547건 · 관련도순
Use Jev to choose among available tools, skills, or agents, or assess outputs against explicit criteria. Receive structured selections, scores, or probabilities to support decisions within a defined set of options.
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Investigate failed or unexpected Stripe payments using exact record IDs, processing states, events, amounts, and currencies. Separate what the customer sees from Stripe's state and integration behavior, then identify the next action without changing payment records during diagnosis.
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Build shared settlement sessions where multiple agents commit funds, update allocations off-chain, and co-sign a final split. Covers Yellow SDK connections, participant voting weights, signature thresholds, deposits, withdrawals, closure, and the system's trust limits.
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Set up and run research that compares evidence across multiple sources, such as market studies, competitor analysis, or fact checks. The skill handles environment checks, installation guidance, research settings, progress reporting, final files, and recovery.
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Turn existing Markdown material into an article by choosing among suggested next passages. Establish what readers already know, introduce concepts before relying on them, and build the article one selected passage at a time.
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Repair navigation between existing Obsidian project notes by improving meaningful links among papers, experiments, results, and writing. Keep references connected to the main note for each subject while avoiding unnecessary extra notes and excessive linking.
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Review APM positioning, public documentation, release communication, and breaking-change decisions using GitHub evidence. Check that incompatible changes include changelog entries and migration notes, and resolve disagreements among the project's specialist reviewers.
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Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL, pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS, convert to Iceberg. Do NOT use for setting up or troubleshooting Glue connections (use connecting-to-data-source), creating empty tables (use creating-data-lake-table), running queries (use querying-data-lake), finding tables by fuzzy name (use finding-data-lake-assets), catalog audit (use exploring-data-catalog), or SaaS platforms like Salesforce, ServiceNow, SAP, MongoDB, Kafka.
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Baidu FaMou algorithm skills for efficient algorithm self-evolution. Provides experiment management and visualization capabilities to help optimize complex algorithms. Use when user needs algorithm optimization or experiment management.
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Make compound growth actually click — see how small, consistent amounts become large over time, and why starting now beats starting bigger later. Use when asked explain compound interest, how does compounding work, is it worth investing small amounts, or why should I start now. Produces an intuitive explanation of compounding with concrete illustrative examples for your situation, the outsized effect of time (why an early start beats a later larger one), how fees and inflation eat into it, and the honest caveats — turning an abstract concept into the motivation to start now. Educational, not financial advice.
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Analyzes species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner with corrected modeltype=6 (Dray 2014), bipartite network metrics (NODF, modularity) with curveball null (Strona 2014), and Mantel-test replacements (dbRDA, GDM) for spatial data. Use when testing how environmental gradients structure communities, identifying habitat indicator taxa, partitioning variance among predictors, deciding whether PERMANOVA significance is location vs dispersion, picking among HMSC/sjSDM/gjam, or replacing Mantel tests for landscape data.
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Detect distant homologs using profile and structure-aware methods that go beyond standard BLAST. Use when sequence identity falls into the twilight zone (<35% pairwise), when BLAST fails to find homologs that should exist, when working at metagenomic scale (DIAMOND, MMseqs2), or when structure beats sequence (Foldseek). Covers PSI-BLAST (iterative PSSM), jackhmmer (iterative HMM), HHblits/HHsearch (profile-profile), DIAMOND, MMseqs2, and Foldseek (3Di structural alphabet, van Kempen 2024).
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AWS DynamoDB NoSQL database for scalable data storage. Use when designing table schemas, writing queries, configuring indexes, managing capacity, implementing single-table design, or troubleshooting performance issues.
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Build API clients with URLSession, Alamofire, and Codable. Use when implementing URLSession networking, Alamofire, or API clients in iOS.
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Cyber threat intelligence production — the intelligence cycle, IOC extraction/normalization/enrichment, STIX/TAXII and MISP, structured analytic models (Diamond, Kill Chain, ATT&CK), source scoring, actor/campaign tracking, and finished intelligence reporting
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Use when you need to add or evaluate Maven dependencies that improve code quality or domain modeling — including nullness annotations (JSpecify), static analysis (Error Prone + NullAway), functional programming (VAVR), architecture testing (ArchUnit), or money and currency support (JavaMoney) — and want a consultative, question-driven approach that adds only what you actually need. This should trigger for requests such as Add Maven dependencies; Add JSpecify nullness dependencies; Add Error Prone NullAway dependencies; Add VAVR functional dependencies; Add ArchUnit architecture testing dependencies; Add JavaMoney dependencies. Part of Plinth Toolkit
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Pythonic RDKit wrapper with sensible defaults for drug discovery. SMILES parsing, standardization, descriptors, fingerprints, similarity, clustering, diversity selection, scaffold analysis, BRICS/RECAP fragmentation, 3D conformers, and visualization. Returns native rdkit.Chem.Mol. Prefer datamol for standard workflows; use RDKit directly for advanced control.
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Medicinal chemistry filters for compound triage. Drug-likeness rules (Lipinski Ro5, Veber, Oprea, CNS, leadlike, REOS, Golden Triangle, Ro3), structural alerts (PAINS, NIBR, Lilly Demerits), chemical group detectors, complexity metrics, and filter composition query language. Built on RDKit/datamol. For hit-to-lead filtering, library design, ADMET pre-screening. For molecular I/O use rdkit-cheminformatics or datamol.
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This skill should be used when the user asks about the invoice system (インボイス制度), qualified invoices (適格請求書), registration numbers (登録番号), input tax credits (仕入税額控除), the 20% special measure (2割特例), the 30% special measure (3割特例), tax-exempt businesses (免税事業者), transitional measures (経過措置), small-amount exceptions (少額特例), corrected invoices (修正インボイス), or any related topics. Trigger phrases include: "インボイス", "適格請求書", "登録番号", "仕入税額控除", "2割特例", "3割特例", "免税事業者", "経過措置", "少額特例", "修正インボイス", "返還インボイス", "簡易インボイス", "インボイス登録", "T番号", "適格請求書発行事業者".
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Track construction retainage/retention amounts. Monitor held amounts by subcontractor, track release conditions, and manage retainage billing.
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Routes DreamOmni2 image generation, image editing, and Gradio demo workflows.
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Training, inference, and deployment workflows for DAMO-YOLO object detection.
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Guides agents using the datamol Python package for RDKit-first molecular IO, preparation, fingerprints, similarity, structure generation, visualization, and utility workflows.
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Generates automated test oracles to verify correct software behavior. Creates assertion-based oracles (expected values), property-based oracles (invariants), differential oracles (comparing implementations), and metamorphic oracles (input transformations). Use when you need to generate assertions for test cases, identify invariants that should always hold, compare new vs legacy implementations, create metamorphic test relationships, validate function correctness, or improve test coverage with better verification strategies. Supports Python (pytest, unittest, hypothesis) and Java (JUnit, property testing).
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