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概要と使いどころ
Use when working with Azure Policy Deep — azure Policy deep compliance and governance management. Covers policy definitions, initiatives, assignments, compliance evaluation, exemptions, remediation tasks, custom policies, and regulatory compliance. Use when managing Azure governance at scale, reviewing compliance states, creating custom policies, or troubleshooting policy evaluation results.
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。
MANDATORY parallel execution patterns, cluster overview script, -o json with jq filtering, batch operations, and common pitfalls
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 working with Argo Workflows — argo Workflows management for Kubernetes-native workflow orchestration. Covers workflow templates, cron workflow scheduling, artifact management, workflow execution, parameter handling, and resource monitoring. Use when checking workflow status, investigating step failures, managing templates, or auditing cron 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。
Use when working with Airflow — apache Airflow workflow orchestration management. Covers DAG management, task instance monitoring, executor health, pool status, variable management, connection audit, and scheduler diagnostics. Use when checking DAG run status, investigating task failures, managing Airflow resources, or auditing configurations.
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 working with Apache Airflow Deep — apache Airflow deep management covering DAG inventory, task instance monitoring, scheduler health, executor status, pool and variable management, connection auditing, and SLA miss tracking. Use when investigating DAG failures, analyzing task duration trends, monitoring scheduler performance, or auditing Airflow configurations.
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 working with Ansible — ansible automation and configuration management. Covers playbook execution, inventory management, role management, vault secrets, ad-hoc commands, fact gathering, and task debugging. Use when running playbooks, managing inventories, debugging task failures, or auditing Ansible configurations.
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 working with Gcp — cost anti-hallucination rules, MANDATORY parallel execution patterns (30x speedup), monitoring aligners, reusable billing/pricing scripts, VAT/tax handling, and filtering/pagination.
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。
MANDATORY parallel execution patterns (30x speedup), monitor metrics aggregation, --output tsv formatting, and common pitfalls
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 working with Azure Functions — azure Functions app analysis, execution metrics, consumption plan monitoring, scaling configuration, and deployment management via Azure CLI.
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 working with Aws — mANDATORY parallel execution patterns (30x speedup), CloudWatch statistics syntax, Cost Explorer aggregation, output token limits, and common pitfalls.
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 working with Aws Systems Manager — aWS Systems Manager parameter store management, session management, patch compliance tracking, and Run Command analysis. Covers parameter inventory, managed instance status, automation execution, maintenance windows, and inventory collection.
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 working with Gcp Cloud Functions — google Cloud Functions management, execution analysis, scaling configuration, event trigger inspection, and runtime diagnostics via gcloud CLI.
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 working with Aws Athena — aWS Athena query execution analysis, workgroup management, Data Catalog integration, and cost-per-query tracking. Covers query history, performance optimization, workgroup quota management, saved queries, and data scanned 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。
Use when working with Aws Cloudwatch Logs — aWS CloudWatch Logs group management, Logs Insights query execution, metric filter analysis, retention policy review, and subscription filter management. Covers log group inventory, storage cost optimization, query patterns, and cross-account log aggregation.
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 working with Great Expectations — great Expectations data quality framework analysis. Covers expectation suite management, validation result review, data docs generation, checkpoint execution, datasource configuration, and batch analysis. Use when reviewing data quality results, managing expectation suites, investigating validation failures, or auditing data quality configurations.
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 working with Snowflake — snowflake data warehouse analysis, query performance tuning, cost optimization, warehouse management, and schema inspection. Covers QUERY_HISTORY analysis, credit consumption, warehouse utilization, storage analysis, data sharing, and Time Travel. Read this skill before any Snowflake operations — it enforces two-phase execution, anti-hallucination rules, and read-only safety constraints.
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。
Gym workout planner and nutrition tracker. Search 690+ exercises by muscle, equipment, or category via wger. Look up macros and calories for 380,000+ foods via USDA FoodData Central. Compute BMI, TDEE, one-rep max, macro splits, and body fat — pure Python, no pip installs. Built for anyone chasing gains, cutting weight, or just trying to eat better.
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 a markdown plan to .openamer/plans/; no execution.
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。
LLM Prompt injection Share Executive Summary Palo Alto Networks has released Securing GenAI: A Com
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。
Autonomous by Design, Uncontrolled in Practice 2026-09-08 Executive Summary Between July 9 and Augus
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 for mission: define, prioritize, --tick, progress, scan.
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。
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
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。
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
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。
Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include "method validation", "analytical method validation", "AMV", "validation protocol", "acceptance criteria", "linearity", "reportable range", "accuracy and precision", "repeatability", "intermediate precision", "recovery", "LOD", "LOQ", "detection limit", "quantitation limit", "specificity", "robustness", "method transfer", "method comparison", "Deming", "Passing-Bablok", "Bland-Altman", "equivalence testing", "OOS investigation", "ICH Q2", "Q2(R2)", "Q14", "USP 1225", "ICH M10", "incurred sample reanalysis", "ISR", "CLSI EP", and any request to show that an assay works.
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。