「uat」の検索結果

Evaluate an incoming workflow upgrade — a skill, MCP server, subagent, library, external service, prompt, or pattern — through one disciplined pipeline before adopting it. Security is a blocking gate. Records every verdict so the same thing is never evaluated twice. Use when considering a new tool from a post, repo, or recommendation. Triggers — "vet this", "should we adopt", "is this safe to install", "evaluate this skill/MCP", "what do you think of this tool".

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Evaluate Jira ticket complexity and inconsistencies via jira-evaluator agent, read-only. NOT estimation (mk:jira-estimator) or full RCA (mk:jira-analyst).

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Experimental behavioral evaluator. Drives a running artifact via browser/curl/CLI and records rubric evidence; the runner is not yet a fully implemented automated evaluation system.

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Use when loading, listing, composing, or validating graded evaluation rubrics — PASS/WARN/FAIL grading with weighted criteria and balanced PASS/FAIL anchor examples. Triggers on /mk:rubric, "load rubric", "compose rubric preset", "validate rubric", or evaluator-style code review that needs anchor-grounded judgments.

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Use when a decision needs deep multi-source technical research with cited evidence — technology evaluation, ecosystem comparison, standards/spec fact-finding, "how do others solve X". Triggers on "research X", "deep dive on X", "evaluate X vs Y", "find best practices for X". NOT for library/API doc lookup (see mk:docs-finder); NOT for one-shot URL fetch (see mk:web-to-markdown); NOT for internal codebase discovery (see mk:scout); NOT for project-only Q&A (see mk:ask-me); NOT for comparing solution designs (see mk:brainstorming); NOT for root-cause debugging (see mk:investigate); NOT for wiki ingestion of sources (see mk:wiki-research).

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Expert in Jungian analytical psychology, depth psychology, shadow work, archetypal analysis, dream interpretation, active imagination, addiction/recovery through Jungian lens, and the individuation process - grounded in primary sources and clinical frameworks. Activate on 'Jung', 'Jungian', 'shadow work', 'archetypes', 'dream interpretation', 'active imagination', 'individuation', 'anima', 'animus', 'collective unconscious', 'addiction', 'recovery', 'spiritus contra spiritum'. NOT for therapy or diagnosis (only licensed analysts diagnose), active psychosis, severe dissociation, or replacing the relational container of actual Jungian analysis.

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This skill recommends re-evaluating the need for treatment of other pituitary hormone deficiencies in the postoperative period following surgical intervention for Cushing's syndrome. It is triggered when assessing a post-op CS patient for ongoing pituitary function, evaluating for hypopituitarism, or checking HPA axis recovery with morning cortisol/ACTH stimulation tests and pituitary MRI within 1–3 months.

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Expert-level fluid physics knowledge. Use when working with fluid statics, fluid dynamics, Bernoulli equation, viscosity, turbulence, Navier-Stokes equations, boundary layers, vorticity, compressible flow, or surface tension. Also use when the user mentions 'Bernoulli', 'Navier-Stokes', 'Reynolds number', 'turbulence', 'viscosity', 'boundary layer', 'vorticity', 'continuity equation', 'laminar flow', 'pressure', 'buoyancy', or 'surface tension'.

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Avalia LLMs em mais de 60 benchmarks acadêmicos (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use quando benchmarking de qualidade de modelo, comparação de modelos, relatório de resultados acadêmicos ou rastreamento de progresso de treinamento. Padrão da indústria usado por EleutherAI, HuggingFace e laboratórios principais. Suporta HuggingFace, vLLM, APIs.

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Turn a proven POC into a real, crouton-native, documented package(s) + a consuming app — the deliberate "step back and rebuild" checkpoint. SPEC-DRIVEN — the POC discovers a behaviour spec (captured at each sign-off), graduation freezes it as authoritative, the app is rebuilt the crouton way to satisfy it, and nothing is done until it's checked + signed off against two gates (experience + crouton-conformance). Use when a `pocs/*` app is proven and "done", or asked to "graduate this poc", "make it a real app", "extract the package".

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Detect typosquatting, homograph phishing, and brand impersonation domains using dnstwist to generate domain permutations and identify registered lookalike domains targeting your organization.

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Professional financial analysis skill for investment decisions, budgeting, financial statement analysis, and business plan evaluation. Use this skill when you need to analyze financial ratios (profitability, safety, efficiency), calculate DCF/NPV/IRR valuations, perform budget variance analysis, or conduct sensitivity/scenario analysis. Triggers include "analyze financial statements", "calculate DCF", "investment evaluation", "budget variance", "財務分析", "投資評価", "予算差異分析".

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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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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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Designs a pre-launch evaluation plan for a declarative agent from its instructions text, skills, capabilities, knowledge sources and sample requests: a behaviour contract of testable statements quoted from the text, test prompts by scenario family and tier with setup, expected behaviour, pass and fail criteria and red flags, a red-flag catalogue, a blank scoring sheet, a coverage table and a go or no-go checklist the owner completes and decides. Use when the user asks to "write test cases for my agent", "how do I evaluate this agent before launch", "build a test plan for the agent", "what should I check before publishing this agent" or "give me a go no-go checklist". Do not use for finding injection or leakage exposures in the instructions, use agent-instructions-red-team instead; do not use for reviewing one SKILL.md, use skill-file-reviewer instead. Drafts for human review; never approves, authorises or signs off.

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MMI評価エージェント - Modularity Maturity Indexによるモジュール成熟度評価（Cohesion/Coupling/Independence/Reusability）。/evaluate-mmi [対象パス] で呼び出し。

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise compari

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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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Scans HOT.md and proposes which rules should graduate to SOUL.md (untouched 30+ days) or be removed entirely. Run weekly via cron, or invoke ad-hoc when HOT.md feels stale. Triggers: "clean up HOT", "graduate rules", "is HOT.md stale", "what HOT rules can I drop".

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Expert en évaluation d'agents IA (benchmarking, human eval, latence, coût, précision, safety testing)

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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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Improve AI application with evaluation-driven development. Define eval criteria, instrument the application, build golden datasets, observe and evaluate application runs, analyze results, and produce a concrete action plan for improvements. ALWAYS USE THIS SKILL when the user asks to set up QA, add tests, add evals, evaluate, benchmark, fix wrong behaviors, improve quality, or do quality assurance for any Python project that calls an LLM model.

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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。

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。