943 skills · sorted by relevance
Data ingestion patterns for loading data from cloud storage, APIs, files, and streaming sources into databases. Use when importing CSV/JSON/Parquet files, pulling from S3/GCS buckets, consuming API feeds, or building ETL pipelines.
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Instantiate or update a workspace `UNITS.csv` from a selected pipeline and units template (deps/checkpoints/acceptance).
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Seederive 非结构化数据打标平台，使用 LLM 对文本、语音、图片数据进行批量分析处理。 当用户提到以下任何场景时必须使用此 Skill：数据打标、标注、情感分析、标签分类、 观点提取、翻译、评论分析、水军识别、内容评分、标签库管理、提示词优化。 即使用户没有直接提到「Seederive」，只要涉及对一批文本做分类/打标/分析/翻译/评分， 或者提到「帮我分析这些评论」「这些数据的情感是什么」「识别水军」「提取观点」 「翻译这批内容」「建个标签体系」「效果不好帮我优化」等表述，都应触发此 Skill。 也适用于用户提供 CSV/Excel 文件要求批量处理的场景。
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清洗原始数据并生成论文级图表，处理缺失值、异常值、单位与格式不一致，产出加载报告、数据计划、图表索引、可复现的图文件与图注。当用户提供 csv 或 xlsx 原始附件需要清洗、需要为某一问画图或补图注、或数据与可视化计划待执行时使用。
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Use when you need to write unit tests for Spring Boot applications — including pure unit tests with @ExtendWith(MockitoExtension.class) for @Service/@Component, slice tests with @WebMvcTest and @MockitoBean for controllers, @JsonTest for JSON serialization, parameterized tests with @CsvSource/@MethodSource, test profiles, and @TestConfiguration. For framework-agnostic Java use @131-java-testing-unit-testing. For integration tests use @322-frameworks-spring-boot-testing-integration-tests. This should trigger for requests such as Review Java code for Spring Boot unit tests; Apply best practices for Spring Boot unit tests in Java code; Write Mockito-first unit tests for Spring Boot services; Avoid unnecessary @SpringBootTest in Spring unit tests; Review Spring Boot slice-free unit test design. Part of Plinth Toolkit
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今日头条爆款内容查询 — 输入关键词搜索今日头条最新作品（图文/视频），支持按阅读量/时间排序、限定时间范围，终端表格展示 + CSV 导出 + 交互式 HTML 报告。当用户需要搜索今日头条热门内容、追踪头条热点、分析爆款文章时使用。
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海外跨平台热文搜索 — 输入任意关键词（中文/英文），一次搜索 X / TikTok / YouTube 三平台热门内容，每平台 Top N（默认5）统一列表（平台/标题/作者/播放/点赞/评论/发布时间/链接），终端分组表格 + CSV 导出 + 交互式 HTML 报告（卡片/表格双视图）。按关键词语言智能优先同语言内容（中文词→中文优先，英文词→英文优先，其余语言兜底）。当用户需要搜索海外平台热点、跨平台内容对比、话题/竞品舆情监控、选题素材采集时使用。
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公众号搜索爬虫 — 通过关键词搜索微信公众号文章，终端表格展示（标题/作者/阅读/点赞/分享/收藏/发布时间/文章链接），自动导出 CSV 并生成交互式 HTML 报告。当用户需要搜索、爬取或浏览公众号文章时使用；也适用于行业热点追踪、竞品内容分析、素材灵感搜集等场景。
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数据分析全流程：体检脏表、清洗、定口径、算指标、对账、出报告。处理 Excel/CSV/系统导出数据，做透视写公式，查两个数为什么对不上，判断一个数靠不靠谱时用。核心是让算出来的数字经得起追问。幻灯片和 PPT 交给 huashu-design。
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This skill handles file format conversions across documents (PDF, DOCX, Markdown, HTML, TXT), data files (JSON, CSV, YAML, XML, TOML), and images (PNG, JPG, WebP, SVG, GIF). Use when the user requests converting, transforming, or exporting files between formats. Generates conversion code dynamically based on the specific request.
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Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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Convert various file formats (PDF, Office documents, images, audio, web content, structured data) to Markdown optimized for LLM processing. Use when converting documents to markdown, extracting text from PDFs/Office files, transcribing audio, performing OCR on images, extracting YouTube transcripts, or processing batches of files. Supports 20+ formats including DOCX, XLSX, PPTX, PDF, HTML, EPUB, CSV, JSON, images with OCR, and audio with transcription.
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QA engineering workflows on top of Playwright. Use when a QA engineer asks to design test cases from a ticket, PRD or acceptance criteria, decide what to automate, write a bug report from a failing test or trace, convert manual test cases (TestRail, Xray, CSV) into Playwright specs with case-ID traceability, or review Playwright test code and pull requests.
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Reconstruct evidence-based work descriptions and append user-confirmed durations to TIME.csv. Use for time logging and corrections; never infer hours or billing from activity.
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Maintain the canonical TASKS.csv with permanent IDs, provenance and lifecycle history; list, add, update, complete, cancel or import selected actions.
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Find and compare current FF&E candidates from a design brief, with sourced purchasing facts and optional CSV-library save. Use to research or source products; not to match from an image or pair coordinating items.
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Clean a local FF&E CSV schedule by normalizing casing, dimensions, units, language, materials, and formatting. Use when asked to clean, fix, or standardize product data.
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Maintain an append-only project INVOICES.csv ledger from explicit billing facts and recorded lifecycle evidence; never infer charges or payment from activity or time logs.
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Query ClinicalTrials.gov API v2 for trial data. Search by condition, drug/intervention, location, sponsor, or phase; fetch details by NCT ID; filter by status; paginate; export CSV. For clinical research, patient matching, and trial portfolio analysis.
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Core Python library for astronomy/astrophysics: units with dimensional analysis, celestial coordinate transforms (ICRS/Galactic/AltAz/FK5), FITS I/O, tables (FITS/HDF5/VOTable/CSV), cosmology (Planck18, distance/age), precise time (UTC/TAI/TT/TDB, Julian, barycentric), WCS pixel-world mapping, model fitting. For general tables use pandas/polars; for radio interferometry use CASA.
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Fast in-memory DataFrame with lazy evaluation, parallel execution, Arrow backend. Use for tabular data in RAM (1–100 GB) when pandas is too slow. Expression API: select, filter, group_by, joins, pivots, window. Lazy mode enables predicate/projection pushdown. Reads CSV, Parquet, JSON, Excel, DBs, cloud. Larger-than-RAM: Dask; GPU: cuDF.
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Out-of-core DataFrame for billion-row data via lazy evaluation and memory-mapped files. Use when data exceeds RAM (10 GB–TB) for fast aggregation, filtering, virtual columns, and visualization without loading. Supports HDF5, Arrow, Parquet, CSV with cloud (S3, GCS, Azure). Built-in ML transformers (scaling, PCA, K-means). In-memory: polars; distributed: dask.
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Parse/write FCS (Flow Cytometry) files v2.0-3.1. Events as NumPy, channel metadata, multi-dataset files, CSV/FCS export. Use FlowKit for gating/compensation.
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Auto-annotate plasmids with features (promoters, terminators, resistance, origins, tags, fluorescent proteins) via BLAST against curated DBs (Addgene, fpbase, SnapGene). FASTA or raw sequence in; annotated GenBank, interactive HTML maps, CSV tables out. Handles circular topology. Use to verify synthetic constructs, prep Addgene submissions, share maps, or batch-annotate cloning libraries.
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