浏览器缓存 browser-caching
SkillSkillsMP
io.github.thedaviddias/Front-End-Checklist/browser-caching
排查页面加载缓慢、资源过大或渲染延迟，落实“启用浏览器缓存”时使用。在 DevTools、Lighthouse 或真实数据中确认实际瓶颈后再提改动建议。
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“Data Analysis” 共 934 个结果
io.github.thedaviddias/Front-End-Checklist/browser-caching
排查页面加载缓慢、资源过大或渲染延迟，落实“启用浏览器缓存”时使用。在 DevTools、Lighthouse 或真实数据中确认实际瓶颈后再提改动建议。
io.github.thedaviddias/Front-End-Checklist/browser-required
排查页面加载缓慢、资源过大或渲染延迟，落实“执行浏览器端性能审计”时使用。在 DevTools、Lighthouse 或真实数据中确认实际瓶颈后再提改动建议。
io.github.thedaviddias/Front-End-Checklist/css-file-size
排查页面加载缓慢、资源过大或渲染延迟，落实“优化 CSS 文件体积”时使用。在 DevTools、Lighthouse 或真实数据中确认实际瓶颈后再提改动建议。
io.github.asgeirtj/system_prompts_leaks/requirements-clarification
Clarify a material user-owned product decision before substantive implementation commitments. Use only when request and named/governing evidence still leave a choice that would materially change scope, platform support, user-facing surface, data/API contract, domain values, project placement, or cross-system architecture; also use when a truncated request omits such a choice. On the initial turn, inspect only named or governing requirements evidence, then ask directly without loading: prefer structured input; ask one blocking outcome question or at most three coupled questions; wait. After the answer, load the body before write or delegation. Do not use for implement/build/fix/debug/refactor work when expected behavior is settled, plan/design deliverables owned by Plan, complexity/greenfield/integration alone, agent-owned technical choices, locally discoverable facts, reversible defaults, permission to begin, or stop/no-tool turns. Never re-ask supplied facts or turn clarification into refusal.
io.github.pytorch/pytorch/ci-metrics
查询 PyTorch CI、GitHub Actions、HUD、Grafana 与基础设施指标。当用户询问 CI 耗时、任务失败、排队时长、工作流趋势、runner 健康度、看板数据或 PyTorch 基础设施指标时使用。
io.github.addyosmani/agent-skills/security-and-hardening
加固代码以抵御漏洞。在审计输入处理逻辑、处理用户输入/认证/数据存储/外部集成，或检查登录流程是否符合 OWASP Top Ten 时使用。在构建任何接收不可信数据、管理用户会话或对接第三方服务的功能时使用。在审计依赖已知漏洞、分诊包管理器审计结果或评估新包供应链风险时使用。涉及个人数据或隐私合规（GDPR、CCPA）时使用。
io.github.affaan-m/ECC/change-impact
Perform evidence-backed impact analysis before changing code, data, interfaces, or governance documents, then compare the planned impact with the actual diff after implementation. Covers call paths, data and schema, API contracts, tests, documentation, ADRs, deployment, migration, and rollback. Use for cross-module or high-risk changes, database migrations, authentication, public interfaces, blast-radius questions, pre-change checks, and post-implementation alignment.
io.github.affaan-m/ECC/change-impact
Perform evidence-backed impact analysis before changing code, data, interfaces, or governance documents, then compare the planned impact with the actual diff after implementation. Covers call paths, data and schema, API contracts, tests, documentation, ADRs, deployment, migration, and rollback. Use for cross-module or high-risk changes, database migrations, authentication, public interfaces, blast-radius questions, pre-change checks, and post-implementation alignment.
io.github.K-Dense-AI/scientific-agent-skills/biopython
Provides Biopython workflows for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Supports batch processing, custom molecular-biology pipelines, BLAST automation, structure analysis, and motif analysis.
io.github.K-Dense-AI/scientific-agent-skills/pydeseq2
Performs bulk RNA-seq differential expression analysis with PyDESeq2, including count validation, formula designs, explicit contrasts, Wald tests, FDR correction, coefficient-matched LFC shrinkage, and result visualization. Use for PyDESeq2 or Python DESeq2 workflows with biological replicates.
io.github.K-Dense-AI/scientific-agent-skills/matlab
Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
io.github.K-Dense-AI/scientific-agent-skills/geomaster
Supports geospatial research workflows for remote sensing, vector and raster GIS, spatial statistics, terrain and network analysis, and machine learning for Earth observation. Use when processing satellite imagery, aligning coordinate systems and raster grids, accessing STAC catalogs, analyzing geospatial time series, or implementing scientific GIS workflows in Python, R, Julia, JavaScript, C++, Java, Go, or Rust.
io.github.K-Dense-AI/scientific-agent-skills/opentrons-integration
为 Flex 与 OT-2 机器人编写、审查、迁移、仿真并排查官方 Opentrons Python Protocol API v2 协议。适用于机器人专属的液体处理、deck 与 labware 设置、移液器、模块、运行时参数、液体类别，以及 Opentrons App 分析。当同一工作流需要支持多品牌机器人时改用 pylabrobot。
io.github.K-Dense-AI/scientific-agent-skills/networkx
使用 NetworkX 在 Python 中创建、分析和可视化复杂网络与图。适用于处理网络/图数据结构、运行图算法（最短路径、中心性、聚类）、社区发现、生成合成网络（随机图、无标度图、小世界图）、读写图文件格式，或绘制网络拓扑。常见应用包括社交、生物、交通与引文网络。
io.github.K-Dense-AI/scientific-agent-skills/statsmodels
Python 统计模型库。当需要具体的模型类（OLS、GLM、混合模型、ARIMA）并要详尽的诊断、残差与推断时使用。最适合计量经济学、时间序列，以及带系数表的严格推断。需要引导式统计检验选择与 APA 报告时使用 statistical-analysis。
io.github.K-Dense-AI/scientific-agent-skills/optimize-for-gpu
将科学 Python 工作负载加速到 NVIDIA GPU 上，并验证结果正确且更快。适用于 CUDA/GPU 优化；CPU 受限的 NumPy、SciPy、pandas、scikit-learn、NetworkX、scikit-image、向量检索、图像处理、图、仿真或文件 I/O 负载；CuPy、cuDF、cuML、cuGraph、cuVS、cuCIM、KvikIO、Warp、Newton、Numba-CUDA、RAFT 相关问题；以及性能剖析、内存搬运、kernel 或多 GPU 瓶颈。当大规模数据并行的 Python 代码很慢且 GPU 加速是合理选项时也应使用，即使用户没有提到 CUDA。
io.github.K-Dense-AI/scientific-agent-skills/astropy
面向需要 Astropy API 的天文与天体物理工作流的核心 Python 库，涵盖单位/量纲、坐标、FITS I/O、表格、时间系统、WCS 与宇宙学。在使用 Astropy 实现或调试天文数据分析代码时使用。
io.github.K-Dense-AI/scientific-agent-skills/pysam
基于 Python/HTSlib 的基因组文件工作流。在使用 pysam 读取、查询、过滤或写出 SAM/BAM/CRAM、VCF/BCF、FASTA/FASTQ 或 tabix 数据时使用，包括 pileup、覆盖度、索引与 CRAM 参考。
io.github.K-Dense-AI/scientific-agent-skills/paperclip
使用 GXL 的 Paperclip CLI 检索和阅读生物医学论文全文、FDA/PMDA/EMA 监管文件、临床试验注册信息和 UniProt/PDB/ChEMBL 条目。涵盖用 PAPERCLIP_API_KEY 安装与鉴权 `paperclip` 二进制、/papers、/fda、/trials、/proteins、/clipboard 下的只读虚拟文件系统、来源范围的语义搜索、全语料 grep、元数据查询与 SQL、跨多篇论文的 map/reduce 阅读、图像视觉分析、带论断核验的可选论文仓库，以及锁定行号的引用。当被要求安装 paperclip、运行 paperclip search/grep/map/reduce/sql/repo、通过 paperclip 查找或阅读生物医学文献、监管文件或临床试验，或生成带行号的引用时使用。
io.github.K-Dense-AI/scientific-agent-skills/bioservices
40+ 生物信息学服务的统一 Python 接口。适用于在单个工作流中查询多个数据库（UniProt、KEGG、ChEMBL、Reactome）并使用一致的 API。最适合跨库分析与跨服务 ID 映射。快速单库查询用 gget；序列/文件操作用 biopython。
io.github.K-Dense-AI/scientific-agent-skills/gget
对 20+ 生物信息学数据库的快速 CLI/Python 查询。适合快速查询：基因信息、BLAST/BLAT、病毒序列下载、AlphaFold 结构、富集分析、OpenTargets、COSMIC、CELLxGENE 以及 8cube 小鼠特异性/表达数据。最适合交互式探索和简单查询。批量处理或高级 BLAST 用 biopython；跨库 Python 工作流用 bioservices。
io.github.affaan-m/ECC/ml-adoption-playbook
End-to-end methodology for AI agents and software engineers to add machine learning algorithms to existing non-ML codebases. Covers problem framing, data readiness, architectural decoupling, and baseline model integration. Use when adding a machine learning capability to a codebase that has none, from problem framing through a baseline model.
io.github.affaan-m/ECC/pytorch-patterns
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading. Use when writing or reviewing PyTorch training loops, model architectures, or data loading, or when a run will not reproduce.
io.github.affaan-m/ECC/production-audit
Local-evidence production readiness audit for shipped apps, pre-launch reviews, post-merge checks, and "what breaks in prod?" questions without sending repo data to an external audit service. Use when auditing production readiness before launch, after a merge, or when asked what breaks in prod.