test-data
This skill should be used when writing, reviewing, or modifying test code that contains literal values like timestamps, names, IDs, paths, or URLs.
What this skill does
# Test Data Guidelines ## Test data must be self-evidently fake Literal values in tests should be immediately recognizable as fabricated. A reader should never wonder whether a value was copied from production. ### Timestamps Use memorable boundary dates, not specific-looking times that could be real: ``` # Bad — looks like it was recorded from a real system 2023-11-14T22:13:20.000Z # Good — obviously a test value 1999-12-31T00:00:00.000Z 2000-01-01T00:00:00.000Z ``` ### Names and identifiers Use the conventional Alice/Bob/Charlie series, or obviously generic names: ``` # Bad — could be a real person, or the developer's own identity user: "jsmith" email: "[email protected]" # Good — conventional test names user: "alice" email: "[email protected]" # Also good user: "bob" email: "[email protected]" ``` The `example.com` domain is reserved by IETF (RFC 2606) specifically for this purpose. ### Numeric IDs Use round numbers or boundary values, not arbitrary numbers that look like real database IDs: ``` # Bad — looks like a real database ID id: 8847291 # Good — round numbers id: 100 id: 1000 # Good — boundary values id: 0 id: 2_147_483_647 id: 9_999_999 ``` ### Paths and URLs Don't use real usernames, home directories, or system paths. Use generic placeholders: ``` # Bad — contains a real username and home directory path: "/Users/jsmith/.config/app/settings.json" # Good path: "/tmp/test/settings.json" url: "https://example.com/api/v1/resource" ```
Related in Writing & Docs
jax-development
IncludedUse this skill when the user is writing, debugging, profiling, refactoring, reviewing, benchmarking, parallelising, exporting, or explaining JAX code, or when they mention JAX, jax.numpy, jit, grad, value_and_grad, vmap, scan, lax, random keys, pytrees, jax.Array, sharding, Mesh, PartitionSpec, NamedSharding, pmap, shard_map, Pallas, XLA, StableHLO, checkify, profiler, or the JAX repo. It helps turn NumPy or PyTorch-style code into pure functional JAX, fix tracer/control-flow/shape/PRNG bugs, remove recompiles and host-device syncs, choose transforms and sharding strategies, inspect jaxpr/lowering/IR, and benchmark compiled code correctly.
nature-article-writer
IncludedDrafts, rewrites, diagnostically critiques, and style-calibrates primary research manuscripts for Nature and Nature Portfolio journals. Use when the user wants a Nature-style title, summary paragraph or abstract, introduction, results, discussion, methods, figure legends, presubmission enquiry, cover letter, reviewer response, or when a scientific draft sounds generic, jargon-heavy, structurally weak, or AI-ish and needs precise, broad-reader-friendly prose without inventing data, analyses, or references. Best for primary research articles and letters rather than reviews or press releases unless explicitly adapting one.
deckrd
IncludedDocument-driven framework that derives requirements, specifications, implementation plans, and executable tasks from goals through structured AI dialogue. Use when user says "write requirements", "create spec", "plan implementation", "derive tasks", "structure this feature", "break down into tasks", or "document this module". Also use for reverse engineering existing code into docs (/deckrd rev). Do NOT use for direct code writing — use /deckrd-coder after tasks are generated. Do NOT use when the user only wants to run or fix existing code without planning.
clinical-decision-support
IncludedGenerate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.
handling-sf-data
IncludedSalesforce data operations with 130-point scoring. Use this skill to create, update, delete, bulk import/export, generate test data, and clean up org records using sf CLI and anonymous Apex. TRIGGER when: user creates test data, performs bulk import/export, uses sf data CLI commands, needs data factory patterns for Apex tests, or needs to seed/clean records in a Salesforce org. DO NOT TRIGGER when: SOQL query writing only (use querying-soql), Apex test execution (use running-apex-tests), or metadata deployment (use deploying-metadata).
accelint-ac-to-playwright
IncludedConvert and validate acceptance criteria for Playwright test automation. Use when user asks to (1) review/evaluate/check if AC are ready for automation, (2) assess if AC can be converted as-is, (3) validate AC quality for Playwright, (4) turn AC into tests, (5) generate tests from acceptance criteria, (6) convert .md bullets or .feature Gherkin files to Playwright specs, (7) create test automation from requirements. Handles both bullet-style markdown and Gherkin syntax with JSON test plan generation and validation.