'evaluation' 검색 결과

Evaluate, select, and contract with vendors and SaaS tools. Use this skill when comparing alternatives, running an RFP, scoring vendors against criteria, negotiating contracts, planning a switch, or assessing a vendor's risk. Triggers on vendor evaluation, RFP, vendor selection, build vs buy, SaaS evaluation, vendor scorecard, vendor comparison, contract negotiation, vendor switch, procurement. Also triggers when a renewal is coming up or when a tool isn't meeting expectations.

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Design a source evaluation protocol using lateral reading and credibility checks for digital information. Use when students need to evaluate websites, online sources, or social media claims.

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Use when the user needs to design evaluation datasets, create test cases, stratify samples, generate adversarial examples, extract eval dimensions from traces/specs, or build a labeled evaluation set. Also use when the user mentions test data design, eval coverage, difficulty stratification, synthetic data generation for eval, or "how to create good evaluation data." Outputs datasets in OpenJudge-compatible format.

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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.

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Provides weighted scoring, rubrics, and decision-threshold patterns. Use when designing quality gates, evaluation systems, or decision frameworks.

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Autonomous AI agent benchmark evaluation register: task completion rates, planning accuracy, tool invocation precision, and cost benchmarks.

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Use when summarizing agent evaluations where autonomous, assisted, failed, timed-out, or invalid outcomes must remain distinct and comparable.

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Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when "langfuse, llm observability, llm tracing, prompt management, llm evaluation, monitor llm, debug llm, langfuse, observability, tracing, llm-monitoring, evaluation, prompt-management, debugging, analytics" mentioned.

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Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarksUse when "agent testing, agent evaluation, benchmark agents, agent reliability, test agent, testing, evaluation, benchmark, agents, reliability, quality" mentioned.

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Agent Evaluation workflow skill. Use this skill when the user needs Testing and benchmarking LLM agents including behavioral testing, and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Design a source evaluation protocol using lateral reading and credibility checks for digital information. Use when students need to evaluate websites, online sources, or social media claims.

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LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations

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Generate msmodelslim evaluation YAML configuration (service_oriented + aisbench + vllm-ascend). Use when user asks for evaluation config generation.

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Create evaluation frameworks for AI outputs with rubrics and scoring. TRIGGERS - Use when user needs help with ai-evaluation-framework related tasks.

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Author and run offline evals for Agentforce agents: fixture format, scoring rubrics, hallucination checks, regression baselines, CI integration, prompt-change safety. Trigger keywords: agent eval, hallucination, doesn't hallucinate, agentforce offline evals, rubric for agent response. NOT for deciding which layers of testing the agent needs — use agentforce/agentforce-testing-strategy. NOT for Testing Center, AiEvaluationDefinition or sf agent test runs — use agentforce/agent-testing-and-evaluation.

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Use when designing evaluation suites for agentic systems, choosing agent benchmarks, measuring multi-agent performance, or critiquing single-score evaluation of agents. Based on arXiv:2605.26112v1. Trigger phrases: "evaluate my agent", "benchmark agent", "measure agent reliability", "pass^k evaluation", "agent metrics beyond success rate", "trajectory quality", "memory hygiene metric", "longitudinal agent eval", "why my agent scores well on benchmarks but fails in production".

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Builds a DRAFT weighted supplier evaluation matrix from supplier responses and the panel's agreed criteria, weights and scale: a provisional score per cell with the quoted evidence and anchor behind it, a register of every missing or partial answer, and calibration and sensitivity flags. Provisional for panel moderation; never recommends, awards or disqualifies. Use when the user asks to score, weight or rank supplier, bid or tender responses against agreed criteria, for example "score these bids", "build the evaluation matrix", "weight the tender responses" or "rank the suppliers against our criteria". Do not use for a side-by-side comparison without scores, use rfp-comparison-pack instead; for writing the criteria, use rfp-requirements-pack instead. Drafts for human review; never approves, authorises or signs off.

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Turns a business need and its constraints into a DRAFT RFP requirements pack: traced functional and non-functional requirements with priorities, mandatory gates, evaluation criteria with weights left to the business owner, and fact-seeking supplier questions. Solution-neutral; never names a supplier or product. Use when the user asks to "write the RFP requirements", "turn this business case into tender requirements", "draft the supplier questionnaire", "structure our RFQ from this brief" or "set up evaluation criteria before we go to market". Do not use for responses already received, use rfp-comparison-pack to compare them or supplier-evaluation-matrix to score them instead. Drafts for human review; never approves, authorises or signs off.

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Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing.

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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.

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DDD評価エージェント - 戦略的設計・戦術的設計の両面からDDD原則への適合度を評価し改善点を特定。/ddd-evaluation [対象パス] で呼び出し。

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評価統合エージェント - MMI評価とDDD評価の結果を統合し、優先度付き改善計画を策定。/integrate-evaluations [対象パス] で呼び出し。

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Generate HTML reports and structured metrics from model evaluation results. Creates publication-ready reports with embedded outputs (images, audio, charts) and metrics.json for cross-model comparison. Use when generating reports, writing metrics, creating evaluation summaries, or formatting benchmark results. Triggers on "generate report", "write metrics", "create report", "evaluation summary", "benchmark results", "format results".

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Focused Signals scout for PostHog projects using feature flags. Watches the flag roster and the `$feature_flag_called` evaluation stream for contradictions between a flag's configured state and its real traffic: evaluation cliffs on healthy flags, ghost flags (code calling keys that no longer exist), response-distribution shifts with no corresponding flag edit, and flag debt (stale, fully-rolled-out, or dead flags still burning evaluations). Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.

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