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概要と使いどころ
Plan and execute Convex schema migrations safely, including adding fields, creating tables, and data transformations. Use when schema changes affect existing data.
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。
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
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Transform Claude Code into a fully autonomous agent system with persistent memory, scheduled operations, computer use, and task queuing. Replaces standalone agent frameworks (Hermes, AutoGPT) by leveraging Claude Code's native crons, dispatch, MCP tools, and memory.
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Integracao completa com Amazon Alexa para criar skills de voz inteligentes, transformar Alexa em assistente com Claude como cerebro (projeto Auri) e integrar com AWS ecosystem (Lambda, DynamoDB, Polly, Transcribe, Lex, Smart Home).
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Detect lateral movement in network traffic using Zeek (formerly Bro) log analysis. Parses conn.log, smb_mapping.log, smb_files.log, dce_rpc.log, kerberos.log, and ntlm.log to identify SMB file transfers, NTLM account spray activity, remote service execution, and anomalous internal connections.
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Detecting data exfiltration attempts from AWS S3 buckets by analyzing CloudTrail S3 data events, VPC Flow Logs, GuardDuty findings, Amazon Macie alerts, and S3 access patterns to identify unauthorized bulk downloads and cross-account data transfers.
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Business Email Compromise (BEC) is a sophisticated fraud scheme where attackers impersonate executives, vendors, or trusted partners to trick employees into transferring funds, sharing sensitive data,
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Detects prompt injection attacks targeting LLM-based applications using a multi-layered defense combining regex pattern matching for known attack signatures, heuristic scoring for structural anomalies, and transformer-based classification with DeBERTa models. The detector analyzes user inputs before they reach the LLM, flagging direct injections (system prompt overrides, role-play escapes, instruction hijacking) and indirect injections (encoded payloads, multi-language obfuscation, delimiter-based escapes). Based on the OWASP LLM Top 10 (LLM01:2025 Prompt Injection) and Simon Willison's prompt injection taxonomy. Activates for requests involving prompt injection detection, LLM input sanitization, AI security scanning, or prompt attack 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。
Build a systematic threat hunt hypothesis framework that transforms threat intelligence, attack patterns, and environmental data into testable hunting hypotheses.
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Transform client hearing sheets and meeting notes into structured requirements documents. Use when converting raw hearing data (Japanese/English) into formal requirements, generating traceability matrices, identifying gaps/ambiguities, and mapping requirements to WBS items. Triggers include "hearing to requirements", "meeting notes to specs", "requirements traceability", "gap analysis for requirements", or requests involving hearing sheet 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。
Refactor Swift code toward declarative functional programming with immutability, value types, pure functions, transformations, and closure-based APIs. Use when asked for functional refactoring or `/refactor:functional`.
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End-to-end data pipeline for empirical research: fetch economic data from APIs (FRED, World Bank, IMF, BLS, OECD, Yahoo Finance), clean and transform raw data, construct strategy-specific variables, and validate panel structure. Use when asked to fetch data, download data, clean data, merge datasets, prepare analysis-ready data.
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Signpost (not a knowledge pack) for OMG modelling specifications — UML, SysML, BPMN, UAF, SoaML, MOF, OCL, XMI, DDS. Contains NO source content: it only points to where the official specs live and what each is for. Use when you need an OMG modelling/architecture specification — the OMG licence forbids redistributing or transforming the text, so these cannot be reconstituted into a pack; download them from OMG directly.
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Knowledge base from the DoD Risk, Issue, and Opportunity (RIO) Management Guide for Defense Acquisition Programs (OUSD R&E, Sep 2023 incl. Change 2.2). Use for the defense-acquisition mechanics of program risk management: the RIO definitions (risk/issue/opportunity), the five-step risk process (plan-identify-analyze-mitigate-monitor), 1-5 likelihood/consequence scoring and the 5x5 risk matrix, the four mitigation options (accept/avoid/transfer/control), burn-down plans, issue management (probability=1), opportunity management toward should-cost, cross-program/interface risk, tailoring RIO to the six AAF acquisition pathways (UCA/MTA/MCA/Software/DBS/Services), specialized methods (RMF, MBCRA/Cyber Table Top, Agile metrics, FMECA, ITRA/DTRAM), and institutionalizing RIO via the PRMP, boards (RMB/JRMB/ROMB/RWG), tools, tiered roles, and WBS/IMP/IMS/EVM/TPM integration. This is the DoD-acquisition complement to nasa-risk (general CRM/RIDM theory) and dau-se-guidebook (the SE process frame). Does NOT teach general risk theory from scratch, and defers specialized risk regimes (system safety/ESOH, HSI, detailed cyber/RMF, spectrum) to their own DoD instructions and the relevant packs.
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Knowledge base from the DoD Digital Engineering Strategy (2018, OUSD/ODASD(SE)). Use for the Department of Defense's five digital engineering goals — (1) formalize the development, integration, and use of models; (2) provide an enduring authoritative source of truth; (3) incorporate technological innovation; (4) establish supporting infrastructure and environments; (5) transform the culture and workforce — plus their focus areas, the document-to-model and design-build-test→model-analyze-build shifts, model formalisms/provenance, governance and access control of the authoritative source of truth, and the coordinate→plan→pilot→sustain rollout. A vision-and-policy strategy, deliberately non-prescriptive. Does NOT provide a how-to method, tool tutorials, MBSE/SysML mechanics, or implementation-plan content; thin on metrics, acquisition-policy detail, and step-by-step procedure. Excludes the CAC-gated Digital Engineering Body of Knowledge (DEBoK).
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Verdichtet gebündelte offene Entscheidungen zu EINEM zusammenhängenden, prosa-basierten Gesamtkonzept auf Basis aller empfohlenen Varianten. Zeigt, wie die Teilsysteme nach der Umsetzung konkret ineinandergreifen, statt den Nutzer durch Dutzende isolierte Einzelfragen zu zwingen. Der Nutzer nimmt das Bild als Ganzes ab oder gibt zielgerichtete Korrekturen, die automatisch in Einzelentscheidungen übersetzt und über decision-briefing Phase 4 ins Register zurückgeschrieben werden. Nutze diesen Skill bei "wie sieht das Ganze aus wenn wir allem zustimmen", "zeichne mir das Gesamtbild", "Gesamtkonzept der Entscheidungen", "decision draw", /decision-draw, "wie greifen die Systeme zusammen". NICHT nutzen, wenn Alternativen pro Einzelpunkt diskutiert werden sollen -- das macht decision-briefing; NICHT für den Abhängigkeitsgraphen -- das macht decision-path; NICHT für strukturierte Einzelblöcke mit Optionen und Pro/Contra (auch bei eng zusammengehörigen Gruppen) -- das macht decision-shot; NICHT zur Herleitung einer einzelnen Entscheidung -- das macht decide.
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Parallel geführte Sprachfassungen eines Dokuments (Paper DE/EN, README + README_de, SKILL.md + SKILL.en.md, Website-Texte) synchron halten: fehlende Fassung nachziehen, Abschnitts-Parallelität prüfen, Divergenzen beheben — mit klarer Leitsprache-Regel und kontrolliertem Rücktransfer, wenn die Nebenfassung etwas besser löst. Nutze diesen Skill bei „sind DE und EN synchron?", „zieh die englische/deutsche Version nach", „Übersetzung ist veraltet", bei zweisprachigen Papers/READMEs/Skills, oder als periodischen Check über einen Dokumentbestand. Enthält auch das Expansions-Audit: bewerten, ob ein Projekt/Dokument WEITERE Sprachen verdient (i18n-Eignung nach Zielgruppe, technische Vorbereitung, kein blindes Massenübersetzen).
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Local, private capture of medical data: diagnoses, symptom histories and examination plans. No BACH origin — custom design with its own SQLite store. Strictly local, no cloud transfer.
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Regelmäßiger Pflegedurchlauf für ein bereits veröffentlichtes GitHub-Repository (Stufe 1, günstig und oft wiederholbar): zuerst alle Distributionsflächen des Projekts ermitteln (npm, PyPI, Registries, Marketplaces, Stores, Website) und Änderungen später dorthin spiegeln, dann Topics setzen, Privacy-Gate, Dokumente auf Veröffentlichungsabsicht prüfen und interne Planungsdateien nachträglich ignorieren, Banner ergänzen, Aussagen im README gegen den echten Code-Stand abgleichen, Darstellung verbessern, Sprachfassungen der README vervollständigen, Sichtbarkeitsmaßnahmen umsetzen, Eintrag auf der Organisationsseite prüfen sowie offene Issues und Pull Requests abarbeiten. Nutze diesen Skill, wenn ein bestehendes Repo gepflegt, aufgeräumt, aktualisiert, aufgehübscht oder "mal wieder durchgesehen" werden soll, wenn ein Repo veraltet oder unaufgeräumt wirkt, bei Formulierungen wie "Repo-Pflege", "after care", "Nachpflege", "Repo auf Stand bringen", "aufräumen und pushen" oder bei rotierenden Qualitätsrunden über mehrere Repos. Für die tiefe Runde inkl. Rechtscheck und orga-übergreifenden Querverweisen stattdessen full-after-care nutzen; für die Erstveröffentlichung github-repo-care.
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Reads one export transaction, order or shipment description and returns a DRAFT export review pack: parties to screen with a screening checklist, an item classification worksheet, a red-flag review, licence-determination questions and open items. Use when the user asks to "prepare the export review for this order", "pre-check this shipment for export control", "who do we need to screen on this deal", "build the classification worksheet for this item" or "assemble the trade-compliance file for this technology transfer". Do not use for vendor security or due-diligence screening of a supplier's questionnaire answers; use vendor-risk-screening-brief instead. Never classifies, screens, clears, decides licence need or releases a shipment; trade compliance does. 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。
Transform a supplied photograph into a tactile miniature layered paper diorama. Use for 微型纸雕、立体纸艺场景、paper-cut photo scenes or a photograph rebuilt as a handcrafted paper miniature.
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Transform a supplied photo into a charming cartoon pixel-art scene with recognizable subjects, source-derived colors and an optional short life-save caption. Use for 卡通像素风, 像素生活存档, retro game scene portraits, pets or travel-photo pixel illustrations.
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Transform a supplied photo into an editorial collage where a realistic foreground gradually continues as fine drawing and light pigment on paper. Use for 实景渐绘拼贴、摄影与建筑线描融合、photo-to-sketch travel posters. Keeps a photographic anchor rather than converting the whole image into an illustration.
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Transform an ordinary object in a supplied photo into a poetic museum-exhibit poster, preserving its identifying shape while reimagining the display setting. Use for 私人生活博物馆, 日常物件展品, or a collectible still-life poster from food, flowers, toys or personal objects.
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。