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Found 58 Skills
This skill should be used when the user asks to forecast aggregate sentiment and opinion dynamics over time—sentiment indices from text streams; temporal rollups; leading/lagging KPI links; time-series and sequence models (ARIMA, Prophet, state-space, ML); nowcasting; spikes, bots, and bias; walk-forward backtests; intervals and scenarios; volume/velocity/topic features; BI or brand dashboards. Triggers: sentiment forecasting, forecast sentiment, sentiment index, opinion trend forecast, social sentiment time series, brand sentiment trajectory, nowcast sentiment, sentiment leading indicator, aggregate polarity forecast, sentiment backtest, walk-forward sentiment, sentiment spike prediction. Not for per-text labeling (sentiment-analysis-engineer), demand forecasting without sentiment (predictive-logistics-developer, data-scientist), trade advice (methodology only), marketing copy (content-creator), macro without text sentiment (financial-analyst partial).
Guidance for querying ML model leaderboards and benchmarks (MTEB, HuggingFace, embedding benchmarks). This skill applies when tasks involve finding top-performing models on specific benchmarks, comparing model performance across leaderboards, or answering questions about current benchmark standings. Covers strategies for accessing live leaderboard data, handling temporal requirements, and avoiding common pitfalls with outdated sources.
Use this for designing complex workflows, scheduled jobs, and task orchestration (Airflow, Prefect, Temporal, Cron, Celery).
Gets, checks, and verifies the current UTC date and time for unambiguous temporal reference. Use when starting tasks, verifying temporal context, ensuring date awareness before time-sensitive operations, or when incorrect date assumptions are detected.
Investigates hypotheses that MEV activity (bundles, searchers, same-block ordering) temporally overlaps or co-occurs with launch-phase rug signals—using public txs, bundle IDs, and clustering with explicit confidence. Use when the user asks about MEV plus rug coordination, launch sniper bundles, Jito or Flashbots overlap with dev exits, or joint profit-flow case studies—not for alleging collusion without evidence, harassing addresses, or live interference.
Use when writing, fixing, or editing TypeScript modules, classes, file structure, declaration order, vertical formatting, dependency direction, cohesion, coupling, dependency construction, temporal coupling, public exports, wiring, or over-abstraction.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Async communication patterns using message brokers and task queues. Use when building event-driven systems, background job processing, or service decoupling. Covers Kafka (event streaming), RabbitMQ (complex routing), NATS (cloud-native), Redis Streams, Celery (Python), BullMQ (TypeScript), Temporal (workflows), and event sourcing patterns.
CQRS and Event Sourcing for auditability, read/write separation, and temporal queries. Triggers: CQRS, event-sourcing, audit-trail, temporal queries, distributed-systems Use when: read/write scaling differs or audit trail required DO NOT use when: selecting paradigms (use architecture-paradigms first), simple CRUD without audit needs.
N-dimensional labeled arrays for geoscience data. Read/write NetCDF, work with climate and oceanographic datasets, perform multi-dimensional analysis with labeled coordinates. Use when Claude needs to: (1) Read/write NetCDF or Zarr files, (2) Work with multidimensional arrays with labeled dimensions, (3) Analyze climate, ocean, or atmosphere data, (4) Compute temporal aggregations (daily/monthly/annual means), (5) Perform area-weighted statistics, (6) Process large datasets with Dask, (7) Apply CF conventions to scientific data.
Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.
Use pkm for personal knowledge management with temporal awareness, quality filtering, hybrid search, and relationship tracking with LSP and MCP server integration.