1,262 件 ・ 関連度順
概要と使いどころ
Detect project stack and generate GitHub Actions CI/CD workflows — PR checks with lint/test/build/security, deploy pipelines for Vercel/AWS/GCP/Fly/Docker, dependency caching, matrix testing, preview deploys, and Dependabot config
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 codebases for leaked secrets and hardcoded credentials, generate .env templates, configure secrets management with AWS Secrets Manager, Vault, Doppler, or GCP Secret Manager, set up credential rotation, and integrate secrets into CI/CD pipelines via OIDC federation
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。
Evaluate survey data pipelines and research methodology. Analyzes response bias detection (speeders, straight-liners, bots), statistical significance testing (t-test, chi-square, ANOVA with Bonferroni correction), sentiment and theme extraction from open-ended responses.
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 a media production studio or post-production facility. Analyzes facility scheduling and utilization, equipment lifecycle tracking, editorial and VFX pipelines, color grading and finishing workflows, digital asset management (MAM/DAM), IMF/MXF format compliance.
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。
Displays the full skills catalog with descriptions, autonomous build chains, recommended pipelines, and parallelization rules.
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 a real estate analytics platform -- evaluate comparable sales and rental engines, automated valuation models (AVM), demographic and economic indicator pipelines, submarket scoring, gentrification detection, price forecasting accuracy, market cycle analysis, and risk modeling.
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 manufacturing predictive maintenance systems for OPC-UA/MQTT sensor data pipelines, time-series storage retention, ML model lifecycle (training-serving skew, drift detection, retraining triggers), MTBF/MTTF/MTTR reliability calculations, Weibull survival 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。
Audit laboratory automation systems -- LIMS architecture, instrument connectivity (SiLA 2, OPC-UA, serial drivers), sample tracking and chain of custody, protocol workflow engines, data acquisition pipelines, and regulatory compliance (21 CFR Part 11 electronic records/signatures.
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 demand forecasting models and inventory optimization logic -- forecast accuracy (MAPE, bias), safety stock calculations, reorder point strategies (EOQ, ROP, min/max), ABC/XYZ classification, demand signal pipelines, and multi-location network optimization..
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 e-discovery and litigation document review systems -- data collection pipelines (PST, MBOX, SharePoint, Slack), document processing (OCR via Tesseract/ABBYY, metadata extraction, deduplication), Technology Assisted Review (TAR 1.0/2.0/CAL with recall/precision tracking).
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 medical billing and revenue cycle management software including claims processing pipelines, EDI transaction handling (837P/837I/835/270/271/276/277), ICD-10 and CPT code validation with NCCI edit checking, payer rules engine configuration, denial management and appeal 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。
Audit game narrative systems for technical quality -- branching dialogue trees, state/flag tracking, quest systems, choice-consequence mapping, localization pipelines, voice-over integration, and cinematic scripting..
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 a drug discovery pipeline for operational efficiency and scientific rigor. Triggers: building or reviewing pharma R&D platforms, cheminformatics pipelines, or screening data management systems.
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 manufacturing defect detection and quality control systems — computer vision inspection pipelines, SPC control charts, Six Sigma process capability (Cp/Cpk), defect classification taxonomies, root cause analysis tooling, and measurement system 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。
Audit a grant management system for proposal workflow efficiency, deadline tracking, budget-narrative alignment, outcome reporting, compliance readiness, and win rate optimization. Use when reviewing nonprofit grant software, building a grants CRM, analyzing proposal pipelines, or evaluating funder reporting 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。
Analyze demand forecasting systems — time-series decomposition (ARIMA, Prophet, ETS), seasonal and event-driven demand modeling, booking curve and pickup analysis, cancellation prediction, forecast accuracy metrics (MAPE, bias), and model retraining pipelines.
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 fraud detection systems including rule engines, ML scoring models, real-time transaction monitoring, alert triage workflows, false positive management, SAR/CTR regulatory reporting, adversarial robustness testing, and adaptive retraining pipelines for payment fraud, account takeover, identity theft, and AML compliance.
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 precision agriculture and crop management software for yield prediction model accuracy, soil analysis integration, irrigation optimization algorithms, pest and disease detection pipelines, satellite and drone imagery processing, weather data integration.
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。
Analyzes SIEM alert pipelines for rule optimization, alert fatigue reduction, criticality scoring, asset-based prioritization, and correlation rule design using NIST CSF and detection engineering principles..
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。
Write an ETL operational runbook. Use when the user says "ETL runbook", "pipeline runbook", "data job runbook", "how to operate this ETL", "on-call guide for data pipelines", "what to do when the data job fails", "pipeline troubleshooting guide", "data ops runbook", or needs to document how to operate, monitor, and recover a data pipeline or ETL job - even if they don't explicitly say "runbook".
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。
**MEASUREMENT SKILL** — Benchmark framework for measuring skill effectiveness. A/B tests agent outputs with and without a target skill, scoring correctness, completeness, and pattern adherence. USE FOR: validating skill quality, comparing before/after skill changes, proving skill ROI with data. DO NOT USE FOR: general testing, unit tests, CI pipelines.
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 pipeline for producing Vox-style explainer videos from a single topic prompt. One topic in, finished film out — script, keyframes (Seedream 5.0 Pro), animation (Gemini Omni Flash), voiceover (xAI TTS), music (MiniMax Music 2.6), all assembled locally with ffmpeg via the Atlas Cloud API. Use this skill whenever the user asks for an explainer video, a Vox-style video, a documentary short, an educational video essay, a "one prompt to video" pipeline, or wants to turn a topic/article/report into a narrated video with subtitles and music. Also trigger when the user mentions Atlas Cloud video pipelines, keyframe-to-animation workflows, or automated video essays — even if they don't say "Vox."
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。
Designs a tailored multi-product agentic data science architecture on Google Cloud that incorporates opinionated best practices. Use when architecting multi-product solutions for agent-based data analytics or ML workloads. Don't use for simple queries, non-agentic pipelines, general cloud reviews, or writing agent 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。
Translates Snowflake dbt SQL models to Standardized BigQuery SQL. Handles SQL compilation, Jinja macro placeholder masking, BigQuery Translation Service migration workflows, AST-based config transformations, explicit type casting, JSON extraction standardization, and deduplication. Use when migrating Snowflake dbt pipelines or models to Google Cloud BigQuery. Don't use for generic BigQuery queries or non-Snowflake SQL migrations.
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。