exported as documents.

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。

Personal finance anomaly review desk (Busabase App-in-Skill) that imports the three business tables — orders, invoices, payments/receipts (回款) — from CSV/JSON exports, flags anomalies with deterministic rules, and runs a human review queue with auditable decisions. Use when the user invokes $kelly-audit or /kelly-audit, mentions finance audit, order invoice payment reconciliation, 对账, 应收, 回款, 发票异常, receivables aging, overdue receivables, duplicate payments, missing invoices, amount mismatches, or anomaly review.

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。

Busabase-backed household pooled-pension / elder-care fund App-in-Skill (家庭统筹基金) that keeps one read-only bookkeeping dashboard where transparency equals fairness. Use when the user invokes $kelly-family-fund or /kelly-family-fund, wants to pool two elders' pensions (退休金) managed by one steward (统筹人), pay a fixed care cost (养老院 / nursing home), and share the surplus across sibling families (交通/聚餐/生日礼物/人情/social gifts), track a household fund balance and monthly income/expense/net, see per-family cumulative benefit, share %, and fairness deviation (公平), a per-category spend split, CSV import or manual ledger entry (记账). It reads Busabase only and NEVER moves money.

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。

Busabase-backed family-office App-in-Skill that consolidates multi-entity investment holdings into one read-only dashboard. Use when the user invokes $kelly-family-office or /kelly-family-office, wants a consolidated family office view, multi-entity aggregation across an individual, trust, company, fund, or foundation, total AUM in a base currency, asset allocation by asset class, by entity/member, or by custodian/institution, multi-currency FX consolidation, CSV import of holdings, unrealized P/L, or read-only portfolio monitoring. It reads Busabase only and NEVER moves money or trades.

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 invoices, receipts, credit notes, statements, PDFs, images, docs, and spreadsheet-like invoice exports into a reviewable table with field confidence, line items, approval decisions, and CSV/JSON export. Use when the user invokes /kelly-invoice-sheet or $kelly-invoice-sheet, asks for "Invoice转表格", invoice OCR, receipt-to-spreadsheet, invoice data extraction, bookkeeping import prep, or a Lido-style Extract Data workflow with a Busabase-backed App-in-Skill UI.

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 the user through connecting a new data warehouse source — Postgres, MySQL, Stripe, Hubspot, MongoDB, Salesforce, BigQuery, Snowflake, and so on. Use when the user wants to "connect Stripe", "import data from Postgres", "add a new data source", "sync my warehouse tables", or wants to pick sync methods for each table. Walks through source-type discovery, credential validation, table discovery, per-table sync_type selection, and the final create call. Also covers picking a good prefix and what to do right after creation.

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Computes sample size and power for clinical trials including continuous, binary, and time-to-event endpoints; superiority, non-inferiority, and equivalence designs; FDA 2016 non-inferiority margin selection with M1/M2 framework; Schoenfeld 1981 and Lakatos 1988 for survival; Schuirmann TOST and 80-125% bioequivalence; minimum clinically important difference (MCID) vs δ distinction. Use when justifying trial size in protocol or SAP per CONSORT 2025 item 7.

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。

Explains ML predictions on omics data with SHAP, LIME, and permutation importance, handling the correlated-feature trap, the conditional-vs-interventional Shapley choice, and the attribution-is-not-causation boundary. Use when interpreting an omics classifier, debugging shortcut/batch learning, or deciding whether an attribution ranking can be trusted as biology. For validated feature selection see machine-learning/biomarker-discovery; explanations are not a selection method.

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 proteomics workflow from MaxQuant output to differential protein abundance. Orchestrates data import, normalization, imputation, and statistical testing with limma (default) or MSstats for complex feature-level designs. Use when processing mass spectrometry proteomics.

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。

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box 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。

Runs the MS-DIAL preprocessing workflow (peak picking, MS2Dec spectral deconvolution, alignment, gap-filling) and imports the alignment-result table into R or Python with honest filtering. Use when preprocessing LC-MS DDA/DIA (SWATH) raw data with MS-DIAL, deciding MS-DIAL vs XCMS, configuring the MsdialConsoleApp console run, or parsing an MS-DIAL export into a clean feature matrix. For programmatic R peak detection and the feature-table-as-artifact framing see metabolomics/xcms-preprocessing; for lipid annotation mode see metabolomics/lipidomics; for MSI-level confidence honesty see metabolomics/metabolite-annotation; for drift correction and QC see metabolomics/normalization-qc.

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 QSAR / QSPR models using chemprop D-MPNN, MolFormer, Uni-Mol, ChemBERTa, random forest baselines, and Gaussian processes with explicit handling of OECD 5 principles, applicability domain (kNN, leverage, conformal prediction, Mahalanobis), scaffold-balanced splits, ensemble uncertainty, calibration (Platt, isotonic), feature importance (SHAP, atomic attribution), and prospective validation. Use when building target-specific predictive models from in-house bioassay data, ADMET endpoints, or selectivity profiles.

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 supplier invoice details structured for direct import into ERP systems like SAP, Oracle, or Microsoft Dynamics.

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 every product from a supplier catalog PDF — SKUs, names, prices, MOQs — and generate a ready-to-import XLSX in two API calls.

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 HS codes, declared values, and duty amounts from customs declaration documents, then generate an XLSX import duty log.

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。

Installs the CARLA Autonomous Driving Leaderboard (leaderboard.carla.org) at a chosen version — 1.0, 2.0 or 2.1 — together with the exact scenario_runner branch and CARLA build each one requires, then wires the four-root PYTHONPATH and verifies the whole stack imports. Detects which version an existing checkout is by reading its code, not its branch name. Use when the user asks to "install the leaderboard", "set up the CARLA challenge", "evaluate my driving agent", "reproduce my leaderboard score", or names a leaderboard version.

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。

Visualises and validates a map's pedestrian navigation mesh by sampling walkable locations (get_random_location_from_navigation) and drawing them as debug points. Use when the user asks to "show/visualise the pedestrian navmesh", "check walkable areas", "validate pedestrian navigation after a map import", or "why won't my walkers move". Confirms the navmesh loaded and how far it covers.

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。

Cooks and packages CarlaUE4 into distributable tarballs under Dist/ — either the full simulator or a standalone asset package of selected maps and props for later import. ROS2=1 keeps the native ROS 2 interface in the cooked package (a plain cook silently drops it). Use when the user asks to "package CARLA", "make a Dist build", "cook the project", "export a map as a package", "package CARLA with ROS2", or needs a build with working camera and lidar sensors.

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。

Packages an agent for CARLA Leaderboard submission — builds the docker image with make_docker.sh (plain or ROS variant), checks the four required roots and the egg layout it assumes, sets the agent/track/routes environment inside the image, and verifies the image can import the stack and load the agent before submission. Use when the user asks to "build the leaderboard docker image", "submit to the leaderboard", "package my agent", or make_docker.sh fails.

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。

Installs Scenic, the probabilistic scenario description language, for the CARLA in use — pip installing it into the same interpreter that holds the CARLA client, optionally cloning the upstream repo for the example scenarios the wheel does not ship, and verifying that the `scenic` CLI, the client and Scenic's version-keyed blueprint table all agree. Use when the user asks to "install Scenic", "set up Scenic", "download Scenic", "pip install scenic", wants to run `.scenic` files, or has a Scenic whose model import or blueprint lookup fails.

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。

Stop AI agents from secretly bypassing your rules. Mechanical enforcement with git hooks, secret detection, deployment verification, and import registries. Born from real production incidents: server crashes, token leaks, code rewrites. Works with Claude Code, Clawdbot, Cursor. Install once, enforce forever.

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。

SQLite-based ticket tracking for structured development workflows. Use this skill when the user asks to initialize a ticket database, import requirements, list tickets, get the next ticket to work on, mark tickets as done, or check ticket status. Essential for managing epics, user stories, and acceptance criteria.

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。

Bidirectional conversion between SpecWeave increments and Azure DevOps work items. Use when exporting increments to ADO epics, importing ADO epics as increments, or resolving sync conflicts. Handles Epic/Feature/User Story/Task hierarchy mapping.

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。

Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape. Activate on "find expungement data", "scrape state laws", "legal research", "court URLs", "statute sources", "Clean Slate laws", "automatic expungement research". NOT for interpreting laws (use national-expungement-expert), building UI, or legal advice.

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。