1.7万 件 ・ 関連度順
概要と使いどころ
End-to-end ATAC-seq workflow from FASTQ files to differential accessibility and TF footprinting. Covers alignment, peak calling with MACS3, QC metrics, and optional TOBIAS footprinting. Use when running end-to-end ATAC-seq analysis from FASTQ to differential accessibility.
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Detect allele-specific chromatin accessibility from ATAC-seq using WASP, GATK ASEReadCounter, or RASQUAL. Use when mapping cis-regulatory genetic variants from heterozygous SNPs, separating cis from trans regulation, building chromatin QTL (caQTL) maps, validating GWAS variant function with allelic imbalance, or detecting reference allele mapping bias before downstream analysis.
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SPR and BLI assay planning, kinetic interpretation, and troubleshooting guidance. Use when: (1) Planning binding kinetics experiments, (2) Troubleshooting poor/no binding signal, (3) Interpreting kinetic data artifacts, (4) Choosing between SPR vs BLI platforms.
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CodeRabbit reviews pull requests with AI on GitHub, GitLab, Azure DevOps and Bitbucket, and reviews local changes from a CLI before they are pushed. Use when a user asks to set up automated PR reviews, write or validate a .coderabbit.yaml, add path-specific review instructions, control a review with @coderabbitai commands, or run coderabbit review from the terminal or a coding agent.
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Manage repositories, pipelines, and code review with Bitbucket Cloud. Use when a user asks to set up Bitbucket repositories, configure Bitbucket Pipelines for CI/CD, manage pull requests, set up branch permissions, use Bitbucket REST API 2.0, create webhooks, manage deployment environments, set up code review workflows, integrate with Jira, configure merge checks, or automate repository operations. Covers repository management, CI/CD pipelines, code review, deployments, and Atlassian ecosystem integration.
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Audit web pages and components for WCAG 2.2 accessibility compliance. Use when a user asks to check accessibility, find a11y issues, audit for WCAG compliance, fix screen reader problems, check color contrast, ensure keyboard navigation works, or prepare for accessibility regulations like the European Accessibility Act or ADA.
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Accessibility Audit and Testing workflow skill. Use this skill when the user needs You are an accessibility expert specializing in WCAG compliance, inclusive design, and assistive technology compatibility. Conduct audits, identify barriers, and provide remediation guidance and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
日本語の概要は準備中です。原文の説明を表示しています。
Accessibility Audit and Testing workflow skill. Use this skill when the user needs You are an accessibility expert specializing in WCAG compliance, inclusive design, and assistive technology compatibility. Conduct audits, identify barriers, and provide remediation guidance and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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数据工程 — 数据平台从业者的认知操作系统, 覆盖把数据从源系统搬运成可靠 / 可查询 / 可信赖形态供分析 / ML / 数据产品消费的全生命周期 (生成 → 摄取 → 存储 → 转换 → 服务 + 安全/数据管理/DataOps/数据架构/编排/软件工程 六条暗流, Reis & Housley 框架): 摄取与集成 (批 + CDC 变更数据捕获 Debezium + EL 工具 Fivetran/Airbyte/Meltano/dlt + Kafka Connect + schema drift) / 存储与文件表格式 (对象存储数据湖 + 列存 Parquet/ORC/Arrow/Avro + 开放表格式 Apache Iceberg/Delta Lake/Apache Hudi + lakehouse + 分区/compaction) / 转换与建模 (ELT dbt/SQLMesh + Spark + 维度建模 Kimball + Inmon + Data Vault + 大宽表 OBT + 渐变维 SCD + 增量模型 + 语义/指标层) / 编排与工作流 (Apache Airflow/Dagster/Prefect/Mage/Kestra/Apache DolphinScheduler + DAG + 幂等 + 回填 backfill + 数据资产调度) / 批流与实时 (Apache Kafka/Apache Flink/Spark Structured Streaming/Kinesis/Pulsar/Redpanda + Lambda vs Kappa + watermark/窗口/exactly-once + 流式 SQL Materialize/RisingWave + 实时 OLAP ClickHouse/Apache Druid/Apache Pinot/StarRocks/Apache Doris) / 数仓与查询引擎 (Snowflake/BigQuery/Redshift/Databricks SQL/Trino/Presto/DuckDB/Polars + 存算分离 + MPP) / 数据质量测试与可观测性 (dbt tests/Great Expectations/Soda + 数据契约 + Monte Carlo data downtime + 新鲜度/量/schema 异常检测) / 数据治理编目与血缘 (DataHub/Amundsen/OpenMetadata/Unity Catalog + 列级血缘 + PII 分类 + 访问控制 + GDPR) / DataOps 与可靠性 (数据 CI/CD + 转换版本控制 + 环境隔离 + 幂等重处理 + 数据 SLA/SLO + 计算存储 FinOps) / 数据架构范式 (现代数据栈 + lakehouse + data mesh + data fabric + 去中心化 vs 中心化所有权) / 分析工程角色 (dbt 时代连接数据工程与分析的桥) — 不含 数据科学/ML 建模本身 (是下游消费者) / BI 仪表盘制作 (serving 下游) / 数据分析报表为终点 / 'data engineer = 跑 Hadoop 的' 过时窄化 / 通用后端应用开发 (平行学科) (Data Engineering — the cognitive operating system of practitioners who design, build, and operate the data platform: moving data from source systems into reliable, queryable, trustworthy form for analytics / ML / products, covering (a) the data engineering lifecycle (generation → ingestion → storage → transformation → serving, with the undercurrents security / data management / DataOps / data architecture / orchestration / software engineering — Reis & Housley framing), (b) ingestion & integration (batch + CDC change-data-capture with Debezium, EL tools Fivetran / Airbyte / Meltano / dlt, Kafka Connect, API + file + database sources, schema drift handling), (c) storage & file/table formats (object storage data lakes, columnar formats Parquet / ORC / Arrow / Avro, open table formats Apache Iceberg / Delta Lake / Apache Hudi, lakehouse architecture, partitioning / compaction / Z-ordering), (d) transformation & modeling (ELT with dbt / SQLMesh, Spark, dimensional modeling Kimball, Inmon CIF, Data Vault, One Big Table / wide tables, normalization vs denormalization, slowly changing dimensions, incremental models, the semantic / metrics layer), (e) orchestration & workflow (Apache Airflow, Dagster, Prefect, Mage, Kestra, Apache DolphinScheduler, DAGs, idempotency, backfills, data-aware / asset-based scheduling), (f) batch vs streaming & real-time (Apache Kafka, Apache Flink, Spark Structured Streaming, Kinesis / Pulsar / Redpanda, the Lambda vs Kappa debate, watermarks / windowing / exactly-once, streaming SQL Materialize / RisingWave, real-time OLAP ClickHouse / Apache Druid / Apache Pinot / StarRocks / Apache Doris), (g) warehouses & query engines (Snowflake, BigQuery, Redshift, Databricks SQL, Trino / Presto, DuckDB, Polars, decoupled storage & compute, MPP), (h) data quality, testing & observability (dbt tests, Great Expectations, Soda, data contracts, Monte Carlo / data downtime, freshness / volume / schema anomaly detection, unit / integration testing of pipelines), (i) data governance, catalog & lineage (DataHub, Amundsen, OpenMetadata, Unity Catalog, column-level lineage, PII / data classification, access control, GDPR / data privacy), (j) DataOps & reliability (CI/CD for data, version control of transformations, environments, idempotent reprocessing, SLAs / SLOs for data, cost / FinOps for compute & storage), (k) data architecture paradigms (modern data stack, data lakehouse, data mesh, data fabric, decentralized vs centralized ownership), (l) the analytics engineering role (the dbt-era bridge between data engineering and analysis); N
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Query patent simple bibliographic (catalog) data from the Nexscope patent database. Triggered when users mention patent bibliographic information queries, patent basic info retrieval, patent catalog data, patent publication details, searching by patent number for inventors, patent applicant information, patent abstract retrieval, patent classification codes (IPC/CPC), patent citation queries, or any request to search for structured metadata by patent ID or publication number, patent brief bibliography, patent basic info, patent number lookup, patent abstract, Nexscope, or patent metadata. Even if the user does not explicitly mention "Nexscope" or "bibliographic info," this skill should be triggered whenever their need involves querying core bibliographic fields of specific patents.
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Nexscope keyword Share of Voice analysis, returning brand visibility share across the first 3 pages of Amazon search results (organic/ad/combined), 30-day exact search volume, median PPC bid, and TOP3 ASIN click and conversion data, covering 10 marketplaces. Trigger when the user mentions brand market share, brand voice share, search result brand distribution, Share of Voice, SOV analysis, brand competitive landscape, ad slot share, organic ranking brand share, PPC bid analysis, brand monopoly analysis, keyword share of voice, brand visibility, organic vs sponsored share, brand dominance, PPC bid analysis, search result brand distribution, competitive landscape, weighted SOV, top ASIN clicks conversions. Even if the user does not explicitly mention "Share of Voice" or "SOV", if their need involves analyzing brand market share or competitive landscape in Amazon keyword search results, this skill should also be triggered.
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Use when interpreting biological research findings, evaluating life science evidence, analyzing molecular or cellular mechanisms, comparing competing biological hypotheses, designing or critiquing experiments in biology, genetics, genomics, cell biology, immunology, neuroscience, ecology, or any life science domain. Triggers on questions about gene function, pathways, phenotypes, GWAS hits, single-cell data, animal models, clinical translation, evolutionary arguments, or any biology/life science reasoning task.
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Use when asked to design iOS or Android mobile app screens, create mobile UI, spec mobile flows, or produce screen designs for a native app. Examples: "design the onboarding screens", "spec the checkout flow for iOS", "design a home screen for Android", "create mobile UI for this feature".
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Recognition and mitigation of systematic reasoning errors documented in the heuristics and biases tradition. Covers confirmation bias, availability, anchoring, representativeness, framing effects, hindsight bias, overconfidence, and motivated reasoning. Each bias is presented with its mechanism, diagnostic signal, and structured mitigation. Use when you suspect your own or another reasoner's conclusions may be shaped by systematic cognitive distortion rather than evidence.
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Design new proteins that bind a chosen surface, using BindCraft's AlphaFold2-guided hallucination or the RFdiffusion backbone plus ProteinMPNN sequence pipeline. Use this skill to specify a target epitope by hotspot residue, trim a receptor to the region worth designing against, set up a design campaign, and filter the output on the in-silico metrics that predict experimental success — interface predicted TM-score, predicted aligned error at the interface, buried surface area, and shape complementarity. Also trigger on BindCraft, RFdiffusion, ProteinMPNN, minibinder, hallucination, inverse folding, hotspot residue, epitope targeting, ipTM, or de novo binder.
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Review user experience and interface design using Nielsen's heuristics, WCAG 2.1 accessibility guidelines, keyboard and screen reader audit, user flow analysis, cognitive load assessment, and form usability evaluation. Use when conducting a usability review before release, assessing WCAG 2.1 accessibility compliance, evaluating user flows for efficiency, reviewing form design, or performing a heuristic evaluation of an existing interface.
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Probe the runtime state of a named feature flag in a CLI binary. Covers the four-pronged evidence protocol (binary strings, live invocation, on-disk state, platform cache), the four-state classification (LIVE / DARK / INDETERMINATE / UNKNOWN), gate-vs-event disambiguation, conjunction-gate handling, and skill-substitution scenarios where a flag appears DARK but the capability is delivered by other means. Use when verifying whether a documented or inferred capability has rolled out, when auditing dark-launched features, or when a prior probe's conclusions need refreshing against a new binary version.
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Establish and maintain longitudinal baselines of CLI binary contents across versions. Covers marker selection by category (API / identity / config / telemetry / flag / function), weighted scoring, threshold-based system-presence detection, and per-version baseline records. Use when tracking a feature's lifecycle across releases, when probing for dark-launched or removed capabilities, or when verifying that a scanning tool itself still catches known-good markers on old binaries.
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Fix stale Haskell analyzer binaries silently being used by the orchestrator even after a successful rebuild. Use when: (1) modified `packages/js-analyzer/` or other Haskell analyzer source, ran `scripts/build-native.sh ... cabal install`, but a subsequent `grafema analyze` shows old behavior (missing new edges, no new node types, etc.), (2) `~/.cabal/bin/grafema-analyzer` has fresh timestamp but `grafema analyze` produces output as if the change didn't happen, (3) a test verifying new analyzer output passes locally in unit tests but fails in end-to-end runs that go through the orchestrator. Root cause: the Rust orchestrator's `resolve_binary` checks `~/.grafema/bin/<name>` BEFORE `~/.cabal/bin/<name>`, so any stale copy that was once installed there (possibly months ago) shadows the fresh `cabal install` output.
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"Net-zero transition playbook: GHG inventory (ISO 14064-1, GHG Protocol Scope 1/2/3), science-based targets (SBTi), TCFD and ISSB S1/S2 climate disclosure, Taiwan carbon fee, decarbonization roadmap, internal carbon pricing, supply-chain carbon, product carbon footprint, green electricity and T-REC. Use for corporate decarbonization roadmap, carbon inventory, carbon-fee response, TCFD/ISSB reporting, supply-chain decarbonization, or responding to international customers (Apple, RE100). Triggers: 『碳盤查』『SBTi』『碳費』『TCFD』『淨零路徑』『範疇 3』『綠電』『CBAM』『ISO 14064』『ISSB』『T-REC』. For Taiwan EMBA ESG/sustainability/operations/strategy/finance cases. Focuses on Taiwan listed-company and supply-chain practice; sustainability strategy theory: use Asgard `grad-sustainability`; ERM 框架整合氣候風險: use `biz-erm`; 董事會 ESG 委員會結構設計: use `biz-corporate-governance`; 併購 ESG 盡職調查: use `fin-m-and-a`.".
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Uses capa to identify malware capabilities from a binary: running rule-based detection over disassembly, mapping matched capabilities to MITRE ATT&CK and MBC, and interpreting results to focus deeper analysis. Activates for requests to run capa, generate a capability report, or map a sample's behaviors to ATT&CK automatically.
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Use when a task needs the judgment of a biomedical/bioengineering engineer — running a design control phase (inputs, outputs, verification, validation) for a medical device, scoring a Design FMEA and deciding whether a failure mode needs mitigation, planning a human factors/usability validation study, building a design traceability matrix, or writing a design verification or risk-mitigation report. Distinct from a regulatory-affairs-specialist (owns the submission/clearance process, not the engineering itself) and from a clinical-data-manager/biostatistician (trial data and analysis, not device design).
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Use when a task needs the judgment of a Biofuels/Biodiesel Technology and Product Development Manager — deciding whether a lab/bench-scale process is ready to scale up, evaluating feedstock flexibility tradeoffs in process design, pricing a technology's value against regulatory credit pathways (RFS RIN, LCFS CI score), or managing IP strategy for a novel conversion process. Distinct from biofuels-production-manager, which runs an existing operating facility — this role owns R&D and commercialization of new or improved process technology.
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Install bioinformatics tools for ENCODE data analysis. Covers CLI tools (BWA, STAR, samtools, MACS2), R/Bioconductor packages (DESeq2, Seurat, ChIPseeker), Python packages (Scanpy, deeptools), and Nextflow pipeline infrastructure. Generates conda environments, R install scripts, and Python requirements. Use when the user needs to set up a bioinformatics workstation, install tools for a specific assay, create reproducible environments, or troubleshoot dependency issues. Trigger on: install tools, set up environment, conda create, bioinformatics setup, install R packages, install Bioconductor, install pipeline tools.
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