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Found 2,038 Skills
End-to-end GECX/CXAS/CES conversational agent lifecycle -- build agents from requirements (PRD-to-agent), create and run evals (goldens, simulations, tool tests, callback tests), debug failures, and iterate to production quality. Use this skill whenever the user mentions GECX, CXAS, CES, SCRAPI, conversational agents, voice agents, audio agents, agent evals, pushing/pulling/linting agents, or agent instructions/callbacks/tools on the Google Customer Engagement Suite platform.
Cross-model benchmark for gstack skills. Runs the same prompt through Claude, GPT (via Codex CLI), and Gemini side-by-side — compares latency, tokens, cost, and optionally quality via LLM judge. Answers "which model is actually best for this skill?" with data instead of vibes. Separate from /benchmark, which measures web page performance. Use when: "benchmark models", "compare models", "which model is best for X", "cross-model comparison", "model shootout". (gstack) Voice triggers (speech-to-text aliases): "compare models", "model shootout", "which model is best".
Benchmark CodeGraph retrieval quality on a real codebase by comparing agent behavior with vs without CodeGraph. Use when the user runs /agent-eval or asks to test, benchmark, audit, or validate a codegraph version (the local dev build or a published npm version) against a language's repo.
Ensure WCAG AA accessibility and readability
Research domain WHOIS data and check marketplace listings. Use when the user says "domain lookup", "check domain", "WHOIS", "domain availability", "buy domain", "domain research", "who owns this domain", "domain marketplace", or asks about researching or acquiring a domain name.
Copilot agent that assists with comprehensive QA strategy and test planning to ensure product quality through systematic testing and quality metrics Trigger terms: QA, quality assurance, test strategy, QA plan, quality metrics, test planning, quality gates, acceptance testing, regression testing Use when: User requests involve quality assurance tasks.
Use when you want rubric based LLM quality scoring on generated outputs; pair with addon-deterministic-eval-suite.
Expert methodology for analyzing and summarizing research papers, extracting key contributions, methodological details, and contextualizing findings. Use when reading papers from PDFs, DOIs, or URLs to create structured summaries for researchers.
Search for jobs matching my resume and preferences
Generate a test suite of natural-language → SQL pairs that becomes the quality benchmark for a nao agent, then run it via `nao test`. Use when the user wants to start measuring agent reliability, extend an existing test suite, or add tests for new metrics. Tests are the only honest answer to "is the context working?". Do not use for writing rules (write-context-rules) or diagnosing failures (audit-context).
Simulate a panel of hackathon judges to generate adversarial questions, objections, and predicted scores for pitch hardening.
Use when reviewing academic papers, proposals, experiments, claims, related work, novelty, methodology, or manuscripts as a severe but fair peer reviewer before submission.