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概要と使いどころ
Use when reviewing scripts, client components, bundles, or runtime behavior related to Never use eval() or unsafe dynamic code execution. Inspect both source code and the browser execution path so fixes target the real bottleneck or bug.
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。
High-performance DataFrame library for Python ETL, analytics, and pandas migration. It supports expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU 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。
Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.
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。
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast 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。
Operations leadership for scaling companies. Process design, OKR execution, operational cadence, and scaling playbooks. Use when designing operations, setting up OKRs, building processes, scaling teams, analyzing bottlenecks, planning operational cadence, or when user mentions COO, operations, process improvement, OKRs, scaling, operational efficiency, or 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。
Provides diagnostic workflows and step-by-step root-cause analysis procedures for actively broken, failing, or slow BigQuery jobs, execution graph and query plan stage bottlenecks, system performance issues, or unexpectedly expensive workloads. Use when interpreting symptoms, isolating bottlenecks, diagnosing cost spikes (on-demand query spend, capacity slot autoscaling, storage growth), execution graph stages or substep variables, identifying root causes, and determining remediation steps. Don't use for writing or optimizing SQL, proactive capacity planning, or storage layout design (use bigquery-optimization), or when the user already knows which telemetry they want and just needs the query (use bigquery-observability).
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。
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.
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。
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast 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。
Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.
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。
Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.
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。
Fallback pattern for executing Python code when sandbox execution fails by writing scripts to disk and running via shell
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。
Fallback from execute_code_sandbox to file-based run_shell execution when sandbox fails
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。
Fallback workflow for reliable file creation and verification when automated code execution fails
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。
Four-step recovery workflow for code execution failures when inline Python fails
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。
Debug Python script execution failures by capturing full tracebacks and verifying working directory
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。
Fallback workflow for code execution when automated tools fail, using shell heredoc with verification
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。
Debug Python code execution failures by capturing partial traces, isolating failing functions, and incrementally verifying outputs
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。
Handle code execution failures with fallback strategies and anchored workspace paths
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。
Two-step script execution workflow for debugging when shell_agent and execute_code_sandbox consistently fail
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。
Reliable Python execution workflow when execute_code_sandbox or shell_agent fail
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。
Fallback workflow for running code via file write and shell when sandbox execution fails
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。
Command and Control framework deployment, configuration, and operational tradecraft for red team engagements. Covers Cobalt Strike (malleable C2 profiles, Beacon types HTTP/HTTPS/DNS/SMB, Beacon Object Files for in-memory execution, sleep and jitter tuning, named pipe pivoting), Sliver (implant generation across mTLS/WireGuard/DNS transport, operator multiplayer mode, armory extensions), Mythic (agent ecosystem with Apollo/Poseidon/Medusa, C2 profile configuration, translation containers), Havoc (Demon agent with sleep obfuscation via Ekko/Zilean, indirect syscalls, dotnet inline execution), Metasploit (msfvenom payload generation, multi/handler staging, Meterpreter post-exploitation modules), redirector architecture using Apache mod_rewrite and Nginx, domain fronting through CDN providers, DNS-based C2 for restrictive network egress, and TLS certificate management for infrastructure OPSEC. Tools: Cobalt Strike, Sliver, Mythic, Havoc, Metasploit Framework. Aligns to MITRE ATT&CK T1071 (Application Layer Protocol), T1573 (Encrypted Channel), T1090 (Proxy/Connection Proxy).
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。
Phishing campaign execution methodology for authorized red team engagements. Covers end-to-end campaign lifecycle: infrastructure provisioning (GoPhish, SMTP relay configuration, domain acquisition and aging, SPF/DKIM/DMARC alignment), payload delivery vectors (Office macro weaponization, HTA droppers, ISO/IMG container abuse, LNK shortcut hijacking, OneNote embedded payloads, HTML smuggling), email authentication bypass techniques (SPF softfail exploitation, DKIM replay attacks, display name spoofing, homoglyph and cousin domain registration), credential harvesting with MFA bypass (EvilGinx2 transparent proxy, Modlishka session relay, pixel-perfect HTML cloning), spear phishing pretext development informed by OSINT, email security gateway evasion, QR code phishing (quishing), and callback phishing for initial access. Integrates with GoPhish for campaign management, EvilGinx2 for adversary-in-the-middle credential interception, King Phisher for template design, and the Social Engineering Toolkit for payload generation. Maps to MITRE ATT&CK T1566 (Phishing) and sub-techniques T1566.001 (Spearphishing Attachment), T1566.002 (Spearphishing Link), T1566.003 (Spearphishing via Service). Assumes you have written authorization and a defined scope before any campaign 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。
Comprehensive CI/CD pipeline exploitation methodology covering GitHub Actions injection vectors (expression injection via PR titles and issue bodies, workflow_run event abuse, GITHUB_TOKEN over-scoping, composite action supply chain compromise), Jenkins attack paths (Groovy sandbox escapes, script console remote code execution, Java remoting deserialization, credential store dumping, shared library injection), GitLab CI exploitation (YAML anchor injection, runner registration token abuse, CI variable extraction, protected branch bypass via merge request pipelines), and Azure DevOps pipeline agent compromise with service connection theft. Includes artifact poisoning techniques across all platforms, tooling guidance for gato and jenkins-attack-framework, and maps to MITRE ATT&CK T1195.002 (Supply Chain Compromise: Compromise Software Supply Chain). Covers enumeration of pipeline configurations, privilege escalation from contributor to code execution, lateral movement through pipeline trust boundaries, and persistence via modified workflow definitions. Each technique section provides working exploitation code, detection indicators, and defensive countermeasures.
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。