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概要と使いどころ
Knowledge base from MIL-STD-882E (DoD System Safety). Use for the system safety process, hazard analysis tasks (PHA/SSHA/SHA/O&SHA), the risk assessment matrix (severity x probability), software safety criticality, and risk acceptance. US-gov public domain.
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Knowledge base from the OUSD(R&E) guidebook Implementing a MOSA in DoD Programs (Feb 2025). Use for the Modular Open Systems Approach as a DoD acquisition + design discipline: the statutory/regulatory basis (10 U.S.C. 4401–4403, FY2017/FY2021 NDAA, DFARS), the five OUSD(R&E) pillars, the plan→modularize→identify→define→standardize interface lifecycle, technical levers (WBS/MIL-STD-881F taxonomy, standards profiling, API-first software, COTS/cybersecurity), MOSA assessment metrics and tools (PART/OAAT/KOSS/SEAM/MAUT), technology-change and DMSMS management, roadmaps, MOSA across the six AAF pathways, and contracting/IP/data-rights mechanics. SCOPE LIMITS: this is U.S. DoD weapon-system acquisition policy, not a general open-architecture or interface-standards reference — it names standards (FACE, OMS, DISR) without reproducing them, is light on detailed engineering of any specific standard, and does not cover the underlying SE processes (use dau-se-guidebook / nasa-npr-7123) or general systems-engineering theory (use sebok).
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Knowledge base from EU Artificial Intelligence Act — Regulation (EU) 2024/1689. Use for AI system classification (high-risk vs prohibited vs general), conformity assessment procedures, GPAI model obligations, transparency and disclosure requirements, governance structure (AI Office / AI Board), penalty tiers, value-chain operator obligations, post-market monitoring, and Annex III use-case analysis. Does not cover EU liability rules for AI (separate proposed directive) or implementation guidance documents issued after July 2024.
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Knowledge base from the NASA Probabilistic Risk Assessment Procedures Guide (NASA/SP-2011-3421, 2nd ed.). Use for quantitative PRA of aerospace and safety-critical systems: the risk triplet and scenario logic stack (MLD, ESD, event trees, fault trees, minimal cut sets), Bayesian data collection and parameter estimation, aleatory/epistemic uncertainty modeling and common-cause failure (Alpha Factor, beta-factor, CCBE), human reliability analysis (THERP, CREAM, NARA, SPAR-H), context-based software risk (CSRM), physics-based and structural/phenomenological models (stress-strength, limit states, FORM/SORM, NASGRO, range safety), uncertainty propagation and importance measures (F-V, RAW, Birnbaum, DIM), and launch-abort modeling with worked PRA examples. This is the QUANTITATIVE engine beneath NASA's risk doctrine — for the qualitative RIDM/CRM decision framework use the nasa-risk pack. Thin on programme management, organisational risk governance, and non-aerospace regulatory contexts; aerospace-focused and anchored to the 2011 second edition.
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Evidenzbasierte Windows-Speicherbereinigung mit kontrolliertem Notfallmodus, physischer Belegungsmessung, Prozess- und Entstehungsforensik sowie reversiblen Maßnahmen gegen wiederkehrende Speicherfüller.
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Neutrale moralisch-rechtliche Prüfinstanz für Politik und Entscheidungen — der lauffähige Prototyp des Forschungsprojekts "Die Position der Ungeborenen" (Schattenmodus Stufe 1). Nutze diesen Skill, wann immer eine politische Entscheidung, ein Gesetz(entwurf), eine Reform, ein Haushaltsbeschluss oder eine gesellschaftliche Streitfrage analysiert, geprüft oder begutachtet werden soll — auch bei Formulierungen wie "prüfe aus Sicht künftiger Generationen", "Gesetzespass", "was sagt das Grundgesetz dazu", "Superposition-Prüfung", "Gesetzesgeschichte/Containeranalyse", "Folgenabschätzung", "analysiere diese Reform", "lebende Verfassung" oder wenn der User eine Politik-Fragestellung mit Bitte um neutrale, mehrstufige Bewertung einbringt. Orchestriert die 5-CORE-Architektur (config.json): moralische Superposition-Instanz, Gesetzbuch-Verkörperungen, zweistufige Folgenabschätzung (retrospektiv/prospektiv mit Evidenzhierarchie), Wissens-Handler mit lokalem Speicher, einstellbarer Workflow.
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Points to the standalone module law-checker ("Legal Department"): source-grounded AI first-look legal assessments for German law with a statute registry and a statute-embodiment agent. Use this skill when a situation, contract, official notice, or legal question under German law should be checked with exact citations (article/section, paragraph, sentence) -- with a clear boundary: AI-assisted first orientation, not a substitute for a lawyer.
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Pre-fills a customer's or prospect's security or third-party risk questionnaire from the organisation's own answer sources (prior questionnaires, answer library, policies), returning one row per question with a draft answer marked Reused, Adapted, Missing or Routed, its source, date, scope match and owner, plus the Missing list by owner. Never invents an answer, certification or control and never submits. Use when the user asks to "pre-fill this security questionnaire", "take a first pass at the customer's due diligence form", "answer this vendor assessment from our previous responses", "populate the third-party risk questionnaire" or "which questions can we reuse answers for". Do not use for screening a vendor's own answers, use vendor-risk-screening-brief instead; for RFP or tender answers use rfp-response-drafter. Drafts for human review; never approves, authorises or signs off.
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Reviews one employee software request (tool, purpose, users, data handled, cost, urgency) against the approved-tools list and the policies the user provides and returns a draft review for the reviewer: match state on the list with the row quoted, approved tools the list itself describes as covering the same need, policy clauses that bear on the request with quoted text, questions for the requester and the reviewer, and one suggested decision with its basis. Never approves, procures, installs or adds a tool to the list. Use when the user asks to "review this software request", "is this tool on the approved list", "check this request against our software policy", "do we already have something approved that does this" or "prepare the decision note for this tool request". Do not use for a full vendor security assessment, use vendor-risk-screening-brief instead; for sorting mixed requests, use request-intake-triage. Drafts for human review; never approves, authorises or signs off.
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Builds a DRAFT course outline from a training need and audience: learning objectives with an observable verb, condition and standard, modules in a stated order with a duration basis and sources, activities and an assessment plan aligned objective by objective, and a coverage table showing what each objective still lacks. Never sets a pass mark, declares learners competent or invents content. Use when the user asks to "build a course outline", "design a training programme for", "turn this need into modules and objectives", "draft the curriculum for this workshop" or "what should this training cover". Do not use for quiz items from finished content, use training-quiz-builder instead; for needs from surveys and manager notes, use training-needs-synthesis; for a new starter's first weeks, use onboarding-plan-builder. 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。
Builds a DRAFT quiz from the training content the user supplies (slide deck, manual, e-learning script, session transcript, procedure): numbered questions with the correct answer, distractors drawn from the content itself, an explanation quoting the source passage with its location, a difficulty tag (Recall, Apply, Analyse) with its basis, and a coverage table showing which sections and objectives each question tests and which are left uncovered. Use when the user asks to "build a quiz from this deck", "write knowledge check questions for this module", "create an assessment from this training", "test understanding of this procedure" or "which sections does this quiz cover". Do not use for a reader-facing question and answer page built from documents, use faq-builder instead; for writing the procedure itself from notes or a walkthrough, use sop-drafter. 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。
Flags where a P&ID draft raises a process safety question and points to the candidate evidence an engineer would review (HAZOP register, relief study, SIL assessment, isolation philosophy), as questions and quoted passages with UNKNOWN for missing documents, plus safety functions, relief devices and isolation requirements carried over as stated. Never rates, sizes, classifies, resolves or declares protection adequate. Use when the user asks to "write the process safety flags", "produce the process safety section of the P&ID enrichment", "list which protection questions this P&ID raises" or "map the HAZOP and relief study evidence to the draft" after gate G2, once the piping and instrumentation sections exist. Do not use for proposing loops, alarms or trips from the control philosophy, use instrumentation-and-control-enrichment instead; do not use for checking identifiers, use tagging-and-numbering. Drafts for human review; never approves, authorises or signs off.
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- Turn findings into a defensible intelligence product — BLUF key judgements, standardised estimative probability language, per-claim sourcing with timestamps and archived copies, separated observation, inference and assessment,… Use when writing an intelligence report, due-diligence memo, evidence pack or executive summary, or when asked to write up an investigation so it survives challenge
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Use to audit a generated or heavily templated interface for generic AI aesthetics, unjustified trend patterns, component-registry residue, vague copy, repetitive motion, novelty theater, or visual decisions that lack brand/UX rationale.
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Authorized security assessment of REST, GraphQL, WebSocket, and SOAP APIs: discovery, authentication and authorization flaws (BOLA/IDOR, JWT/OAuth), rate-limit testing, and a structured multi-phase me
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Access matrix of role-by-module permissions, with per-role scope, confidentiality level and SME tier, as CSV, SQL, JSON Schema or Notion on request. Use for access reviews.
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Master orchestrator for B2B SaaS content creation and the editorial production system behind it. Use this skill to produce blog articles, whitepapers, case studies, video scripts, newsletters and ghostwritten thought leadership, to develop content strategy, and to run the publishing line itself — content briefs, editorial calendars and production capacity, review and approval workflow, subject-matter-expert access, the style guide, an AI-drafting policy, and a content inventory with owners. Also triggers on: blog, article, case study, whitepaper, newsletter, video script, ghostwriting, content calendar, editorial, thought leadership, content strategy, content brief, content operations, content ops, editorial workflow, managing editor, who reviews this, our content pipeline is stuck, content is stuck in review, content audit, content inventory, content governance, style guide, house style, SME interview, expert interview, freelance writers, AI content policy, how much content can we actually produce.
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-level guidance for Flutter production applications: architecture decisions (Clean/Layered/Hexagonal/Feature-First), advanced state management (BLoC/Riverpod/GetX), performance optimization (eliminating jank, memory profiling, reducing rebuild overhead), code review with design pattern enforcement, scalability assessments, native platform integration (MethodChannel/EventChannel/FFI), complex UI challenges (custom render objects, slivers), testing strategies (unit/widget/integration/golden), CI/CD pipeline architecture, build optimization, and accessibility compliance. Triggers: 'architecture decision', 'design pattern', 'performance issue', 'code review', 'production problem', 'scaling concerns', 'best practices', 'technical debt', 'refactoring strategy'. Essential for complex enterprise apps, critical production debugging, architectural refactors, or when choosing between competing technical approaches.
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Use when performing security audits, vulnerability assessments, penetration testing, or when asked to find security issues. Use when encountering code that handles user input, executes commands, reads files, or makes network requests.
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生成AIで書いた記事・スライド・文書の「AI臭」を徹底除去するスキル。 「なんかAIっぽい」「無機質」「整いすぎ」と感じる文章を人間の体温が宿る文体へ変換する。 記事・スライドのリライト依頼、AI臭チェック、「もっと人間らしく書いて」という要求で積極的に使用する。
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on re...
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Write clean, readable, and maintainable code following principles from Robert C. Martin's "Clean Code" and Object Calisthenics. Use when writing, reviewing, or refactoring code to improve naming, function design, formatting, error handling, and class structure. Includes code smell detection and refactoring guidance.
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Use when working with Crowdstrike — crowdStrike Falcon endpoint detection and response, threat intelligence, host management, and incident investigation. Covers detection queries, host inventory, IOC searches, real-time response sessions, and vulnerability assessment. Use when investigating security incidents, reviewing endpoint health, analyzing threat detections, or managing CrowdStrike Falcon sensors.
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Use when working with Codeball — codeBall AI-powered pull request risk assessment including automatic PR quality scoring, risk classification, and auto-approval for low-risk changes. Use when configuring CodeBall for automated PR triage, risk-based review routing, and review workload optimization.
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。