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Found 3,718 Skills
Verify browser-rendered work in a real browser. Use for HTML, UI, visual docs, presentations, local apps, and browser-facing changes.
End-to-end feature addition: takes a feature request in plain English and incrementally writes ***plain specs (concepts, implementation reqs, functional specs, acceptance tests) one functionality at a time, asking, authoring, and reviewing per functionality. Use when the user wants to add a new feature to an existing project.
Executes real-user QA sessions through public interfaces using personas, journeys, exploratory charters, test tours, edge-case probes, CFR checks, and browser evidence. Reads qa-report artifacts from <qa-output-path>/qa/ when present, captures issues/screenshots/reports under the same output tree, and classifies bugs by user impact. Use when validating a release candidate, migration, refactor, or user-facing change against production-like behavior. Do not use for AI implementation audits, task-status reconciliation, CI gate runs, integration/security/performance templates, or flaky-test triage; use agent-output-audit for those.
Use this skill to benchmark shell commands reliably with warmup runs, statistical summaries, and exportable artifacts using hyperfine.
This skill should be used when the user asks to "create a workflow", "create a getlark test", "add an end-to-end test", "author a larkci workflow", or runs `/getlark:create-workflow`. Converts a natural-language test description (target + ordered steps; target may be a URL, API endpoint, CLI binary, script, or any other software surface) into a `getlark workflows create` invocation with an auto-generated name. Prefer `manage` when the user wants to update or archive an existing workflow, and `invoke-workflow` when they want to run one — this skill only *creates* new workflows.
Executes full-project QA like a real user by discovering the repository verification contract, running build, lint, test, and startup commands, exercising core workflows end-to-end, creating realistic fixtures when needed, fixing root-cause regressions, and rerunning the full gate. Use when validating a branch, release candidate, migration, refactor, or risky commit. Do not use for static code review only, one-off unit test edits, or architecture brainstorming without execution.
[BETA] Dogfood the active branch end-to-end as a QA engineer. Diffs the branch against main, builds an exhaustive browser test matrix of every change (full user journeys, not just features), drives the app with agent-browser, then auto-fixes issues, adds regression tests, and commits each fix until the matrix is green. Use when you want a hands-off 'test everything we just built and make it actually work' pass before shipping.
UI test recipe -- composes browser-record (capture) + browser-replay (verify) so every test produces a replayable RVF artifact, not an ephemeral run
Compress an agent's routing file (RESOLVER.md or AGENTS.md) by converting granular skill-per-row tables into functional-area dispatchers. Each area lists sub-skills in a "(dispatcher for: ...)" clause. The LLM reads one area entry and routes to the correct sub-skill. Proven via held-out A/B eval: dispatcher pattern outperforms naive pipe-table compression.
Go programming patterns and idioms
Browser verification, proof screenshots, traces, console and network checks, and reproducible UI evidence for Workbench QA.
Use when a user asks to build, optimize, backtest, rebalance, or analyze a stock portfolio with Mean-CVaR, efficient frontiers, scenario generation, or NVIDIA cuOpt.