1.3万 件 ・ 関連度順
概要と使いどころ
指导外部自动化 agent 使用 QwenPaw Data CLI 发现 Data Bridge 数据源、通过自然语言规划与执行数据分析任务、复用 SOP YAML，并根据流式输出、执行摘要和错误信息验收结果。用于需要调用本地 `qwenpaw-data` 命令完成数据任务、选择 `run` 或 `plan`/`execute`、传入 `--datasource-id`、处理长任务输出或排查 CLI 失败的场景。
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 any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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 adding or changing tuning values, items, enemies, abilities or levels - put them in the engine's data assets (ScriptableObject, Resource, DataAsset/DataTable, JSON/RON) with validation and stable IDs, keep runtime state out of shared data, inject services instead of singletons, and test data invariants.
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。
Switch this session to another Cognee dataset. Lists the datasets you can write to, lets you pick one, syncs the current session into its dataset, then starts a fresh Cognee session bound to the chosen dataset. All later saves and recalls target the new dataset and the status line shows it.
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 any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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。
Investigate data incidents and find root causes using Monte Carlo's observability data. Guides the agent through systematic investigation: alert lookup, lineage tracing, ETL checks, query analysis, and data profiling. Activates when a user asks about data issues, incidents, alerts, or why data looks wrong.
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。
Search TikTok e-commerce store leaderboards via Kalodata and query detailed information for specific stores. Supports viewing high-ranking, high-sales TikTok Shop stores by region, currency, language, and date range, and using shopId to retrieve revenue, sales volume, on-sale product count, self-operated/affiliate/shopping mall channel revenue, and creator collaboration count. Trigger when users mention TikTok store ranking, TikTok shop leaderboard, TikTok top shops, TikTok store ranking, TikTok Shop store ranking, kalodata store search, kalodata store ranking, TikTok store detail, TikTok shop profile, store revenue, store sales, TikTok shop ranking, TikTok shop leaderboard, TikTok top shops, TikTok store ranking, TikTok shop detail, TikTok store detail, kalodata shop search/detail. Even if the user does not explicitly mention "kalodata", trigger this skill whenever their need involves viewing TikTok store leaderboards or detailed data for a specific TikTok store.
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 when reading, writing, moving, copying, modifying, deleting, or generating data files — including any operation that touches data/raw, data/external, data/interim, data/processed, or outputs directories.
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。
Lightweight, script-driven variant of data2motion for turning data into a smooth, on-brand animated chart (one self-contained HTML) — built for a TEXT-ONLY model that cannot see its own output. You never write HTML/CSS/SVG/animation; you extract the data, pick a chart, fill a small JSON spec, and run one build script that owns 100% of the look and the motion, so the result cannot drift from the house template style or lose its smoothness. Use to turn a number/stat/table/CSV/paragraph into a quick animated chart, data reveal, or KPI when you want guaranteed template fidelity with minimal reasoning and no image feedback.
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。
Process vehicle insurance Excel data using Pandas - file handling, data cleaning, merging, validation. Use when processing Excel/CSV files, handling data imports, implementing business rules (negative premiums, zero commissions), debugging data pipelines, or optimizing Pandas performance. Keywords: data_processor.py, Excel, CSV, Pandas, merge, deduplication, date normalization.
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 a data pipeline — ETL/ELT with extraction, transformation, loading, error handling, and scheduling. Use when asked to "build ETL", "data pipeline", "move data from X to Y", or "sync 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。
Database reconnaissance — full inventory of schema, migrations, data volume, backups, connection pooling, and query patterns. Use when asked to "assess this database", "understand the schema", or "database health 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。
Outlook の予定表・送受信メール・Teams チャットから営業活動を抽出し、Dataverse CRM の営業活動と商談の「次のアクション」へ差分だけ登録する日報・週報スキル。 Use when ユーザーが「今日の営業報告をして」「日報を CRM に登録して」「今週の商談活動を CRM に反映して」「会議の内容を CRM に記録して」と依頼したとき。 Dataverse MCP コネクタ（describe / read_query / search_data / create_record / update_record）を使用する。
株主総会の想定問答を、IR 抜粋（決算短信・説明資料・招集通知など）を根拠に下書きし、利用者の確認後に Dataverse の想定問答テーブルへ「下書き」として登録するスキル。 Use when ユーザーが「配当について想定問答を作って」「この論点の想定問答を 3 件追加して」「招集通知から想定問答を作って」「想定問答を登録して」と依頼したとき。 Dataverse MCP コネクタ（describe / read_query / search_data / create_record / update_record）を使用する。削除・テーブル変更のツールは使わない。
株主総会の質疑応答のリハーサル台本（議長・株主・回答役員の読み上げ原稿）を、承認済みの想定問答から作り、Dataverse のリハーサル台本テーブルへ登録するスキル。 Use when ユーザーが「リハーサルの台本を作って」「QA-001〜QA-010 で読み上げ原稿を作って」「番号を言わない株主も入れた台本を作って」と依頼したとき。 Dataverse MCP コネクタ（describe / read_query / search_data / create_record / update_record）を使用する。削除・テーブル変更のツールは使わない。
Data cleaning, transformation, reshaping, joins, missing data handling, and tidy data principles. Covers the full pipeline from raw ingestion to analysis-ready datasets -- type coercion, deduplication, outlier detection, normalization, melting/pivoting, regex extraction, and reproducible transformation chains. Use when preparing, cleaning, or transforming data for 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。
This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.
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。
Specialist skill for Python data engineering — pandas, polars, DuckDB, numpy, ETL pipelines, tabular data ingestion, and notebook-to-module extraction. Use when working with dataframes, data validation at ingress boundaries, merge/join operations, typed column contracts, or choosing between pandas vs polars vs DuckDB for a data task.
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 last30days-style pre-research pass in Deepline: discover the critical public, private, CRM, workflow, social, and web data sources for a research/enrichment job; compare provider coverage; estimate Deepline credit cost; recommend the source plan before building or running the workflow; or build custom language/messaging from buyer, competitor, community, and CRM evidence. Triggers: pre-research, source discovery, provider strategy, research data sources, ScrapeCreators, X/Twitter data, Reddit comments, public and private datasets, CRM data, workflow data, custom language, messaging language, pain 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。
Convert Kai workflow data, CSV exports, audit folders, SDR package outputs, and marketing reports into dashboard-ready specs or lightweight static dashboards. Use when "data dashboard", "operator dashboard", "operator room", "HTML operators room", "sales dashboard", "SDR dashboard", "turn this data into a dashboard", "dashboard handoff", "visualize Kai data", or any request to package sourced marketing, sales, audit, or SDR data for a dashboard or presentation surface.
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for cloud metadata services, instance identity, workload identity, link-local credential paths, role assumption, and metadata-to-privilege trust edges. Use when the user asks to inspect metadata-service access, instance credentials, pod or workload identity, link-local token paths, SSRF-to-metadata escalation, or explain how metadata-derived credentials turn into accepted cloud or control-plane privilege. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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。
Design and operate a data integrity monitoring programme based on ALCOA+ principles. Covers detective controls, audit trail review schedules, anomaly detection patterns (off-hours activity, sequential modifications, bulk changes), metrics dashboards, investigation triggers, and escalation matrix definition. Use when establishing a data integrity monitoring programme for GxP systems, preparing for inspections where data integrity is a focus area, after a data integrity incident requiring enhanced monitoring, or when implementing MHRA, WHO, or PIC/S guidance.
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。
Fix silent metadata issues in RFDB node storage. Covers two traps: (1) metadata flattening — nested metadata fields become top-level after serialization, so node.metadata.field is undefined but node.field works. (2) reserved keys — fields named "type", "id", "name", "file", "exported" are silently stripped from metadata by _parseNode() to prevent overwriting top-level node fields.
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。
Datadog provider for obz. Supports metrics (Datadog Query Language), logs (Datadog Log Query), and traces. Requires api-key and app-key configured under auth in config.yaml. Does not support metric labels, label-values, or series commands. This skill should be used when the user mentions "Datadog", "DD", "obz -p dd", Datadog Query Language, or needs to configure Datadog provider instances with regional endpoints.
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。