By yzavyas
Write rigorous evals for LLM agents, skills, MCP servers, and prompts. Use when: building test suites, measuring effectiveness, choosing frameworks. Covers: DeepEval, Braintrust, RAGAS, precision/recall, F1.
A marketplace of cognitive extensions for Claude Code.
📚 Documentation · 🔍 Catalog · 💡 Ethos
/plugin marketplace add yzavyas/claude-1337
/plugin install core-1337@claude-1337
Known issues: #14815, #14061, #15369
Workaround:
~/.claude/plugins/marketplaces/claude-1337/plugins/core-1337/scripts/install-workaround.sh
Development happens on the dev branch. This main branch is for marketplace distribution only.
git checkout dev
See CONTRIBUTING.md or the contributor guide.
MIT
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
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Sign in to claimnpx claudepluginhub yzavyas/claude-1337 --plugin eval-1337AI image and video generation. Use when: Midjourney prompting, choosing image/video models, troubleshooting AI art, reference types, style transfer, text-in-image.
Rust production patterns. Use when: building Rust systems. Covers ownership decisions, async gotchas, crate selection, domain knowledge (networking, embedded, WASM, FFI, proc-macros).
JVM static and runtime analysis. Use when: finding dead code, optimizing Java/Kotlin apps, profiling, debugging memory leaks. Covers SootUp, Scavenger, async-profiler, JFR, ProGuard.
Elite Kotlin development patterns. Use when: writing Kotlin for backend (Ktor, Spring Boot), Android, Multiplatform. Covers coroutines, structured concurrency, Flow, scope functions, null safety, Java interop, testing (Kotest, MockK), benchmarking (kotlinx-benchmark).
Architectural reasoning with The Guild. 13 specialized agents with orthogonal perspectives for multi-viewpoint architecture review.
Agent and skill evaluation harness with MLflow integration
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
This skill should be used when the user asks to "evaluate agent performance", "build test framework", "measure agent quality", "create evaluation rubrics", or mentions LLM-as-judge, multi-dimensional evaluation, agent testing, or quality gates for agent pipelines.
Benchmark, evaluate, and optimize skills to ensure reliable performance across all LLMs
Skills for building LLM evaluations: pipeline audit, error analysis, synthetic data generation, LLM-as-Judge design, evaluator validation, RAG evaluation, and annotation interfaces.
Set up evaluation of AI agents with tool call validation, correctness checks, task completion, and tool reliability using Dokimos. Framework-agnostic — works with any agent framework.