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概要と使いどころ
URLScan.io is a free service for scanning and analyzing suspicious URLs. It captures screenshots, DOM content, HTTP transactions, JavaScript behavior, and network connections of web pages in an isolat
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。
Analyzes encryption algorithms, key management, and file encryption routines used by ransomware families to assess decryption feasibility, identify implementation weaknesses, and support recovery efforts. Covers AES, RSA, ChaCha20, and hybrid encryption schemes. Activates for requests involving ransomware cryptanalysis, encryption analysis, key recovery assessment, or ransomware decryption feasibility.
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。
Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.
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Analyzes malware command-and-control (C2) communication protocols to understand beacon patterns, command structures, data encoding, and infrastructure. Covers HTTP, HTTPS, DNS, and custom protocol C2 analysis for detection development and threat intelligence. Activates for requests involving C2 analysis, beacon detection, C2 protocol reverse engineering, or command-and-control infrastructure mapping.
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。
Reverse engineer Go-compiled malware using Ghidra with specialized scripts for function recovery, string extraction, and type reconstruction in stripped Go binaries.
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。
Review product designs before engineering handoff or release for flow and state coverage, business-rule alignment, implementation clarity, accessibility, responsiveness, analytics, and testability.
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Comprehensive data science workflow skill for analyzing tabular and time series data. Use when conducting exploratory data analysis, building predictive models, performing feature engineering, or generating data-driven insights. Supports end-to-end analysis from data understanding through model deployment, following industry best practices for regression, classification, time series forecasting, and statistical analysis.
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Use when developing, reviewing, debugging, porting, or engineering Linux kernel device drivers, kernel modules, device tree bindings, bus clients, subsystem drivers, DMA/IRQ/memory paths, userspace interfaces, or hardware bring-up plans.
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Use when designing, reviewing, implementing, or validating UI/UX and frontend experiences for web, mobile, responsive apps, design systems, Figma-to-code handoff, frontend stack/library selection, personas, UX mapping, information architecture, UI patterns, accessibility, interaction design, UX research, usability testing, theme/token application, or XR/spatial interfaces.
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Use when performing cybersecurity operations - offensive or defensive - including threat detection and hunting, SOC operations, incident response, digital forensics and DFIR, malware analysis and reverse engineering, threat intelligence, cloud/container/network/application/API security, identity-access and zero-trust, OT/ICS and endpoint security, vulnerability and supply-chain management, DevSecOps, cryptography, AI security, compliance and governance, and red-team or penetration testing under signed rules of engagement; routes to one of 800+ technique playbooks mapped to MITRE ATT&CK, D3FEND, and NIST CSF.
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Use when designing, reviewing, modifying, migrating, optimizing, securing, operating, troubleshooting, or decommissioning data architectures across data model exploration, SQL, ISO SQL conformance, NoSQL, vector, graph, document, time-series, NewSQL, search, in-memory, SQLite, CDC/Debezium, DBMS storage/query/recovery internals, database incident prevention, hardware acceleration, cache, big-data, lakehouse, and warehouse systems.
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IDS/IPS detection content for a CVE — Snort/Suricata-compatible rules, YARA signatures, ProjectDiscovery Nuclei templates, traffic-filter rules. Capability-aware: skips families when the binary is not installed (no Snort = no Snort output). Use when deploying defences for a CVE without a patch, augmenting SAST with active detection, or feeding the SOC engineering pipeline.
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。
A six-year-old code reviewer who asks "why?" about everything. Catches over-engineering, confusing names, and unnecessary complexity, then corrects the code according to the language spec, but through kids eyes, maintaining simplicity and readability.
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A six-year-old code reviewer who asks "why?" about everything. Catches over-engineering, confusing names, and unnecessary complexity. Output is a 1-2 line summary of the request as a whole: how is the file? Is the class nice? Is the function correct?
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。
A six-year-old code reviewer who asks "why?" about everything. Catches over-engineering, confusing names, and unnecessary complexity. To invoke, say `6yo`.
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。
XP-style plan review that identifies gaps, hidden assumptions, and critical misses. Triggers on 'review the plan', 'check the plan', 'audit the plan'. Reviews plans for YAGNI violations, missing feedback loops, and over-engineering.
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Create a study cheatsheet by extracting and synthesizing key concepts from lecture materials, homework, and past exams. Use this skill whenever a user asks for help making a cheatsheet, study sheet, formula sheet, exam reference sheet, or "one-pager" — especially when they upload lecture slides (PDF/PPTX/DOCX), problem sets, past exams, or scanned/handwritten notes. Trigger this even for casual phrasings like "help me prep for my final," "summarize my course materials into something I can bring to the exam," or "make me a study reference from these slides and homeworks." Tuned for math/science (formulas, proofs, theorems) and CS/engineering (code, algorithms). Processes one file at a time and persists intermediate summaries to disk to avoid blowing past the context window on large courses.
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Knowledge base from the SD-22 DMSMS Guidebook (DSPO, FY24 working draft). Use for managing Diminished Manufacturing Sources and Material Shortages (DMSMS) and obsolescence across a long-lived system's life cycle: the five-step process (Prepare, Identify, Assess, Analyze, Implement) plus the Strategize overlay; standing up a DMSMS Management Team (DMT) and Management Plan (DMP); risk-based monitoring and surveillance (the risk cube, predictive tools, vendor surveys, GIDEP/DLA PDNs, critical-materials analysis); health assessments and resolution timing; costing and selecting resolutions (life-of-need buys, substitutes, redesign, technology refreshment) by AoA/BCA; and budgeting/executing resolutions through modification funding and the ECP process. Defense-sustainment / DoD-acquisition framing throughout. Does NOT teach general systems engineering, parts-management standards, or contracting law in depth, and does not reproduce DoD policy text or the source's tables and figures.
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Knowledge base from NIST SP 800-37 Revision 2, the Risk Management Framework (RMF) for information systems and organizations. Use for the seven RMF steps (Prepare, Categorize, Select, Implement, Assess, Authorize, Monitor) and their tasks; the three-tier organization-wide risk model; authorization boundaries; the requirements-to-controls relationship; security categorization (FIPS 199/200 high-water mark) and control selection/tailoring (SP 800-53/53B baselines); assessment, the authorization decision and risk acceptance, continuous monitoring and ongoing authorization; the security/privacy distinction; and supply chain risk management. This is the security-and-privacy-risk-governance edge of systems engineering — it governs how much protection and whether to accept residual risk, complementing the disciplines that decide how protection is built. Does NOT reproduce the SP 800-53 control catalog or its baselines, the FIPS impact tables, or the ISO/IEC/IEEE 15288 process text; it names and routes to them rather than restating them.
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Orchestrator (not a knowledge pack): routes a free-text systems-engineering question to the right catalogue pack(s), reads them with you, and answers with pack and chapter citations. Runs only on explicit `/se <question>`; carries no source content. Use when you do not know which `/pack` to ask.
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 NIST Framework for Cyber-Physical Systems (SP 1500-201/202/203, v1.0, 2017). Use for engineering and analyzing cyber-physical systems (CPS) and IoT: the CPS Framework's aspect × facet grid (nine aspects of concern examined through conceptualization, realization, and assurance facets), cross-property trustworthiness risk management (safety/security/privacy/reliability/resilience together), data interoperability (syntactical/semantic/contextual, canonical models, identifiers, provenance), the timing aspect (time-interval classes, time domains, time-aware networking) and secure/resilient time (GNSS jamming/spoofing, PTP/NTP attacks, holdover, the clock-error model and Allan/MTIE metrics), plus the use-case requirements method. Bridges IT and OT under a clock. Scope limits: a 2017 living/draft document, thin on post-2017 standards and on detailed control catalogs; it names but does not reproduce ISO/IEC/IEEE 42010 and 15288 process text or permission-licensed third-party figures.
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。
Signpost (not a knowledge pack) for the systems-engineering standards landscape — ISO/IEC/IEEE 15288, 24748, 29148, 42010, 15026, 24765; INCOSE SE Handbook & Vision 2035; OMG SysML; SAE/EIA 1001/649/748; ECSS space standards; NATO AAP-48; CMMI; NIST SP 800-160; and more. Contains NO source content: it lists each standard's designation, title, purpose, owning SDO, whether it's redistributable, and where to get it. Use when you need to identify or locate an SE standard — most are paywalled/all-rights-reserved and cannot be packaged, so this points you to the owner (and to the open packs where one exists).
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 NIST/SEMATECH e-Handbook of Statistical Methods (NIST HB 151) — the practical statistics reference for engineering, metrology, and quality. Use for: exploratory data analysis and the four univariate assumptions (4-plot); measurement process characterization including bias/precision, calibration designs, gauge R&R, and ISO/GUM uncertainty budgets; production process characterization (stability vs. capability); process modeling and regression (LS/WLS/NLS/LOESS); design of experiments (screening, fractional/factorial, response-surface, Taguchi); statistical process control (Shewhart/CUSUM/EWMA charts, capability indices, acceptance sampling); product/process comparisons (hypothesis tests and confidence intervals for 1/2/3+ groups, ANOVA, multiple comparisons); and reliability (lifetime & repair-rate models, accelerated testing, reliability growth). Scope limits: this is applied frequentist statistics for measurement and quality — it does NOT reproduce the per-distribution formula galleries, worked case studies, datasets, plot images, or Dataplot/R code of the original web Handbook (those are described, not copied); it is thin on modern machine learning, Bayesian methods beyond conjugate reliability priors, time-series/forecasting, and Bayesian experimental design.
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 NASA Human Systems Integration (HSI) Practitioner's Guide (NASA/SP-2015-3709). Use for HSI process implementation, lifecycle phase activities, function allocation, ConOps development, HSI Plan writing, HSI metrics (MOE/MOP/TPM/KPP), HITL testing strategy, HSI team organisation, scaling/tailoring for program size, and V&V planning. Does not cover the specific human engineering design standards (NASA-STD-3001 content) or detailed HFE methods beyond what the guide itself provides.
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