Author skipped Vitest spec files for newly added non-beta ai-projects features under test/. Use when new GA (non-beta) classes, methods, or namespaces appear in review/ai-projects-node.api.md and need test coverage. Generates .skip-ped specs with TODO markers so a human can add recordings, then verifies the file compiles via npm run test:node.
npx skills add https://github.com/Azure/azure-sdk-for-js --skill author-tests
apply-post-emitter-edits has merged in new public API surface AND that surface is not under project.beta.*.test/.author-samples skill (or recompute via ../author-samples/prompts/diff-api-surface.prompt.md).Run from sdk/ai/ai-projects/.
From the api-surface diff, drop every entry where isBeta === true or where the namespace path matches *.beta.* / Beta*. Beta surface is intentionally not test-covered by this skill.
For each remaining feature, generate a Vitest spec at test/<feature>.spec.ts (or extend an existing related spec) using templates/test-skeleton.spec.ts.
Conventions taken from test/snippets.spec.ts:
it, describe from vitest; AIProjectClient from ../src/index.js; DefaultAzureCredential from @azure/identity; types via import type.process.env["FOUNDRY_PROJECT_ENDPOINT"], model from process.env["FOUNDRY_MODEL_NAME"].it.skip(...) with a TODO(<feature>): unskip after recording added comment for every new test, so the suite stays green until a human captures recordings.npm run test:node
.skip-ped tests will not run, but the file must compile and lint clean. Fix any TS or ESLint errors.
Done. Next: the update-changelog skill.
assets.json, _recordings/). Surface to the human if a recording is needed.apply-post-emitter-edits. If a method on the public surface was renamed (e.g. listSessionFiles → getSessionFiles), npm run test:node will fail to compile *existing* specs that referenced the old name. Patch those call sites as part of this skill. Cross-reference the rename table in ../apply-post-emitter-edits/references/post-emitter-workarounds.md.npm run test:node will report runtime failures (missing env vars like FOUNDRY_PROJECT_ENDPOINT, missing recordings) for the *non-skipped* tests. Those are expected in this skill — only treat TypeScript / ESLint errors as blockers.Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take azure/author-tests from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.