633 件 ・ 関連度順
概要と使いどころ
QA engineering workflows on top of Playwright. Use when a QA engineer asks to design test cases from a ticket, PRD or acceptance criteria, decide what to automate, write a bug report from a failing test or trace, convert manual test cases (TestRail, Xray, CSV) into Playwright specs with case-ID traceability, or review Playwright test code and pull requests.
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Query ClinicalTrials.gov API v2 for trial data. Search by condition, drug/intervention, location, sponsor, or phase; fetch details by NCT ID; filter by status; paginate; export CSV. For clinical research, patient matching, and trial portfolio analysis.
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Core Python library for astronomy/astrophysics: units with dimensional analysis, celestial coordinate transforms (ICRS/Galactic/AltAz/FK5), FITS I/O, tables (FITS/HDF5/VOTable/CSV), cosmology (Planck18, distance/age), precise time (UTC/TAI/TT/TDB, Julian, barycentric), WCS pixel-world mapping, model fitting. For general tables use pandas/polars; for radio interferometry use CASA.
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。
Out-of-core DataFrame for billion-row data via lazy evaluation and memory-mapped files. Use when data exceeds RAM (10 GB–TB) for fast aggregation, filtering, virtual columns, and visualization without loading. Supports HDF5, Arrow, Parquet, CSV with cloud (S3, GCS, Azure). Built-in ML transformers (scaling, PCA, K-means). In-memory: polars; distributed: 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。
Fast in-memory DataFrame with lazy evaluation, parallel execution, Arrow backend. Use for tabular data in RAM (1–100 GB) when pandas is too slow. Expression API: select, filter, group_by, joins, pivots, window. Lazy mode enables predicate/projection pushdown. Reads CSV, Parquet, JSON, Excel, DBs, cloud. Larger-than-RAM: Dask; GPU: cuDF.
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。
Auto-annotate plasmids with features (promoters, terminators, resistance, origins, tags, fluorescent proteins) via BLAST against curated DBs (Addgene, fpbase, SnapGene). FASTA or raw sequence in; annotated GenBank, interactive HTML maps, CSV tables out. Handles circular topology. Use to verify synthetic constructs, prep Addgene submissions, share maps, or batch-annotate cloning libraries.
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。
Parse/write FCS (Flow Cytometry) files v2.0-3.1. Events as NumPy, channel metadata, multi-dataset files, CSV/FCS export. Use FlowKit for gating/compensation.
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。
Reconstruct evidence-based work descriptions and append user-confirmed durations to TIME.csv. Use for time logging and corrections; never infer hours or billing from activity.
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。
Maintain the canonical TASKS.csv with permanent IDs, provenance and lifecycle history; list, add, update, complete, cancel or import selected actions.
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。
Clean a local FF&E CSV schedule by normalizing casing, dimensions, units, language, materials, and formatting. Use when asked to clean, fix, or standardize product 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。
Find and compare current FF&E candidates from a design brief, with sourced purchasing facts and optional CSV-library save. Use to research or source products; not to match from an image or pair coordinating items.
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。
Maintain an append-only project INVOICES.csv ledger from explicit billing facts and recorded lifecycle evidence; never infer charges or payment from activity or time logs.
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 running throughput/latency (serving speed) benchmarks against an already-running model server. Uses sgl_jax.bench_serving to sweep batch-size/concurrency and measure throughput and latency, producing summary.csv and per-point bs_<N>/result.jsonl. Speed only — accuracy/eval belongs to model-accuracy-benchmark. Triggers on throughput, latency, bench_serving, batch-size sweep, tok/s, TTFT, TPOT — not accuracy/eval.
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。
Provides parameterized testing patterns with JUnit 5, generates data-driven unit tests using @ParameterizedTest, @ValueSource, @CsvSource, @MethodSource. Creates tests that run the same logic with multiple input values. Use when writing data-driven Java tests, multiple test cases from single method, or boundary value 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。
Extract structured data from construction PDFs. Convert specifications, BOMs, schedules, and reports from PDF to Excel/CSV/JSON. Use OCR for scanned documents and pdfplumber for native PDFs.
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。
Load and parse DDC CWICR construction cost database from multiple formats: Parquet, Excel, CSV, Qdrant snapshots. Foundation for all CWICR operations.
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。
Generate realistic synthetic data using Spark + Faker (strongly recommended). Supports serverless execution, multiple output formats (Parquet/JSON/CSV/Delta), and scales from thousands to millions of rows. For small datasets (<10K rows), can optionally generate locally and upload to volumes. Use when user mentions 'synthetic data', 'test data', 'generate data', 'demo dataset', 'Faker', or 'sample 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。
Generates markdown digests and CSV exports for GitHub initiative health. Use when reporting on issue/PR progress across a milestone or project.
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。
Transform raw data from CSVs, Google Sheets, or databases into executive-ready reports with visualizations, key metrics, trend analysis, and actionable recommendations. Creates data-driven narratives for leadership. Use when users need to turn spreadsheets into executive summaries or board 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。
Cleans up CRM data from HubSpot, Salesforce, Pipedrive, Zoho, Close, Attio, or any CRM CSV export. Finds duplicate contacts and companies with fuzzy matching (not just exact email), normalizes emails, phones to E.164, names, domains, states, and countries, flags stale, invalid, role, and test records, finds broken contact-company associations, and produces a reviewable merge plan plus import-ready files without touching the live CRM. Use this skill whenever the user wants to dedupe, find or merge duplicate contacts, merge companies or accounts, clean up a CRM, prep a CRM migration or re-import, or says the CRM export is a mess. Also use it when they mention only one symptom, like "the same person is in here three times", "half our leads are junk", or "our company records don't match".
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 a tabular dataset (CSV, Excel, Parquet, Stata, SAS) needs a data dictionary. Profiles every variable (type, levels, range, missingness) into codebook.md and codebook.json and flags coded values of unknown meaning as [NEEDS DICTIONARY] instead of guessing.
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 a clinical CSV/Excel dataset needs profiling and cleaning before analysis (missing values, outliers, duplicates, type mismatches). Profiles, flags and generates cleaning code in three stages, each gated on the researcher's approval. Never auto-cleans.
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 ColabFold for protein structure prediction workflows: validate FASTA/CSV/A3M inputs, generate MSAs, plan batch predictions, inspect outputs, and troubleshoot optional AlphaFold, MMseqs2, JAX, GPU, and OpenMM dependencies.
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 trail register: timestamp, user, module, record, action, field changed, old and new value, and the reason for the change. Use for change history and control evidence.
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。