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Found 56 Skills
Clean and reconstruct raw auto-generated captions (Zoom, YouTube, Teams, Google Meet, Otter.ai, etc.) into readable, coherent transcripts. Use when the user provides raw caption files (.txt, .vtt, .srt), meeting transcripts with timestamps and speaker tags, or asks to clean up/refine a transcript. Handles: timestamp removal, speaker tag normalization, filler word removal, broken sentence reconstruction, transcription error correction, paragraph formation. Preserves every piece of substantive content while removing noise. Trigger phrases: 'clean this transcript', 'refine captions', 'fix this transcript', 'process Zoom captions', 'clean up meeting notes'.
Production-ready RNA-seq differential expression analysis using PyDESeq2. Performs DESeq2 normalization, dispersion estimation, Wald testing, LFC shrinkage, and result filtering. Handles multi-factor designs, multiple contrasts, batch effects, and integrates with gene enrichment (gseapy) and ToolUniverse annotation tools (UniProt, Ensembl, OpenTargets). Supports CSV/TSV/H5AD input formats and any organism. Use when analyzing RNA-seq count matrices, identifying DEGs, performing differential expression with statistical rigor, or answering questions about gene expression changes.
Analyze mass spectrometry proteomics data including protein quantification, differential expression, post-translational modifications (PTMs), and protein-protein interactions. Processes MaxQuant, Spectronaut, DIA-NN, and other MS platform outputs. Performs normalization, statistical analysis, pathway enrichment, and integration with transcriptomics. Use when analyzing proteomics data, comparing protein abundance between conditions, identifying PTM changes, studying protein complexes, integrating protein and RNA data, discovering protein biomarkers, or conducting quantitative proteomics experiments.
Dimensional modeling and schema design for data products. Star schema patterns, slowly changing dimensions, denormalization decisions, and architecture decision records. Use when designing data models, reviewing schema designs, choosing between normalization strategies, or when someone asks "how should I model this data?" or "should I denormalize?" For OMOP CDM patterns specifically, see healthcare-data-domain.
Development guide for @rytass/sms base package (簡訊基底套件開發指南). Use when creating new SMS adapters (新增簡訊 adapter), understanding base interfaces, or extending SMS functionality. Covers SMSService interface and implementation patterns for Taiwan SMS providers (台灣簡訊服務提供商). Keywords - SMS adapter, 簡訊 adapter, service interface, 服務介面, message delivery, 訊息發送, batch processing, 批次處理, number normalization, 號碼正規化, Every8D, 互動資通, Interactive Communications
Review Express.js security audit patterns for middleware and routes. Use for auditing Helmet.js, CORS, body-parser limits, and auth middleware. Use proactively when reviewing Express.js apps. Examples: - user: "Secure my Express app" → add Helmet.js and disable x-powered-by - user: "Check Express CORS config" → verify origin allowlists and credentials - user: "Review Express auth middleware" → check route order and coverage - user: "Scan for Express path traversal" → verify path normalization and validation - user: "Audit Express session config" → check secure, httpOnly, and sameSite flags
Local execution tools for Xiaohongshu/Rednote hosted collection workflows, including actor runs, dataset normalization, account and post ranking, comment clustering, product-pool ranking, and topic-map building.
Generate ASCII mini charts (sparkline/bar/simple line) for plain-text trend inspection, with minimal + annotated variants and normalization notes.
Use when writing tests for serialization, validation, normalization, or pure functions - provides property catalog, pattern detection, and library reference for property-based testing
Use this skill when designing database schemas for relational (SQL) or document (NoSQL) databases. Provides normalization guidelines, indexing strategies, migration patterns, and performance optimization techniques. Ensures scalable, maintainable, and performant data models.
Use when asked to normalize audio volume, match loudness, or apply peak/RMS normalization to audio files.
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for parser differentials, HTTP normalization gaps, ambiguous headers, path decoding drift, transfer-framing mismatches, and request smuggling routes. Use when the user asks to trace proxy and backend parse differences, conflicting path normalization, Host or forwarded-header ambiguity, CL/TE issues, or routing outcomes that differ across hops. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.