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Found 402 Skills
Use when investigating inbox placement, reputation, and compliance signals across senders.
Analyzes provided content for quality, E-E-A-T signals, and SEO best practices. Scores content and provides improvement recommendations based on established guidelines. Use PROACTIVELY for content review.
Angular 21 standalone frontend with Clean Architecture, vertical slice (pages by feature), signals, OnPush, and ng-zorro. Use when creating or refactoring Angular apps, adding pages, components, services, directives, pipes, or when the user mentions Angular frontend, standalone components, or signals.
Full token research workflow using Messari x402 API. Fetches asset fundamentals, price history, sentiment signals, and news, then synthesizes a research brief via Messari AI. Total cost ~$1.00–$1.50 USDC per run.
HODLMM volatility risk monitor — reads Bitflow HODLMM pool state, computes current-state volatility proxy from bin distribution, scores regime (calm/elevated/crisis), and emits position-sizing or liquidity-pull signals for LP agents. Read-only; no wallet required.
Design, weight, and tune a lead scoring model for your sales funnel. Use when building a lead scoring system, defining MQL/SQL criteria, assigning point values to lead attributes, setting up scoring in your CRM or MAP, tuning conversion thresholds, or deciding which signals should trigger sales follow-up. Do NOT use for reading existing buying signals (use /sales-intent), building prospect lists (use /sales-prospect-list), or marketing-to-sales handoff process design (use /revops).
ALWAYS LOAD WHEN WORKING WITH PYSIDE6, QT, OR DESKTOP GUI CODE. PySide6 desktop apps: Manager→Service→Wrapper architecture, qasync integration, signals, system tray, testing.
Signal-gated HODLMM yield allocator. Reads aibtc.news signals and Quantum Readiness Index alongside live HODLMM APR to compute a risk-adjusted yield score, then executes a Bitflow swap to prepare wallet for HODLMM deposit when conditions align.
Audit landing pages. Use when: scoring above-fold clarity, trust signals, form friction, message match, or mobile UX.
Investigate Kubernetes workload, node, and control-plane issues using OTel telemetry (EDOT). Use when diagnosing pod failures (CrashLoopBackOff, OOMKilled, Error), node pressure, resource exhaustion, image pull failures, admission rejections, autoscaling anomalies, or correlating K8s state with application signals. OTel ingest path only — the legacy ECS Kubernetes integration shape is out of scope.
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
Framework for developing, testing, and deploying trading strategies for prediction markets. Use when creating new strategies, implementing signals, or building backtesting logic.