Results for “chromatin”

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

Access the COSMIC catalogue of somatic mutations in cancer to query somatic mutations, the Cancer Gene Census, mutational signatures, and gene fusions (authentication required). Use when curating known cancer driver genes, looking up recurrent somatic mutations in a gene, or interpreting mutational signatures for cancer research and precision oncology. Not for germline pathogenicity calls (use alterlab-clinvar) or interactive cohort visualization like OncoPrints and survival from study data (use alterlab-cbioportal). Part of the AlterLab Academic Skills suite.

Store and query embeddings with metadata, vector and full-text search, and filtering. Integrates with LangChain and LlamaIndex for RAG and semantic search applications.

Embedding database for RAG and semantic search.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

Calculates CRAP (Change Risk Anti-Patterns) scores for .NET methods by combining cyclomatic complexity with code coverage data from Cobertura XML reports.

Kit de ferramentas de patologia computacional para análise de imagens de lâminas inteiras (WSI) e dados de imagem multiparamétrica. Use esta habilidade ao trabalhar com lâminas de histopatologia, imagens coradas com H&E, imunofluorescência multiplex (CODEX, Vectra), proteômica espacial, detecção/segmentação de núcleos, construção de gráficos de tecido ou treinamento de modelos ML em dados de patologia. Suporta 160+ formatos de lâmina incluindo Aperio SVS, NDPI, DICOM, OME-TIFF para fluxos de trabalho de patologia digital.

Process and analyze mass spectrometry data with the Matchms Python library, including importing spectra, filtering peaks, calculating similarity scores, and building reproducible analytical workflows.

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

Audits colour decisions on a screen for contrast ratios, palette coherence, semantic meaning, and accessibility, flagging deviations and recommending specific corrections.

Simplify molecular cheminformatics with a Pythonic wrapper around RDKit for SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, and parallel processing.

Simulates meiotic recombination to produce offspring genomes from parent pairs, modeling Mendelian segregation, de novo mutation, sex determination, trait inference, and clinical evaluation against a disease registry.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.

Documents the design system for Composio's marketing site, including color palette, typography, layout, elevation, shapes, and component specifications.

Kotest — flexible, idiomatic Kotlin testing framework. Multiple specification styles (StringSpec, FunSpec, BehaviorSpec, DescribeSpec, FeatureSpec, FreeSpec), rich matcher library, property-based testing, data-driven tests, coroutine support, KMP-friendly. Drop-in alternative or complement to JUnit. USE WHEN: user mentions "Kotest", "io.kotest", "shouldBe", "StringSpec", "BehaviorSpec", "DescribeSpec", "kotest property testing", "Arb.list", "forAll", "kotest matchers", "kotlin tests" DO NOT USE FOR: JUnit-specific patterns - use junit skill (or framework-specific test skills) DO NOT USE FOR: Flow testing - use `testing/turbine` DO NOT USE FOR: Compose snapshot tests - use `testing/compose-snapshot` DO NOT USE FOR: Mobile E2E - use `testing/maestro`

Documents Pinterest's design system including color palette, typography, shape geometry, and layout patterns for the home page, search results, and creator marketing surfaces.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

Cardinality and model family jointly determine the encoding.

변경된 파일을 기능별로 그룹화하여 커밋 생성

Personal CRM built on monday.com. Tracks contacts, last interactions, next meetings, and topics. Runs daily to update from Calendar + email. Delivers pre-meeting briefings as part of morning-briefing. Use when: someone asks about a contact, before a meeting, or during daily CRM sync.

Turbine — small Kotlin testing library for kotlinx.coroutines Flows. Provides ergonomic API to test Flow emissions deterministically: awaitItem, expectMostRecentItem, awaitComplete, awaitError. Works with StateFlow, SharedFlow, Channel-backed flows, combine/map/debounce. KMP-friendly. USE WHEN: user mentions "Turbine", "app.cash.turbine", ".test {}", "awaitItem", "Flow testing", "StateFlow test", "SharedFlow test", "expectMostRecentItem", "cancelAndIgnoreRemainingEvents" DO NOT USE FOR: Mobile E2E - use `testing/maestro` DO NOT USE FOR: Compose snapshot - use `testing/compose-snapshot` DO NOT USE FOR: Generic Kotlin testing - use `testing/kotest` DO NOT USE FOR: Suspend function (non-Flow) testing - use `kotlinx-coroutines-test` directly

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.

Process and analyze mass spectrometry data: import spectra from MGF, mzML, MSP, and JSON formats; apply 40+ filters for metadata harmonization and peak cleaning; compute spectral similarities (cosine, modified cosine) for compound identification; build reproducible processing pipelines.

Conduct passive OSINT reconnaissance on target organizations using a MITRE ATT&CK framework. Discovers employees, email patterns, subdomains, infrastructure, leaked credentials, and cloud assets with confidence-scored findings.

NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance/Innovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget/scope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Use when the user asks about research funding or makes any grant-related request (e.g., 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research'). NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake.

Recursive self-improving holon λ(ο,Κ,Σ).τ' for knowledge compounding and schema evolution. USE WHEN learning, improving, optimizing, assessing, reflecting, debugging, synthesizing, or refining—whether human, AI, or organizational. Triggers on /learn, /compound, /improve, /refine, /optimize, /assess, /reflect, "lessons learned", "best practices", "continuous improvement". Preserves Κ-monotonicity, η≥4, homoiconicity.