tasks
無料View and manage the task stack and processing queue. Shows pending work, active tasks, completed items, and queue state. Triggers on "/tasks", "show tasks", "what's pending", "task list", "queue status".
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。
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概要と使いどころ
View and manage the task stack and processing queue. Shows pending work, active tasks, completed items, and queue state. Triggers on "/tasks", "show tasks", "what's pending", "task list", "queue status".
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。
Queue processing with fresh context per phase. Processes N tasks from the queue, spawning isolated subagents to prevent context contamination. Supports serial, parallel, batch filter, and dry run modes. Triggers on "/ralph", "/ralph N", "process queue", "run pipeline tasks".
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。
Add a source file to the processing queue. Checks for duplicates, creates archive folder, moves source from inbox, creates extract task, and updates queue. Triggers on "/seed", "/seed [file]", "queue this for processing".
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。
Azure Service Bus SDK for .NET. Enterprise messaging with queues, topics, subscriptions, and sessions. Use for reliable message delivery, pub/sub patterns, dead letter handling, and background processing. Triggers: "Service Bus", "ServiceBusClient", "ServiceBusSender", "ServiceBusReceiver", "ServiceBusProcessor", "message queue", "pub/sub .NET", "dead letter queue".
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。
Manage Apify datasets, key-value stores, and request queues programmatically, and orchestrate multi-Actor pipelines. Use when you need to read or write Apify datasets, export scraped data to CSV/JSON/XLSX, store config or binary artifacts in a key-value store, manage a resumable request queue, chain Actors into a scrape → transform → export pipeline, or monitor Actor run status and cost. Trigger with "apify dataset", "apify key-value store", "apify storage", "export apify data", "apify pipeline", "apify request queue".
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。
Inspecteur des finances publiques IA. Simule un contrôle fiscal DGFIP complet sur les comptes d'une entreprise française (SASU, EURL, SAS, SARL). Analyse le FEC, la liasse fiscale, les charges déduites, le compte courant d'associé, la TVA, l'IS selon 8 axes de vérification. Identifie les chefs de redressement potentiels avec montants, base légale et niveaux de risque. Triggers: contrôle fiscal, redressement, vérification comptabilité, DGFIP, FEC, déductibilité, audit fiscal, tax audit
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。
Make a PortalJS portal harvestable by national/EU/US open-data portals — emit standards-compliant DCAT catalog feeds (DCAT 2/3, DCAT-AP, DCAT-US, national profiles) in JSON-LD, Turtle, and RDF/XML at build, with autodiscovery and per-profile conformance checking. Use when a portal needs to be harvested by data.europa.eu, data.gov, or a national open-data catalog.
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。
Für Mehrdeutigkeit 305c2: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Schnittstellenkarte mit Zuständigkeits- und Nachweisfragen.
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。
Für Force Majeure Höhere Gewalt: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Schnittstellenkarte mit Zuständigkeits- und Nachweisfragen.
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。
Für Architekten Ingenieur AGB: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Schnittstellenkarte mit Zuständigkeits- und Nachweisfragen.
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。
Only for explicit prior-work/reuse/history requests, not status checks. Finds and verifies existing work before new production when reuse is plausible. Use when the user explicitly references earlier work, existing code, history, or prior decisions, or says 以前做过, 已有代码, 别重复造轮子. Not for current tests, files, bug fixes, or mechanical verification. Produces a reuse/adapt/reject receipt; zero hits never prove absence.
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 travel with Amadeus: flight offers and prices, airport autocomplete, hotel offers, cheapest dates. Trigger phrases: amadeus, flight search, flight prices, hotel 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。
Three modes for CS-conference papers (CVPR/ICCV/ECCV vision, ACL/EMNLP/NAACL NLP, ICLR/NeurIPS/ICML/AAAI ML). DIRECT-EDIT mode (common): the user describes a change in Chinese or English and the manuscript (LaTeX or Markdown) is edited directly through a CS-venue writing toolkit with author sign-off (use for 改这段 / 把中文想法写成 latex / polish / de-AI / translate / compress a passage). REVIEW mode (occasional, pre-submission): harden the paper through an adversarial courtroom review engine (N holistic domain reviewers / contestability routing / two-sided trial / three-way verdict / clerk-converged multi-round loop) with consensus-gated, author-signed revisions (use for review / critique / 审稿 / 评审 / mock-review). AUTO mode (unattended, opt-in via /goal): run the review-revise loop toward a verifiable goal, applying safe fixes under a drift-bounded policy and queueing risky ones. Resolves all inputs at runtime, no hardcoded paths. Not a from-scratch drafter (use ml-paper-writing) and not an official-venue rebuttal.
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。
Predicts protein-coding gene structures (exons, introns, UTRs) in eukaryotic genomes with BRAKER3 (RNA-seq + protein evidence), BRAKER1/BRAKER2, GALBA (protein-only), Funannotate (fungi), GeMoMa (homology projection), or Helixer/Tiberius (deep-learning ab initio). Covers the evidence-first tool decision, mandatory soft-masking, the training-set-quality-dominates principle, OrthoDB clade-partition selection, the one-isoform-per-locus and missing-UTR traps, merge/split errors, and reference bias against orphan genes. Use when annotating a newly assembled eukaryotic genome, choosing a gene-prediction pipeline based on available evidence, or diagnosing a poor annotation.
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。
Assigns isolate identity at the right resolution for the question -- ANI/Mash species triage, 7-locus MLST historical comparability, cgMLST/wgMLST outbreak resolution (chewBBACA, BIGSdb, Ridom SeqSphere, EnteroBase HierCC), in-silico serotyping (SISTR/SeqSero2 Salmonella, SerotypeFinder E. coli, Kaptive Klebsiella, SeroBA pneumococcus, spa+SCCmec S. aureus), and lineage callers (TB-Profiler/Mykrobe barcode for MTBC, Pangolin + Nextclade for SARS-CoV-2, PopPUNK GPSC for S. pneumoniae). Use when typing bacterial isolates for surveillance or outbreak investigation, choosing between cgMLST allele distance and core-SNP distance for cluster definition, harmonising calls across schemas/database versions, assigning MTBC lineage with the Napier 90-SNP barcode, calling Salmonella serovar via SISTR with monophasic Typhimurium awareness, running Pangolin UShER mode with pangolin-data version pinning, or selecting a typing resolution to match the surveillance question.
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。
Generate pileup data for variant calling using samtools mpileup and pysam. Use when preparing data for variant calling, analyzing per-position read data, or calculating allele frequencies.
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。
AWS SQS message queue service for decoupled architectures. Use when creating queues, configuring dead-letter queues, managing visibility timeouts, implementing FIFO ordering, or integrating with Lambda.
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。
EU NIS2 Directive (Directive (EU) 2022/2555) compliance advisor for essential and important entities: entity classification, Art. 21 risk management measures, Art. 23 incident reporting timelines (24h/72h/1 month), Art. 20 governance obligations, supply chain security (Art. 21(2)(d); coordinated risk assessments Art. 22), gap assessments, policy drafting, ISO 27001 alignment, and penalty exposure analysis. Also covers Commission Implementing Regulation (EU) 2024/2690, the technical/methodological sub-requirements for Art. 21(2) and the significant-incident thresholds binding on DNS/cloud/data-centre/MSP/MSSP/trust-service and other digital entities. Use for NIS2 readiness, transposition questions, ENISA technical implementation guidance, significant-incident thresholds, supervisory differences between essential and important entities, and cross-border coordination.
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。
Recherche, cartographie et analyse des thèses de doctorat françaises via theses.fr (API ABES) et TEL (HAL). Spécialisée droit avec filtrage par sous-disciplines juridiques. Ouverte sur d'autres domaines universitaires. Capacités : (1) recherche multi-critères — sujet, directeur, jury, établissement, école doctorale, période, statut soutenue/en préparation, discipline ; (2) cartographie académique — qui dirige sur quel sujet, qui siège dans quels jurys, réseaux de co-direction, écoles doctorales productives ; (3) analyses temporelles — évolution d'un sujet, détection de saturation, calendrier des soutenances à venir ; (4) filtrage sous-discipline juridique — droit privé, public, social, affaires, international, etc. Déclencher dès que l'utilisateur mentionne thèse, doctorat, directeur de thèse, jury de thèse, école doctorale, soutenance, doctorant, ou demande une analyse d'écosystème doctoral. Pour articles ou ouvrages de doctrine, utiliser plutôt recherche-doctrine.
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。
EU NIS2 Directive (Directive (EU) 2022/2555) compliance advisor for essential and important entities: entity classification, Art. 21 risk management measures, Art. 23 incident reporting timelines (24h/72h/1 month), Art. 20 governance obligations, supply chain security (Art. 21(2)(d); coordinated risk assessments Art. 22), gap assessments, policy drafting, ISO 27001 alignment, and penalty exposure analysis. Also covers Commission Implementing Regulation (EU) 2024/2690, the technical/methodological sub-requirements for Art. 21(2) and the significant-incident thresholds binding on DNS/cloud/data-centre/MSP/MSSP/trust-service and other digital entities. Use for NIS2 readiness, transposition questions, ENISA technical implementation guidance, significant-incident thresholds, supervisory differences between essential and important entities, and cross-border coordination.
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 when asked to analyze how Iceland has implemented an EU directive or regulation into national law, identify gaps between EU/EEA law and Icelandic implementation, or assess Iceland's compliance with EEA obligations. Triggers on questions about EEA transposition, ESA infringement, or EU-Iceland legal gaps.
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。
Assess obligations under Regulation (EU) 2023/2854 for connected products, related services, statutory B2B data sharing, unfair data-contract terms, exceptional-need B2G requests, data-processing-service switching, international governmental access, interoperability, smart contracts, enforcement, and non-EU representatives. Use for Data Act scope analysis, gap assessments, product and service design, request handling, contract review, cloud exit planning, or cross-regulation 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。
Practitioner skill for advising on EU Regulation 2023/2854 (Data Act). Covers Chapters II-VII (IoT data access, mandatory B2B sharing, unfair contract terms, public-sector exceptional need, cloud switching, third-country governmental access) and Chapter VIII (interoperability and smart contracts, gate-only). Use when the user asks about Data Act rights or obligations, drafts a Data Act notice or letter, reviews a data-sharing or cloud-switching contract under the Data Act, runs a Data Act gap analysis, or asks how the Data Act interacts with GDPR, the DMA, the Trade Secrets Directive, or sectoral law. Triggers include "Data Act", "Datengesetz", "Regulation (EU) 2023/2854", "Art. 4(1) request", "Art. 5(1) third-party request", "trade-secret handbrake", "cloud switching obligations", "Chapter VI", "Ch V exceptional need", and references to specific Data Act articles or recitals.
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。
EU Data Act (Regulation (EU) 2023/2854) skill for lawyers. Use when the user asks about Data Act classification, drafting, lookup, analysis, or audit. Triggers include "Data Act", "Regulation 2023/2854", "connected product", "related service", "data processing service", "DPS switching", "Article 3(2) pre-contract", "Article 25 contract", "trade-secret handbrake", "international government access", "Chapter VI cloud switching", "Article 50 timeline", "FAQ Q22a", "data holder", "exportable data", "functional equivalence", "Art. 4(10) competing product", and similar EU Data Act phrases. The skill produces lawyer-style Word output and cites verbatim from bundled regulation and FAQ source texts.
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。