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Found 2,139 Skills
Provides file paths to language-specific reference files for the test ANALYSIS skills (assertion-quality, test-anti-patterns, test-gap-analysis, test-smell-detection, test-tagging). Call this skill to discover available extension files (e.g., dotnet.md for .NET/MSTest/xUnit/NUnit/TUnit, python.md for pytest/unittest, typescript.md for Jest/Vitest/Mocha, java.md for JUnit/TestNG, etc.). Do not use directly — invoked by the test-quality-auditor agent and polyglot analysis skills that need framework-specific lookup tables (test markers, assertion APIs, skip annotations, sleep patterns, mystery guest indicators, integration markers, setup/teardown, tag-support capability).
Create and manage test data with factory patterns, fixture strategies, data anonymization, and synthetic data generation. Covers Fishery (TypeScript), FactoryBot (Ruby), Factory Boy (Python), database seeding, cleanup strategies, and GDPR-compliant data handling. Use when: "test data," "fixtures," "factories," "seed data," "synthetic data," "test database," "data anonymization." Not for: migration/integrity testing of the DB itself — use database-testing; environment provisioning and database branching strategy — use test-environments. Related: test-environments, database-testing, api-testing, unit-testing.
Multi-agent review-and-improve loop for a GitHub PR you have checked out — posts a "starting" PR comment cc'ing the original author, runs requested rounds plus any adaptive continuation, applies fix commits to the local branch after each round, pushes everything back to the PR, then edits the starting comment in-place with the synthesized report (or a failure summary). Auto-detects the PR from the currently checked-out branch when no locator is supplied. Use when the user wants to "improve a PR", "review and commit fixes", "iterate on my PR", or "review and push back" against a checked-out PR branch. Requires `gh`, `uuidgen`, `jq`, and `uv` or `python3` on PATH. Activates the `review-anvil` engine in per_fix mode.
Workspace locales and translations for Factorial Code — i18n/<locale>.yaml locale files, the fcode.i18n(key, args) runtime helper in JavaScript and Python, fcode.i18n("key") tokens in form schemas, execution-locale selection (Fcode-Locale header, ?locale=, schedules), inheritance and primary-locale fallback, locale versioning, and the fcode i18n:* CLI commands. Use when adding a locale, translating or internationalizing existing process code or form text, calling fcode.i18n, testing with fcode run --locale, or syncing translations with i18n:push.
Installing, configuring, and running @sasjs/server — the open-source NodeJS wrapper around the SAS binary that provides a REST API, filesystem (SASjs Drive), Stored Program execution, and web app streaming. Covers desktop vs server modes, runtime configuration (SAS/JS/Python/R), environment variables, auth (tokens, LDAP), and mock server types. Use when deploying, troubleshooting, or developing against sasjs/server.
Service metrics, RED metrics (Rate, Errors, Duration), and runtime-specific telemetry for .NET, Java, Node.js, Python, PHP, and Go applications.
Grades a specified set of test methods individually and produces a concise table mapping each test (fully-qualified name) to a letter grade (A–F), a score band, and a one-line note — designed to be posted as a PR comment. Use when the caller wants per-test feedback on a curated list of methods (for example, the new or modified tests in a pull request), not a suite-wide audit. Polyglot: .NET, Python, TS/JS, Java, Go, Ruby, Rust, Swift, Kotlin, PowerShell, C++. Input is a list of test methods (or method bodies / file+line spans); output is a compact markdown table plus a short summary. DO NOT USE FOR: full suite audits (use test-quality-auditor agent or test-anti-patterns), writing new tests (use code-testing-generator agent or writing-mstest-tests), fixing failures, or measuring code coverage.
Execute code and manage compute on Databricks: run Python/Scala/SQL/R via serverless, classic, or interactive clusters, and create/resize/delete clusters and SQL warehouses.
Build Zerobus Ingest clients for near real-time data ingestion into Databricks Delta tables via gRPC. Use when creating producers that write directly to Unity Catalog tables without a message bus, working with the Zerobus Ingest SDK in Python/Java/Go/TypeScript/Rust, generating Protobuf schemas from UC tables, or implementing stream-based ingestion with ACK handling and retry logic.
Provides guidance for authoring Apache Airflow DAGs in Managed Service for Apache Airflow (MSAA; formerly Cloud Composer). Covers environment context discovery, Airflow 2 vs 3 compatibility, authoring best practices, and local/remote validation processes. Use when creating or extending an Airflow DAG. Don't use when authoring Python code unrelated to Airflow DAGs.
Scan an experiment repo and generate a complete paper outline (H1/H2/H3) with user approval checkpoints at each level, then generate body text with evidence annotations, citations, and bilingual output. Python ML repos. 扫描实验仓库，逐级生成论文大纲（H1/H2/H3），每级用户确认后推进， 然后生成带证据标注、引用和双语输出的正文文本。
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.