'evals' 검색 결과

Prompt engineering for production LLMs — structured outputs, evals, RAG, tool workflows, multimodal prompting, and safety. Use when designing, debugging, or shipping prompts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Creates or improves an Agent Skill by defining a precise trigger boundary, concise procedural instructions, progressive-disclosure references, deterministic helpers, routing evals, and validation evidence. Use when authoring or reviewing SKILL.md, skill metadata, bundled references, or skill packaging. Not for ordinary project documentation or application code.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Smithy the Toolsmith builds, converts, reviews, packages, validates and migrates BMad skills, agents and modules. Use when the user wants a skill, agent, persona, command or module made, changed, reviewed or packaged; a prompt or rule from another tool turned into a skill; a skill mined from session logs; an old module migrated; or asks for Smithy. Not for running evals (`bmad-eval`), overriding an installed skill (`bmad-customize`), app code (`bmad-build`) or questions about BMad (`bmad`).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Runs the evals of a BMad skill through the agent harness the user works in and reports what they show: baseline against the bare model, variant against a stripped or prior version, quality against a rubric, and trigger accuracy of the description with an optional optimization loop. Use when the user asks to evaluate, benchmark, test the triggers of, grade, or compare versions of a skill, or when the bmad-toolsmith skill asks for an eval of a skill it built.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

RAG & Knowledge Architect: Retrieval systems: ingestion, chunking, embeddings, hybrid search, reranking, evals.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

LLMOps Architect: Run LLM features in production: model routing, evals, observability, caching, cost, safety.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

AI Agent Architect: Design agent systems: single vs multi-agent, tools and MCP, memory, guardrails, evals.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design an LLM eval — task schema, scoring rubric, dataset composition, and pass/fail thresholds. Use when asked to "design an LLM eval", "write a scoring rubric", or "how do we measure this model".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build automated regression suites — golden sets, threshold alerting, CI integration for model changes. Use when asked to "catch model regressions in CI", "build a golden set", or "automate eval regression".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze LLM eval results — score breakdowns by category, regression detection vs baseline, failure clustering. Use when asked to "analyze our eval results", "did the model regress", or "cluster the eval failures".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design eval harnesses — task schemas, metrics, dataset versioning, eval-as-code patterns. Use when asked to "build an eval harness", "set up eval-as-code", or "version our eval datasets".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ML/AI engineer — LLM integrations, prompt engineering, model pipelines, evals, RAG. Use when asked to "add AI to this", "integrate an LLM", "build a RAG pipeline", "evaluate model accuracy", or "improve this prompt".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Patterns for continuous autonomous agent loops with quality gates, evals, and recovery controls.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design and implement evaluation frameworks for AI agents. Use when testing agent reasoning quality, building graders, doing error analysis, or establishing regression protection. Framework-agnostic concepts that apply to any SDK.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when you want to benchmark and improve an existing repo skill through repeated evals, prompt mutations, and score tracking.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Respan for LLM observability. Use to set up tracing or the gateway, manage prompts (create/version/deploy), run evals, experiments, and datasets, or work with traces and logs. Trigger for any Respan platform task or when instrumenting an app to send telemetry to Respan.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use Arcade MCP to create custom MCP servers and tools with OAuth-aware authorization, evals, and deployment paths for agent tool-calling workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Nature-style remote-sensing manuscript reviewer skill for evaluating Earth-observation products, retrievals, machine-learning maps, validation design, uncertainty propagation, spatial transferability and claim-scope calibration.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Intercept, inspect, and modify HTTP/HTTPS traffic via a Rust MITM proxy with Lua scripting hooks. Supports API debugging, LLM call capture for evals, endpoint mocking, CORS/auth header injection, latency simulation, and telemetry blocking. Triggers on 'proxy', 'intercept traffic', 'inspect requests', 'mock API', 'capture API calls', 'MITM', 'network debug', 'HTTP inspection'.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when analyzing an existing TypeScript or JavaScript codebase to decide where and how to introduce Inngest. Covers repository discovery, framework and package detection, finding durability gaps in HTTP handlers, webhooks, cron jobs, queues, long-running jobs, AI agents, Agent Evals, polling loops, eval loops, and side-effect-heavy code, then producing and implementing an incremental integration plan.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.