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Found 24 Skills
Use when discussing or working with DeepEval (the python AI evaluation framework)
Write, refine, run, and QA promptfoo evaluation suites: promptfooconfig.yaml, prompts, providers, vars, tests, assertions, model-graded rubrics, transforms, datasets, exports, and CI gates. Use for non-redteam eval coverage, regression tests, or new eval matrices. Do not use for adversarial redteam plugin or strategy setup.
Migrate configuration from Bluejay voice AI testing platform to Coval. Use when customer says "migrate from bluejay", "bluejay migration", "import bluejay config", or needs to transfer agents, simulations, metrics, and schedules from Bluejay to Coval.
Minimal image-understanding smoke test for Model Studio Qwen VL.
AI rules for writing tests with Stably Playwright SDK. Use this skill when writing or modifying Playwright tests with Stably AI features. Covers when to use Playwright vs Stably methods, plus minimal patterns for aiAssert, extract, getLocatorsByAI, agent.act, Inbox, and Google auth.
Full evaluation workflow - launch a run, watch progress, and summarize results. Use for end-to-end agent testing.
Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.
Orchestrator workflow for running ZeroContext Lab (ZCL) attempts/suites with deterministic artifacts, trace-backed evidence, and fast post-mortems (shim support for "agent only types tool name").
The meta skill. Turn any raw feature into a properly-skilled, tested, resolvable unit of agent capability. Cross-modal eval is the recommended Phase 3 quality gate: 3 frontier models from different providers critique the output, you iterate to quality, THEN write tests that lock in the proven-good behavior.
Use when the user wants to measure or set up evals/checks for one of their skills — how fast it is, whether its output is valid, whether it fires when expected, or whether its opening classification/routing gate labels inputs correctly.
Generate realistic synthetic evaluation datasets by analyzing the user's codebase, prompts, production traces, and reference materials. Interactive, consultant-style — asks clarifying questions, proposes a plan, generates a preview for approval, then delivers a complete dataset uploaded to LangWatch. Use when user asks to generate, create, or build a dataset for evaluation, testing, or benchmarking.
Build, debug, balance, or test combat enemy AI for playable action games. Use for aggro, target selection, navigation, spacing, attack choices, telegraphs, retreats, boss behavior, behavior-state machines, and deterministic AI regression tests.