2,351 件 ・ 関連度順
概要と使いどころ
Detect anomalies in DNP3 (Distributed Network Protocol 3) communications used in SCADA systems by monitoring for unauthorized control commands, firmware update attempts, protocol violations, and deviations from baseline traffic patterns using deep packet inspection and machine learning approaches.
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。
Deep reflection skill - read memory and context, decide whether there is something worth sending
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。
Shared vocabulary for designing deep modules, interfaces, seams, adapters, leverage, and locality. Use when designing or reshaping module boundaries, making code more testable, or evaluating interface depth.
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 skills in any project using a dual-axis method: (1) deterministic code-based checks (structure, scripts, tests, execution safety) and (2) LLM deep review findings. Use when you need reproducible quality scoring for `skills/*/SKILL.md`, want to gate merges with a score threshold (for example 90+), or need concrete improvement items for low-scoring skills. Works across projects via --project-root.
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 this skill for critical code review that focuses on whether the code actually works correctly and achieves the expected results - not just whether it matches a design document. This skill assumes designs can be wrong, finds bugs the design didn't anticipate, and verifies end-to-end correctness. Triggers include "critical code review", "deep code review", "verify this implementation works", "find bugs in this code", or reviewing implementation against requirements. NOTE - Security review is OUT OF SCOPE; use a dedicated security review skill for that purpose.
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。
Android device offensive security, forensic extraction, and mobile app exploitation. Use this whenever the user needs to: break into or bypass the lock screen of an Android phone, extract data from a seized Android device for evidence, analyze or reverse-engineer an APK to find vulnerabilities, intercept communications on an Android device, exploit Android apps via deeplinks, steal crypto wallets or app data, set up rogue WiFi to target a phone, or plan any mobile device investigation or penetration test. Also trigger when the user mentions Android forensics, mobile pentesting, law enforcement phone extraction, device exploitation, WhatsApp or Telegram extraction from a phone, Android app reversing, CVE research for Android, or needing a chain of custody for mobile evidence. Even if the user describes the scenario casually or in another language, if they need to gain access to or pull data from an Android phone, use this skill.
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。
Compare standardized metrics across multiple ego-benchmark-harness runs by recalculating them from tasks.jsonl and session JSONL, never from HTML reports. Covers overall aggregates (success rate/average score), Odysseys per-rubric pass rates, duration distributions (avg/p50/p90/max), turns/tools/cost, error and exception counts, per-task success matrices, helper distributions, and the share of DOM operations. Use whenever users ask to compare data or metrics across runs, calculate aggregate/Odysseys/duration/turn/error/helper metrics, inspect differences among runs, or compare multiple rwb/real-world-bench runs. Use pi-session-analyzer instead for deep forensic analysis of why an individual task was slow or failed.
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。
Mitigate incidental code complexity when control flow is tangled, nesting is deep, names are hard to parse, or reasoning requires cross-file hops. Use when a review stalls on readability, you need an analysis-first refactor plan before edits, or you want essential-vs-incidental verdicts, ranked simplification steps, a small structural sketch, and a TRACE assessment (analysis-only; no edits).
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。
Interview the user deeply about a plan, probing technical implementation, UI/UX, risks, tradeoffs, and missing decisions before writing or refining a spec. Use when the user says "interview me" or asks to be questioned about a plan.
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。
Iterative deep planning with critiques and alternatives. Use when facing complex design decisions, cross-platform changes, native module modifications, or API changes requiring thorough analysis.
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Load only when the user asks for Robin/Rodin neutral mode, Socratic intellectual sparring, steelmanning, anti-echo-chamber critique, or a deep societal/political/religious/economic discussion; not for coding, factual lookup, or ordinary advice.
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。
Load when editing Blazor `.razor.css`, scoped selectors, `::deep`, BEM class names, RTL/logical CSS properties, or styling nested MudBlazor components; not for global design tokens or non-Blazor CSS.
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Audit code quality focusing on deep modules, naming, model hygiene, and architectural clarity. Use after a burst of development, when codebase structure needs assessment, or to make code more agent-navigable.
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。
Deep expertise in cmux — the terminal multiplexer with native browser views. Use when managing panes, reading terminal output, sending keystrokes, opening browser views, or manually testing web UIs and TUIs inside cmux. Triggers on: cmux, open a browser pane, split terminal, read screen, send keys, test this UI in cmux, preview in cmux.
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。
Run an extremely strict maintainability review for abstraction quality, giant files, and spaghetti-condition growth. Use for a thermo-nuclear code quality review, thermonuclear review, deep code quality audit, or especially harsh maintainability review.
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.
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 NASA Digital Engineering Acquisition Framework Handbook (NASA-HDBK-1004). Use for acquiring digital/model-based data under contract: the Data Requirements Description (DRD) instrument and the Appendix A DRD suite (TDP, MBx, CDMP, CSA, CR, FCA/PCA, SDT, EMDAL, REF, MRI); the Data Type 1-5 control scale; SOW/RFP/Cost-Volume contract language and data rights (FAR/DFARS, Table 3 license categories); the Requirements Exchange Format (REF/ReqIF, UUID schema) and Master Records Index; model-based data definition (MBSE, CAD/PMI, M&S credibility, BOM, release-state mapping, PLM); digital data collaboration, the four owned architectures, and interoperability standards (MIL-STD-31000, STEP/ISO 10303, ASME Y14); the four MBE interoperability use cases; and MBE Plan development. Scope is the acquisition and contracting side of digital engineering. Does not reproduce ISO/STEP/ASME/MIL-STD/SAE standard text, and the companion NASA-HDBK-1009A carries the deeper digital-engineering practitioner detail this handbook defers to.
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 DoD Manufacturing Readiness Level (MRL) Deskbook (Version 2022, OSD ManTech / Joint Service–Industry MRL Working Group). Use for assessing and managing manufacturing maturity and producibility risk in defense acquisition: the 10-level MRL scale and its non-linear, target-not-grade nature; the five demonstration environments (laboratory → production-relevant → production-representative → pilot line → production line); the nine manufacturing-risk threads and sub-threads; how MRL targets map onto Adaptive Acquisition Framework milestones (MRL 4/6/7/8/9); conducting an MRL Assessment; building a Manufacturing Maturation Plan; putting MRL maturity into contract language (Section L/M, SOW, AS6500A, DIDs); tailoring criteria for S&T, single-system, ship, sustainment, and industry contexts; and screening operational-technology cybersecurity. Manufacturing-readiness focused — it measures producibility risk and frames mitigation; it is NOT a technology-readiness (TRL) guide, a full systems-engineering process model, a cost-estimating or schedule guide, or a deep cybersecurity audit method (see sibling packs for those).
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 DoD Test & Evaluation Enterprise Guidebook (August 2022 edition 8.02) with DOT&E-facing enterprise emphasis. Use for T&E enterprise roles (DOT&E vs USD(R&E)), DT&E/OT&E/LFT&E, cybersecurity T&E, MOSA/automation/digital enablers, suitability/reliability growth, and TEMP/Strategy/STE planning. Distinct routing from dod-te-guidebook (same guidebook family, pathway-deep pack). Guidance only—defer to DoDI 5000.89 and Title 10 for binding requirements.
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。
Tiefe Pflegerunde für ein veröffentlichtes GitHub-Repository (Stufe 2): enthält den vollständigen surface-after-care-Durchlauf und ergänzt ihn um drei teure Schritte — rechtliche Ersteinschätzung über die Law-Checker mit Wiedervorlage nach einem Jahr (Gutachten bleibt gitignored im Repo), Querverweise zu verwandten Repos über ALLE Organisationen hinweg sowie das Nachziehen aller Sprachen auf App-Ebene, nicht nur in der Doku. Nutze diesen Skill bei "full after care", "deep after care", "tiefe Repo-Pflege", "große Runde", "Repo grundlegend durchgehen", wenn ein Repo länger nicht geprüft wurde, vor größeren Releases oder wenn rechtliche Relevanz, Querverweise oder Mehrsprachigkeit ausdrücklich Thema sind. Für die günstige, oft wiederholte Runde stattdessen surface-after-care; für die Erstveröffentlichung github-repo-care.
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。
Collective research memory for AI coding agents — search prior research before hitting the web, save findings back to the shared cache, view karma stats. Use when the user asks a library/API/framework question, before running WebSearch/WebFetch, or after deep web research so the next agent doesn't repay the cost. Talks directly to the hosted MCP endpoint via JSON-RPC — no local MCP server required.
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。
複数事業セグメントを持つ企業の、セグメント別の収益性・成長性・資産配分を深掘りする時に使う。「〇〇のセグメント別採算を教えて」「どの事業が稼いでいるか」のような、事業構成に着目した指示で発火する。
Weave a new node (concept, framework, theory, project, principle) into the whole vault graph rather than just creating a note. Mode A links one new node against the vault by top-K retrieval, with the LLM judging only those candidates; mode --deep finds orphaned islands and wires them in. Backup before edits, preview before writing. Triggers: "/relink", "integrate this concept".
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 a dossier on a person: recall everything the vault already knows, map their public profiles by web research, emit a deep-research prompt for external tools, and persist a well-linked person card. Triggers: "/portret <name>", "dossier on <name>", "profile <name>".
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。