1,147 件 ・ 関連度順
概要と使いどころ
Configure, compile, and test Yuzu with the repo's canonical Meson entrypoints and per-OS build directories. Use when validating code changes, selecting test suites, reconfiguring a build directory, or recovering from host/builddir mismatches on Linux, macOS, or Windows.
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。
Author, validate, test, and deploy YARA-L 2.0 detection rules and evaluate end-to-end detection coverage gaps in Google SecOps. Use when writing new detection rules, tuning existing rules, validating syntax, testing logic against historical telemetry, or evaluating detection coverage against threat intelligence blogs, CVE disclosures, and Threat Detection Opportunities (TDOs) using synthetic UDM events and long-running coverage analysis. Don't use for alert triage (use secops-triage), deep forensic event reconstruction on infected hosts (use secops-investigate), or case management operations (use secops-cases).
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。
Guides agents through a 6-step discovery process to design and deploy Google Cloud global external Application Load Balancers with Cloud CDN, Cloud Armor, and Service Extensions, mapping workload requirements to best-practice configurations. Use when: - Designing, configuring, or deploying a Google Cloud global external Application Load Balancer, Cloud CDN, Cloud Armor WAF, or Service Extensions. - Discovering existing Google Cloud resources (Cloud Storage, MIGs, GKE, Cloud Run) to use as backends. - Generating production-grade Terraform HCL or gcloud CLI scripts for global external Application Load Balancers. - Actuating deployments via Infrastructure Manager or bash scripts, including IAM pre-checks. - Detecting, analyzing, or reconciling configuration drift on deployed global external Application Load Balancers. Don't use for: - Non-Google Cloud load balancing or security configurations. - Purely regional or internal load balancing setups (unless part of a hybrid/failover global design).
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。
Creates Cloud Storage (Google Cloud Storage, or GCS) buckets. Analyzes the workload (sensitive data, media hosting, ingestion, web hosting, archiving, backup, logging, analytics, AI/ML, or general-purpose), validates project-level security settings, and designs a secure-by-default, cost-effective configuration (location, storage class, uniform bucket-level access, public access prevention, soft delete, lifecycle) before creating it. Use whenever a user wants to create, make, set up, provision, or spin up a bucket, or needs object storage for an app, service, pipeline, or dataset — even a "simple" or "default" bucket, or when bucket creation is one step in a larger workflow. Outputs or executes the creation via gcloud, the JSON/REST API, Terraform, or SDK client libraries (C++, Java, Python, Go). Don't use for anything other than creating new buckets — for uploads, downloads, access changes, or reconfiguring existing buckets, use google-cloud-storage-basics.
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。
Configure and troubleshoot Salesforce Billing revenue recognition rules, schedules, and GL transaction generation under ASC 606. Triggers: 'revenue schedule not generated after order activation', 'blng__RevenueSchedule__c missing', 'blng__RevenueRecognitionRule__c on a product', 'Finance Periods not set up', 'performance obligation allocation for bundled products', 'distribution method', 'ASC 606 in Salesforce Billing'. NOT for billing schedules, invoice plans and invoice runs - use admin/billing-schedule-setup. NOT for invoice-to-payment mismatches at month-end close - use data/billing-data-reconciliation.
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 when planning, reviewing, or troubleshooting Salesforce data imports, migrations, and bulk updates. Triggers: 'Data Loader', 'Data Import Wizard', 'Bulk API', 'upsert', 'cutover', 'load failed', 'reconcile data'. NOT for sequencing a whole multi-object migration with automation bypass and rollback — use data/data-migration-planning. NOT for choosing the upsert key itself — use data/external-id-strategy. Also triggers on 'process-conf.xml', 'sdl mapping file', 'sfdc.loadBatchSize', 'insert null values', 'insertNulls', '#N/A', 'serial mode', 'hard delete', 'assignmentRuleId', 'lineEnding', 'unprocessedrecords', 'failedResults', 'load order'.
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 writing test-first, behavior-driven acceptance criteria in Given/When/Then format for a Salesforce user story. Covers happy path, edge cases, negative paths, permission boundaries, and data-state preconditions so the AC block can drive UAT scripts and Apex test design downstream. Trigger keywords: given when then, gherkin, behavior driven AC, test first acceptance criteria, scenario outline, BDD acceptance criteria. NOT for the user-story format itself (use admin/user-story-writing-for-salesforce). NOT for UAT script writing (use admin/uat-test-case-design). NOT for Apex test method generation (use agents/test-class-generator). NOT for high-level UAT planning (use admin/uat-and-acceptance-criteria). More triggers: acceptance criteria record, ac_id, req_id on a criterion, artefact under test, negative case for a rule-type requirement, no-match fall-through criterion, oracle for a Then clause, test type apex flow manual, criteria linter, check_ac_format.py, --manifest-dir.
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。
AI นักบัญชีรายวัน — General Ledger, Journal Entry, Reconcile, AR/AP, SME Bookkeeping, งบการเงินรายเดือน
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。
White-box security audit. Blue-teamer and lead red-teamer run in parallel isolation for an independent first pass — neither sees the other's output during reconnaissance. A synthesis step categorizes findings into four prescriptive buckets (anchoring-suppressed, convergent, blue-flagged-unverified, divergent), producing a unified target list. Focused red-teamers then deep-dive each target. Iterates when exploit chains are discovered. Heavy and thorough by design. Advisory only — produces findings and proposes tickets; does not implement remediation.
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。
Complete MCP engineering work on the live path: build or repair servers and Apps, debug stdio or Streamable HTTP, migrate protocol/SDK/transport versions, reconcile source with package and activated runtime, inspect capability/effect authorization, and verify named-host claims without promoting weaker evidence. Use for real Model Context Protocol implementation, review, migration, runtime, or host-integration tasks. Do not use for ordinary non-MCP APIs, generic frontend work, or incidental product copy.
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。
Design, audit, harden, review, or upgrade MCP servers and MCP Apps with revision-aware protocol profiles, explicit HTTP/deployment boundaries, capability-centered validation, bilingual reporting, and reproducible evidence. Use for MCP server architecture, JSON-RPC or Streamable HTTP bugs, security hardening, protocol-version migration, MCP Apps widget boundaries, review reconciliation, test planning, and public engineering evidence.
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。
Audit and maintain the learnings in `docs/solutions/` against the current codebase — fix drifted paths and links, merge overlapping docs, replace guidance the code no longer supports, delete obsolete docs, reconcile the `CONCEPTS.md` glossary, then report and commit. Also bootstraps `CONCEPTS.md` on request and, when asked, culls docs whose reasoning the code already explains. Use for "refresh the learnings", "audit docs/solutions", "are our solution docs still accurate", "clean up / prune the learnings", "create a CONCEPTS.md", or after a refactor, rename, migration, or dependency upgrade that likely invalidated documented solutions. Not for capturing a new learning — use `learn-with-docs`.
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。
Auto-activate for litestar_security, SecurityPlugin, SecurityConfig, SecureController, CurrentUser, Principal, SecurityContext, or requires_role/scope/tenant/capability/assurance. Not for basic Litestar Guards.
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。
Auto-activate for litestar_autowire, AutowirePlugin, AutowireConfig, domain_packages, AutowireIntegration, AutowireLoader, or clear_autowire_cache. Not for manual Router composition — use explicit routes.
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。
Makes the agent reconsider a task it just handed back to the human and find a way to do it itself, checking memory, CLI tools, skills, MCP servers, and documentation in turn. Use when the user invokes `/you` or `$you`, or replies that the agent should do something itself rather than asking them.
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。
Inspect scoped Autodesk data, compare versioned BOMs, and prepare ownership-aware drafts or approved custom-property changes. Not for implicit PLM release, membership changes or CAD geometry editing.
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。
Prepare, submit and reconcile reviewed Fusion Automation jobs with exact sources, scoped authorization and explicit budgets. Not for arbitrary model-generated cloud scripts.
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。
Professional network graph analyst identifying Gladwellian superconnectors, mavens, and influence brokers using betweenness centrality, structural holes theory, and multi-source network reconstruction. Activate on 'superconnectors', 'network analysis', 'who knows who', 'professional network', 'influence mapping', 'betweenness centrality'. NOT for surveillance, discrimination, stalking, privacy violation, or speculation without 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。
Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for visualisation.
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。
Estimates time-scaled phylogenies, molecular-clock rates, effective reproduction number R_e, and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict/relaxed clocks; coalescent, Bayesian-Skyline, Skygrid, Birth-Death-Skyline, and sampled-ancestor priors; structured coalescent via MASCOT). Covers root-to-tip clock QC via TempEst, date-randomisation tests, recombination masking via Gubbins/ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction, multi-chain convergence diagnostics, and reconciling phylodynamic R_e with case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline models, diagnosing temporal-signal failure, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.
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。
Detects acquired antimicrobial-resistance determinants and chromosomal point-mutation resistance in bacterial assemblies using AMRFinderPlus, ResFinder 4.0 (acquired + PointFinder), CARD-RGI, abritAMR, staramr, and species-specific callers (TB-Profiler, Mykrobe). Harmonises cross-tool output via hAMRonization, contextualises determinants with mobile-genetic-element annotation (MOB-suite, PlasmidFinder, MobileElementFinder, ICEberg), predicts phenotype against EUCAST or CLSI breakpoints, and translates calls into WHO GLASS reporting categories. Use when screening clinical or surveillance isolates for AMR, distinguishing acquired vs intrinsic vs point-mutation resistance, calling rpoB / katG / pncA / gyrA / mgrB mutations, reconciling AMRFinderPlus vs RGI vs ResFinder disagreement, contextualising carbapenemases or mcr alleles on plasmids, predicting susceptibility from genotype against the WHO Mtb 2nd-edition catalogue, or building a hAMRonized multi-lab AMR surveillance pipeline.
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。
Detect horizontal gene transfer (HGT / LGT) using compositional methods (GC%, codon usage, tetranucleotide z-scores via SIGI-HMM, AlienHunter, IslandViewer 4, IslandPath-DIMOB), phylogenetic-incongruence methods (AvP, HGTphyloDetect, ALE / GeneRax / AleRax reconciliation, RANGER-DTL), and BLAST-distribution methods (HGTector v2, DarkHorse, Alien Index). Use when screening prokaryote genomes for genomic islands and HGT events, distinguishing HGT from incomplete lineage sorting / differential gene loss / hybridization, mapping donor lineages via phylogenetic placement, separating eukaryotic HGT from contamination, ruling out gBGC as a false signal, or quantifying DTL rates with ALE/GeneRax on bacterial trees.
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。
Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, diagnosing flat-reference false positives, or interpreting log2 ratios into copy-number states.
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。
Discover RBP binding motifs from CLIP-seq peaks or single-nucleotide crosslink sites using HOMER, MEME/STREME, kpLogo, mCross (CL-position-registered motifs), PEKA (positional k-mer enrichment), RBPamp (affinity), and RNA Bind-n-Seq (RBNS) cross-validation. Use when characterizing RBP sequence specificity, registering motifs to crosslink positions, validating in vivo CLIP motifs against in vitro RBNS Kd, reconciling motif disagreements across tools, or correcting for the uracil crosslinking bias that contaminates raw CLIP motif logos.
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。