Loading...
Loading...
Found 776 Skills
Multi-method geophysical modelling and inversion framework. Use when Claude needs to: (1) Perform electrical resistivity tomography (ERT) inversion, (2) Run seismic refraction tomography (SRT), (3) Model induced polarization (IP) data, (4) Simulate ground penetrating radar (GPR), (5) Create finite element meshes for geophysical problems, (6) Perform joint inversions of multiple datasets, (7) Forward model geophysical responses, (8) Analyze time-lapse monitoring data.
Guide counterparty credit risk measurement and management for OTC and securities trading. Use when measuring current or potential future exposure to a counterparty, setting or reviewing counterparty credit limits, evaluating ISDA Master Agreement netting benefits, designing collateral management or CSA terms, assessing central clearing mandates under Dodd-Frank or EMIR, monitoring counterparty creditworthiness via CDS spreads or ratings, managing Herstatt or settlement risk in FX, quantifying wrong-way risk, or building real-time exposure dashboards. Also use for counterparty default scenarios, credit deterioration events, EAD and SA-CCR calculations, and CVA capital charges.
Manage data programs, governance operations, and data reliability. Cover data roadmaps, stakeholder coordination, metadata stewardship, lifecycle management, monitoring, incident response, capacity planning, and SLA frameworks. Triggers on "manage data team", "data roadmap", "governance review", "data incident", "SLA framework", "data ops", "stewardship", "data product delivery", or "data KPIs". Human annotation/labeling platform PM: product-management-human-data-platform.
Wash sale detection under 2025 US crypto rules with 61-day window monitoring, disallowed loss tracking, and safe re-entry countdown
Expert in deploying and using Hermes HUD Web UI for monitoring AI agent memory, sessions, costs, and health
Alibaba Cloud EMAS APM (mobile Application Performance Monitoring) issue troubleshooting skill. Covers the 4 read-only OpenAPIs exposed by the `aliyun emas-appmonitor` plugin: `get-issues` / `get-issue` / `get-errors` / `get-error`. Capabilities: Top-N aggregation, sample stack drill-down and dimension breakdowns for 6 issue types (crash / anr / lag / custom / memory_leak / memory_alloc), combined with the user's source code (Java / Kotlin / Objective-C / Swift / ArkTS / Dart / C# / JS) to produce root cause analysis and fix suggestions. Client coverage: native Android / iOS / HarmonyOS, Flutter, Unity (bundled to android / iphoneos / harmony; H5 is out of scope). Triggers: analyze app crash, troubleshoot ANR, APM crash investigation, list top issues, "what is this digestHash", iOS ANR Top 5, Android memory leak analysis, Flutter custom exception stacks, pull lag samples, emas appmonitor usage, sort issues by error rate, map stack to source, appKey problem, EMAS APM issue analysis, analyze APM issues.
Detect DCSync attacks where adversaries abuse Active Directory replication privileges to extract password hashes by monitoring for non-domain-controller accounts requesting directory replication via DsGetNCChanges.
Prometheus monitoring and alerting for cloud-native observability. USE WHEN: Writing PromQL queries, configuring Prometheus scrape targets, creating alerting rules, setting up recording rules, instrumenting applications with Prometheus metrics, configuring service discovery. DO NOT USE: For building dashboards (use /grafana), for log analysis (use /logging-observability), for general observability architecture (use senior-software-engineer with infrastructure focus). TRIGGERS: metrics, prometheus, promql, counter, gauge, histogram, summary, alert, alertmanager, alerting rule, recording rule, scrape, target, label, service discovery, relabeling, exporter, instrumentation, slo, error budget.
Quickly set up monitoring for a competitor company. Tracks news, product updates, funding, and public announcements.
Comprehensive patient stratification for precision medicine by integrating genomic, clinical, and therapeutic data. Given a disease/condition, genomic data (germline variants, somatic mutations, expression), and optional clinical parameters, performs multi-phase analysis across 9 phases covering disease disambiguation, genetic risk assessment, disease-specific molecular stratification, pharmacogenomic profiling, comorbidity/DDI risk, pathway analysis, clinical evidence and guideline mapping, clinical trial matching, and integrated outcome prediction. Generates a quantitative Precision Medicine Risk Score (0-100) with risk tier assignment (Low/Intermediate/High/Very High), treatment algorithm (1st/2nd/3rd line), pharmacogenomic guidance, clinical trial matches, and monitoring plan. Use when clinicians ask about patient risk stratification, treatment selection, prognosis prediction, or personalized therapeutic strategy across cancer, metabolic, cardiovascular, neurological, or rare diseases.
Scaffolds or references a production-ready Node.js REST API with Express 5, TypeScript, Mongoose (MongoDB), Redis, Sentry, JWT auth, bcrypt, rate limiting, and centralized error handling. Use when the user wants to start a new observable and resilient backend, needs a Node.js API boilerplate with security and monitoring, or asks to clone or adapt this template repository.
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.