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概要と使いどころ
Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials. Supports Windows, Linux, and macOS memory forensics. Activates for requests involving memory forensics, RAM analysis, volatile data examination, process injection detection, or memory-resident malware 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。
Performs Linux memory acquisition using LiME (Linux Memory Extractor) kernel module and analysis with Volatility 3 framework. Extracts process lists, network connections, bash history, loaded kernel modules, and injected code from Linux memory images. Use when performing incident response on compromised Linux 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。
Parses and analyzes the Windows Amcache.hve registry hive to extract evidence of program execution, application installation, and driver loading for digital forensics investigations. Uses Eric Zimmerman's AmcacheParser and Timeline Explorer for artifact extraction, SHA-1 hash correlation with threat intel, and timeline reconstruction. Activates for requests involving Amcache forensics, program execution evidence, Windows artifact analysis, or application compatibility cache 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。
Analyze Chromium-based browser artifacts using Hindsight to extract browsing history, downloads, cookies, cached content, autofill data, saved passwords, and browser extensions from Chrome, Edge, Brave, and Opera for forensic 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。
Investigate compromised Docker containers by analyzing images, layers, volumes, logs, and runtime artifacts to identify malicious activity and evidence.
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。
ネットワーク品質を総合的に診断し、ボトルネックの特定と根本原因の深堀りまで行うスキル。 接続情報、レイテンシ、ダウンロード速度、HTTP接続タイミング、経路解析を自動実行し、 品質閾値に基づく総合評価を日本語レポートとして出力する。 外部依存なし（OS標準ツールのみ使用）、macOSおよびLinux対応。 Use when diagnosing network quality, troubleshooting slow connections, identifying network bottlenecks, measuring latency/bandwidth/jitter, or generating network health reports. 「ネットワーク診断」「network diagnostics」「ネットワークが遅い」 「latency check」「bandwidth test」「traceroute analysis」
Value Metrics（価値概念・インパクト評価・スコア乖離検証）の観点を説明する
Signpost (not a knowledge pack) for the avionics development assurance standards landscape: RTCA DO-178C / EUROCAE ED-12C and its supplements (DO-330/ED-215, DO-331/ED-218, DO-332/ED-217, DO-333/ED-216); RTCA DO-254 / EUROCAE ED-80; SAE ARP4754B and ARP4761A; and the free regulator documents that recognise them (FAA AC 20-115D, AC 20-152A, Order 8110.49A; EASA AMC 20-115D and AMC 20-152A). Contains no source content: each row carries only the designation, title, edition, owner, redistributability status, and the owner's URL. Use when you need to identify, cite, or locate an avionics standard; paywalled rows point to the owner, free rows point to the regulator text, and the FAA rows also name the in-repo faa-8110-49 pack.
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。
Econometrics skill for OLS regression and linear models. Activates when the user asks about: "run OLS", "linear regression", "ordinary least squares", "interpret regression results", "heteroskedasticity", "multicollinearity", "regression assumptions", "robust standard errors", "GLS", "WLS", "fit a regression model", "check regression diagnostics", "OLS假设", "最小二乘法", "线性回归", "回归系数", "残差检验", "异方差", "多重共线性", "普通最小二乘", "稳健标准误", "回归诊断"
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。
Knowledge base from the GAO Agile Assessment Guide (GAO-24-105506, 2023). Use for assessing and managing Agile software development in federal programs: the Agile Manifesto and framework family (Scrum, Kanban, XP, SAFe, DA, DSDM, Lean, DevOps); the three adoption perspectives (team dynamics, program operations, organization environment); requirements decomposition (epics, features, user stories, INVEST, MoSCoW, definition of done); Agile-aware federal contracting (FAR, modular contracting, SOO/PWS/SOW, product owner vs. COR); program monitoring and control (WBS, cost, schedule, EVM at the feature level); Agile metrics (velocity, lead/cycle time, CFD, burn charts); and the auditor's key-question bank. Oversight- and acquisition-oriented: it tells you how to *assess and govern* Agile programs, not how to code. Thin on hands-on engineering practice, deep cost/schedule checklists (see gao-cost, gao-schedule), and any non-US-federal context.
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Writes a DRAFT weekly KPI report from the figures the user pastes or attaches (this week, last week, optionally target and earlier weeks): a metrics table with change versus last week computed only from the stated figures, plain-language notes per metric, and numbered questions on every figure that moved more than the agreed threshold, crossed its target or broke its run. Use when the user asks to "write the weekly KPI report", "turn these numbers into this week's metrics report", "what changed versus last week", "draft the Monday numbers update" or "add commentary to this metrics table". Do not use for a recurring emailed report with a maintained trend sheet, use report-attachment-analyzer instead; for actuals versus budget, use budget-variance-explainer. Drafts for human review; never approves, authorises or signs off.
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。
Query PostHog product analytics via REST API — event analytics, HogQL SQL queries, feature flags, experiments, session recordings, insights, persons, cohorts. Use when the user wants to answer an analytics question, toggle/inspect a flag, or pull event data programmatically without opening the PostHog UI or installing the PostHog MCP.
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。
Produces marketing performance reports and data-driven recommendations from WooCommerce sales data, social media metrics, and campaign analytics. Use when the task asks for "campaign performance", "ROI analysis", "monthly report", "sales trends", "product velocity", "audience insights", or "KPI tracking". Covers metric definition, reporting templates, slow-mover detection, and insight-to-action frameworks.
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。
Compatibility skill for analytics tracking. Use when a user or upstream workflow invokes this name; route the task to `analytics` while preserving the requested scope and source-specific intent.
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 evaluating, critiquing, transforming, or generating a Master's Informatics thesis idea, research gap, novelty, research question, experiment, AI/ML/CV/NLP/LLM/Edge-AI method, or when distinguishing R&D research from software implementation using the Frascati Manual 2015.
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。
Render the beat table into alpha overlays. Use at the graphics stage after gate 3, or when the user says "build the graphics" or "render the overlays".
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Create an Estuary derivation that continuously aggregates metrics (daily totals, running counts, min/max, lifetime revenue) using reduction annotations. Use for real-time dashboards, continuous materialised views, and replacing batch ETL aggregations with streaming. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this. Use when user says "aggregation derivation", "derivation to sum daily totals", "running-count derivation", "transformation for min/max per group", "derive lifetime metrics", "aggregate-by-date derivation", or "real-time-dashboard derivation".
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 Codeium — codeium (Windsurf) AI coding assistant management and analytics. Covers team seat management, usage tracking, language analytics, editor adoption metrics, security policy configuration, and enterprise deployment review. Use when managing Codeium/Windsurf licenses, reviewing team adoption, or analyzing AI-assisted coding productivity.
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Use when working with Aws Elasticache Deep — aWS ElastiCache deep analysis for Redis and Memcached clusters, replication health, failover analysis, and performance metrics. Covers node-level metrics, memory utilization, cache hit rates, eviction tracking, connection analysis, and engine-specific diagnostics.
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。
Search arXiv physics, math, and computer science preprints using natural language queries. Powered by Valyu semantic search.
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。
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.
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。
Expert en Elasticsearch (indexing, mappings, aggregations, Kibana, DSL queries)
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。
Data-driven growth v3 — experimentation, metrics, dashboards, correlation 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。
Collect, download, and verify App Store Connect Analytics reports with asc. Use when users need to discover analytics report requests, select report instances by processing date or granularity, download every segment, or verify downloaded files against Apple's size and MD5 metadata before 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。