1,041 件 ・ 関連度順
概要と使いどころ
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 Checkov — checkov infrastructure-as-code security scanning. Covers Terraform, CloudFormation, Kubernetes, and Dockerfile scanning, policy management, custom checks, compliance frameworks, and suppression management. Use when scanning IaC for security misconfigurations, evaluating compliance, managing custom policies, or reviewing scan 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。
Use when working with Kubescape — kubescape Kubernetes security posture analysis. Covers NSA/CISA framework assessment, MITRE ATT&CK mapping, CIS Kubernetes benchmarks, workload scanning, RBAC analysis, and network policy evaluation. Use when assessing Kubernetes cluster security, evaluating compliance frameworks, or analyzing workload 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 Aws Cognito — aWS Cognito user pool analysis, identity pool management, authentication flow analysis, and MFA status tracking. Covers user statistics, app client configuration, password policy review, Lambda trigger inspection, and federation setup.
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 Batch — aWS Batch compute environment management, job queue analysis, scheduling policy review, and array job monitoring. Covers compute resource utilization, job status tracking, failure investigation, Fargate vs EC2 comparison, and queue priority 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。
The paper 'When EOS Tokens Disagree: Understanding Length Inflation in On-Policy Distillation' revea
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。
They re context management, tool design, policy gates, memory strategies and, if you want to run loc
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。
AiZynthFinder retrosynthetic route planning (CASP) from AstraZeneca Molecular AI. Monte Carlo tree search guided by a template-based neural expansion policy recursively disconnects a target SMILES until precursors are found in a purchasable stock. Covers config.yml (v4 format), aizynthcli batch screening, the AiZynthFinder/AiZynthExpander Python API, one-step disconnections, custom stocks via smiles2stock, scorers, Retro*/breadth-first/DFPN search alternatives, and reading output.json.gz / trees.json. Use for synthesis route planning, synthesizability screening, and building-block/precursor search. For reaction barriers use neb-irc-activation-energy; for 2D reaction scheme drawing use rdkit-chemdraw-cdxml.
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。
Comprehensive investigation of complex multi-domain narratives (technology, theology, policy, finance). Systematically decouples factual claims from speculation through iterative verification, multi-source comparison, and contradiction resolution. Use for investigations requiring institutional knowledge, historical context, and handling of conflicting incentives.
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。
Release tdmcp: write the Keep-a-Changelog entry, bump the SemVer version across every manifest, then (autonomously, per project policy) commit, tag, and push — gated on a td-qa PASS, with hard git safety rails. Use when releasing/shipping/cutting a tdmcp version, writing the CHANGELOG, bumping the version, or tagging a release.
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: ticket lifecycle, state transitions, ready for QA, changes requested, QA passed, role-based workflow policy, Developer to QA handoff, or relay routing across delivery agents.
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。
Implement eBPF-based runtime security observability and enforcement in Kubernetes clusters using Cilium Tetragon for kernel-level threat Tespit and policy enforcement.
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。
Implements data loss prevention policies using Microsoft Purview to protect sensitive information across Exchange Online, SharePoint, OneDrive, Teams, endpoint devices, and Power BI. The analyst configures sensitivity labels with encryption and content marking, creates DLP policies using built-in and custom sensitive information types with regex patterns, Dağıt:s endpoint DLP rules to control file operations on Windows and macOS devices, and monitors policy effectiveness through Activity Expl...
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。
bu skill covers implementing automated security scanning for Infrastructure as Code (IaC) templates using tools like Checkov, tfsec, and KICS. It addresses Tespit etme misconfigurations in Terraform, CloudFormation, Kubernetes manifests, and Helm charts before Dağıt:ment, establishing policy-based governance, and integrating IaC scanning into CI/CD pipelines to prevent insecure cloud resource provisioning.
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。
bu skill guides practitioners through hardening AWS Identity and Access Management configurations to enforce least privilege access across cloud accounts. It covers IAM policy scoping, permission boundaries, Access Analyzer integration, and credential rotation strategies to reduce the blast radius of compromised identities.
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。
Performing authorized privilege escalation assessments in AWS environments to identify IAM misconfigurations that allow users or roles to elevate their permissions using Pacu, CloudFox, Principal Mapper, and manual IAM policy analysis techniques.
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。
Enforce Kubernetes network segmentation using Calico CNI network policies and global network policies to control pod-to-pod traffic, restrict egress, and implement zero-trust microsegmentation.
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。
Auditing Terraform infrastructure-as-code for security misconfigurations using Checkov, tfsec, Terrascan, and OPA/Rego policies to tespit etmeoverly permissive IAM policies, public resource exposure, missing encryption, and insecure defaults before cloud Dağıt:ment.
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。
Implements secure API key generation, storage, rotation, and revocation controls to protect API authentication credentials from leakage, brute force, and abuse. The engineer designs API key formats with sufficient entropy, implements secure hashing for storage, enforces per-key scoping and rate limiting, monitors for leaked keys in public repositories, and builds key rotation workflows. Activates for requests involving API key management, API key security, key rotation policy, or API credenti...
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。
Implements AWS Nitro Enclave-based confidential computing environments with cryptographic attestation, KMS policy integration using PCR-based condition keys, and secure vsock communication channels. The practitioner builds enclave images, configures attestation-aware KMS policies, validates attestation documents against the AWS Nitro PKI root of trust, and establishes isolated computation pipelines for processing sensitive data such as PII, cryptographic keys, and healthcare records. Activate...
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。
tespit etme (s) and prevents code injection attacks targeting serverless functions (AWS Lambda, Azure Functions, Google Cloud Functions) through event source poisoning, malicious layer injection, runtime command execution, and IAM privilege escalation via function modification. The analyst combines static analysis of function code, CloudTrail event correlation, runtime behavior monitoring, and IAM policy auditing to identify injection vectors across the expanded serverless attack surface including AP...
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。
Implements input and output validation guardrails for LLM-powered applications to prevent prompt injection, data leakage, toxic content generation, and hallucinated outputs. Builds a security validation pipeline using NVIDIA NeMo Guardrails Colang definitions, custom Python validators for PII Tespit and content policy enforcement, and the Guardrails AI framework for structured output validation. The guardrails system intercepts both user inputs (blocking injection attempts, stripping PII, ...
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。
授業のシラバスに生成 AI の使用ルールを書き込みたい、学生に AI 使用可否と範囲を明示したい、 学科で AI 使用の共通テンプレートを作りたい、同僚教員から「うちの授業でどう書けば」と聞かれた、 教務委員会でシラバス記載ルールを議論する必要がある場面で使うシラバス AI 方針設計スキル。 4 段階テンプレート（自由／場面限定／完全禁止／その他）と科目類型別選択肢カタログで、 教員が 10 分で決められる判断フローを提供する。
入試の志望理由書や総合型選抜書類に生成 AI を使った場合の扱いを決めたい、 出願要項に AI 使用可否をどう書くか悩む、AI 検知ツール導入を検討している、 志望理由書に AI 使用の疑義が生じた時の判断基準を整備したい、 不正認定と教育機会のどちらで運用するか迷う場面で使う入試部門職員向けスキル。 文科省通知を骨格に、3 類型判断・二重確認プロセス・運用語彙選択・ツール未導入大学向け代替案を提供する。