554 件 ・ 関連度順
概要と使いどころ
Traces complete execution paths through DAG workflows. Records timing, inputs, outputs, and state transitions for all nodes. Activate on 'execution trace', 'trace execution', 'execution path', 'debug execution', 'execution log'. NOT for performance analysis (use dag-performance-profiler) or failure investigation (use dag-failure-analyzer).
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。
Performs root cause analysis on DAG execution failures. Traces failure propagation, identifies systemic issues, and generates actionable remediation guidance. Activate on 'failure analysis', 'root cause', 'why did it fail', 'debug failure', 'error investigation'. NOT for execution tracing (use dag-execution-tracer) or performance issues (use dag-performance-profiler).
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。
Delegate an implementation or investigation task to Claude.
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。
Delegate an implementation or investigation task to Claude.
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。
Systematically find and fix performance problems in applications, frontend and backend, by measuring first. Use this skill whenever the user reports slow endpoints, high latency, poor throughput, memory leaks, large bundles, or slow page loads; wants to profile with cProfile, py-spy, Chrome DevTools, Node --cpu-prof, or perf; run load tests with locust or k6; set performance budgets or SLOs; optimize database queries (EXPLAIN, indexes, N+1); add caching (Redis); improve frontend performance (code splitting, lazy loading, image optimization, memoization, virtualization); or tune the network (CDNs, gzip/br, HTTP/2/3). Also trigger for p50/p95/p99 latency, throughput, error rate, connection pooling, async vs threads, GIL, and memory leak investigations.
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。
Specialized GitHub PR intelligence agent for automatically gathering comprehensive context from Pull Requests. Activate when users need CI failure analysis, review comment investigation, or PR status assessment. Triggers on requests like "CI 실패 원인 찾아줘", "gather review comments", "check PR status", "analyze PR".
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 multi-step investigation — deep research carried end-to-end without per-step approval; explicit request only, never for normal feature work.
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。
[Workflow] Use when activating the Visual Diagram workflow for create visual excalidraw diagrams from codebase investigation or web research.
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。
[Investigation] Use when quickly locating relevant files and affected areas across a large codebase.
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。
[Investigation] Use when about to author a new Feature Spec from an idea — investigate all existing Feature Specs AND related code logic first to surface related/overlapping/affected specs, missing features, missing test cases/user stories, system unknowns, and the invariant landscape, before any spec is drafted.
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。
[Research] Use when starting a web research task — discover, gather, and triage candidate sources on a topic to feed deeper investigation.
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。
[Workflow] Use when activating the Bug Fix workflow for systematic debugging with root cause investigation, fix, and 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。
[Workflow] Use when activating the Feature Implementation workflow for implement a well-defined feature with investigation, planning, implementation, and review. Also covers TDD/test-first development and spec-driven feature implementation with test specs written before 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。
GStack investigation method for bugs, incidents, failing tests, regressions, and unclear behavior where evidence and root cause should precede fixes.
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 an evidence-backed working model of an existing codebase. Use when you must orient in a large or unfamiliar repository; explain architecture or a subsystem; trace a request, runtime, control, or data flow for a concrete question or bug; analyze a branch, commit, diff, or pull request for behavioral impact and blast radius; or assess AI-generated or changed code against requirements, contracts, invariants, tests, and runtime evidence. Default to read-only investigation. Do not trigger only because a coding task touches an existing repository; use when understanding or verification is the requested outcome or a substantial prerequisite.
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。
Detects and responds to OAuth token theft and replay attacks in cloud environments, focusing on Microsoft Entra ID (Azure AD) token protection, conditional access policies, and sign-in anomaly detection. Covers access token theft, refresh token replay, Primary Refresh Token (PRT) abuse, and pass-the-cookie attacks. Activates for requests involving OAuth token theft detection, token replay prevention, Azure AD conditional access token protection, or cloud identity attack investigation.
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。
Detects and analyzes fileless malware that operates entirely in memory using PowerShell, WMI, .NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing traditional executable files to disk. Activates for requests involving fileless threat detection, in-memory malware investigation, LOLBin abuse analysis, or WMI persistence examination.
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。
Detects and analyzes malicious behavior in mobile applications through behavioral analysis, permission abuse detection, network traffic monitoring, and dynamic instrumentation. Use when analyzing suspicious mobile applications for data exfiltration, command-and-control communication, credential stealing, SMS interception, or other malware indicators. Activates for requests involving mobile malware analysis, app behavior monitoring, trojan detection, or suspicious app investigation.
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。
Detects and analyzes process injection techniques used by malware including classic DLL injection, process hollowing, APC injection, thread hijacking, and reflective loading. Uses memory forensics, API monitoring, and behavioral analysis to identify injection artifacts. Activates for requests involving process injection detection, code injection analysis, hollowed process investigation, or in-memory threat detection.
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。
Detects 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 API Gateway, S3, SQS, DynamoDB Streams, and CloudWatch event triggers. Activates for requests involving Lambda security assessment, serverless injection detection, function event poisoning analysis, or serverless privilege escalation investigation.
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。
Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity 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。
Detects command-and-control (C2) communications tunneled through DNS protocol including DNS tunneling tools (Iodine, dnscat2, dns2tcp, Cobalt Strike DNS beacon), domain generation algorithms (DGA), encoded payload delivery via TXT/CNAME records, and DNS beaconing patterns. Covers Shannon entropy analysis of query subdomains, statistical anomaly detection, ML-based DGA classification, passive DNS correlation, and Zeek/Suricata signature development. Activates for requests involving DNS-based C2 detection, DNS tunnel identification, suspicious DNS traffic investigation, or DGA domain classification.
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。
Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs. Activates for requests involving authentication anomaly detection, login behavior analysis, UEBA implementation, or suspicious sign-in investigation.
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。
Configures Windows Event Logging with advanced audit policies to generate high-fidelity security events for threat detection and forensic investigation. Use when enabling audit policies for logon events, process creation, privilege use, and object access to feed SIEM detection rules. Activates for requests involving Windows audit policy, event log configuration, security logging, or detection-oriented logging.
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。