「datasets」の検索結果

Use when verifying information (fact, number, quote, event, statement) against authoritative primary sources, or cross-checking a number via one-level metric decomposition (Z = P × Q). Triggers: "verify X", "is this true", "find the original source", "where is this number from", "two sources disagree", "is it true X never did Y". Covers five scenarios: (1) basic truthfulness check, (2) completeness / out-of-context quoting, (3) one-level reasoning verification, (4) negative-statement handling, (5) multi-source conflict side-by-side output. Dig into whitelisted primary sources only (user-supplied files, official websites & databases, authoritative industry sources); cited reports / charts / datasets must be downloaded and read locally to count as verified — if download is blocked, hand the link to the user. If nothing can be found, plainly state "cannot verify" rather than guessing, patching, or citing secondary paraphrases. Always reply in the user's question language.

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。

Roll out governed MotherDuck analytics to internal teams, choosing trusted datasets, access boundaries, and owners.

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。

Run SQL against Google BigQuery and browse its catalog — submit queries (sync or async), poll job status, page through results, list datasets/tables, and read table schemas. Use this whenever the user wants to query a BigQuery table, ask "what's in this dataset", check a BigQuery job's status, or mentions bigquery.googleapis.com or a `project.dataset.table` path. Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.

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 Kiln to create eval datasets, rate outputs, optimize prompts and models, and ship the same AI task or agent definition from a local workbench into production code.

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 MemoryBench to run repeatable conversational memory and RAG benchmarks across providers, datasets, judge models, checkpoints, and structured reports.

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。

Respan for LLM observability. Use to set up tracing or the gateway, manage prompts (create/version/deploy), run evals, experiments, and datasets, or work with traces and logs. Trigger for any Respan platform task or when instrumenting an app to send telemetry to Respan.

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。

Specific optimization strategies for Python scripts working with larger-than-memory datasets via Dask.

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。

Hugging Face Hub operations, model inference, dataset management, PEFT/LoRA fine-tuning, and Spaces deployment via MCP tools and Python APIs

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 near the end of EZ_math_model intake when external/user-corpus contains user-provided papers, notes, PDFs, datasets, or examples that should become a local AGENTS.md reference index.

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 an OCR/HTR engine by benchmarking candidates on the same labelled samples — source ground truth from open datasets (e.g. Hugging Face PAGE-XML corpora) or hand-transcription, wrap every engine behind a transcribe(image_path) -> str adapter (vision-LLMs via any OpenAI-compatible endpoint, dedicated HTR REST APIs, cloud OCR), and score with zero-dependency raw CER, lenient CER, WER, and a critical name/date token diff. Use when choosing an engine for handwriting or historical-document transcription, when published accuracy claims need honest verification on your own material, or when a records workflow must surface confident-but-wrong names and dates that a low aggregate CER hides.

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。

PHPUnit and Pest patterns for any PHP project: test structure, naming, data providers, test doubles (mocks/stubs/fakes), fixtures, and coverage targets. Stack-agnostic — referenced by every PHP plugin in the marketplace. Use this skill to: - Structure unit and integration tests with PHPUnit or Pest consistently. - Drive cases with data providers / datasets instead of copy-pasted test bodies. - Use test doubles with discipline — mock collaborators, not the system under test. - Set up and tear down fixtures cleanly and aim for a meaningful coverage target. Do NOT use this skill for: - PHP language idioms — see php-foundation:php-conventions. - Composer / autoloading — see php-foundation:composer-tooling. - Framework-specific test helpers (Laravel RefreshDatabase/actingAs, Symfony WebTestCase/KernelTestCase) — those are injected by the framework plugin's QA phase.

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 Therapeutics Data Commons through the PyTDC Python package for registry discovery, approved dataset access, task-aware splits (scaffold, cold-start, temporal, combination), evaluator metrics, benchmark groups, and bounded molecular-oracle workflows. Use this skill to find which TDC datasets exist for a therapeutic task, load them with a split that does not leak, score predictions with the task's own official metric rather than a generic one, and run benchmark groups reproducibly. Also trigger on PyTDC, Therapeutics Data Commons, tdc.single_pred, tdc.multi_pred, ADMET Benchmark Group, scaffold split, get_split, or molecular oracles such as GSK3B, JNK3 and DRD2.

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。

Query the ChEMBL database web services for measured bioactivity data, compound records and calculated properties, targets, assays, mechanisms of action, drug indications and warnings. Use this skill to build curated SAR or QSAR datasets for a target, look compounds up by SMILES, InChIKey, name, or ChEMBL id, run similarity and substructure searches, and check what chemistry is already known against a protein. Also trigger when a query mentions ChEMBL ids (CHEMBL...), pChEMBL values, IC50/Ki/Kd/EC50 retrieval, assay confidence scores, or ebi.ac.uk/chembl.

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。

Analyze datasets and extract insights. Use when user needs to understand data patterns, statistics, or trends.

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 system for creating supervised fine-tuning datasets from books and training style-transfer models. Covers text extraction, intelligent segmentation, synthetic instruction generation, Tinker-compatible output, LoRA training, and validation.

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。

Ascend cluster comparison tool. Invoke when user asks to compare two cluster datasets (DB or TEXT), or to generate cluster_analysis_output from raw profiling data via msprof-analyze and then compare.

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。

Compares two Prof datasets (GPU vs NPU / NPU vs NPU) by auto-installing and calling msprof-analyze compare, then parses the result xlsx into HTML report and Chinese xlsx. Invoke when user provides two prof dirs to compare, or has a compare result xlsx to analyze/translate.

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。

Curate and prepare datasets for AI training. TRIGGERS - Use when user needs help with ai-dataset-curator 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。

Use when the user wants a complete SPSS or SPSS-MCP empirical research workflow: organize source data, prepare datasets, design variables and models, run SPSS analyses, export Chinese result paragraphs and tables, write a Chinese LaTeX paper, and compile the final PDF.

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。

"Apply Benford's Law to detect anomalies in numerical datasets by analyzing first-digit frequency distributions. Use this skill when the user needs to audit financial data for fraud indicators, validate data integrity, or detect fabricated numbers — even if they say 'data manipulation detection', 'first digit test', or 'accounting fraud screening'.".

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 preparing or publishing a local project as a clean GitHub repository. Covers new repositories, private staging releases, public README cleanup, updates to existing remotes, and research-code packaging without datasets, checkpoints, papers, or generated artifacts.

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。

Audit data quality across six DAMA DMBOK dimensions — Completeness, Accuracy, Consistency, Timeliness, Validity, Uniqueness. Produces a scored data quality report with issue log and remediation plan. Use when profiling datasets, onboarding new data sources, or building data quality gates.

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 sensitive training data (PII, API keys, intellectual property, or code) directly from a deployed Large Language Model (LLM). This AI Red Teaming skill focuses on forcing models to regurgitate memorized, unredacted data from their massive internet-scraped datasets through repetition attacks, prefix continuation, and context window manipulation.

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。

Identify and exploit vulnerabilities in the AI Supply Chain by injecting malicious models, datasets, or dependencies. Use this skill to simulate advanced persistent threats (APTs) compromising Hugging Face repositories, manipulating pre-trained weights (Model Poisoning), and exploiting insecure deserialization during model loading (e.g., Pickle files).

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。