「evaluation」の検索結果

Use before evaluations or after roster churn to resolve live policy versions and restore the champion flag.

日本語の概要は準備中です。原文の説明を表示しています。

Use when monitoring Crewrift evaluations, rounds, or rankings across turns with a persistent poller.

日本語の概要は準備中です。原文の説明を表示しています。

Codified expertise for electricity and gas procurement, tariff optimization, demand charge management, renewable PPA evaluation, and multi-facility energy cost management. Informed by energy procurement managers with 15+ years experience at large commercial and industrial consumers. Includes market structure analysis, hedging strategies, load profiling, and sustainability reporting frameworks. Use when procuring energy, optimizing tariffs, managing demand charges, evaluating PPAs, or developing energy strategies.

日本語の概要は準備中です。原文の説明を表示しています。

Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles

日本語の概要は準備中です。原文の説明を表示しています。

Use when a task explicitly creates or improves an MCP server and needs a safe implementation and evaluation workflow

日本語の概要は準備中です。原文の説明を表示しています。

Auto-assembles a domain-specific expert panel (7–10 experts), scores any content or strategy artifact against a typed rubric, and iterates until the aggregate hits 90+ (max 3 rounds). Use as a quality gate on copy, email sequences, landing-page drafts, strategy docs, charts, titles, or recruiting evaluations — or when another skill needs a final review gate on its output. Triggers on "expert panel this", "score this", "rate these variants", "quality check this", "panel review", "expert score", "evaluate this copy/strategy/page". For variant generation and multi-round conversion optimization see autoresearch; for live-URL CRO auditing see conversion-ops; for the scripted content-production pipeline see content-pipeline.

日本語の概要は準備中です。原文の説明を表示しています。

Build high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services. Covers design principles, architecture, implementation patterns, and evaluation-driven development.

日本語の概要は準備中です。原文の説明を表示しています。

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

日本語の概要は準備中です。原文の説明を表示しています。

Scope, preregister, execute, audit, normalize, and publish GEODE evaluations with research-question, reproduction, attempt-lineage, trajectory, verifier-receipt, and immutable artifact contracts. Use for benchmark runs or comparisons, eval-artifact and cache-accounting audits, GPT or subscription result normalization, research question or hypothesis framing, retry diagnosis, trajectory publication, DPO or reward-data readiness reviews, and requests involving MCPMark, tau2, BFCL, Agent-World, HAL, Terminal-Bench, or Toolathlon.

日本語の概要は準備中です。原文の説明を表示しています。

Ground GEODE design and evaluation work in Stanford CS329A Parts 2 and 5. Use for test-time compute, planning, multi-step reasoning, deep research, LATS, independent subplans, best-of-N, repair depth, measurement replication, verifier authority, and GEODE/Eco²/SIL/Crucible comparisons.

日本語の概要は準備中です。原文の説明を表示しています。

Generates a structured attorney work product memorandum from an initial client consultation for commercial litigation. Extracts verifiable facts from uploaded documents, builds a chronological narrative, identifies parties and contracts, flags preliminary legal claims and defenses, and produces a prioritized action plan. Triggers at matter intake when a business client needs a foundational case evaluation document for a pre-filing commercial dispute.

日本語の概要は準備中です。原文の説明を表示しています。

Produces an attorney-ready memo from a corpus of legal documents supplied by the user. Use when a user shows up with a folder, zip, or vault of case documents and asks for a case summary, case evaluation, litigation package, intake memo, matter overview, or "can you summarize this case for me." The skill ingests the corpus into a searchable index, OCRs anything non-searchable, inventories and diagnoses the practice area, loads the appropriate practice-area playbook module(s) (PI/tort, commercial litigation, IP infringement, or user-authored extensions), iteratively searches the corpus across eight core dimensions plus any module-specific dimensions, defers specialized document clusters (depositions, medical records, discovery, liens) to dedicated sibling skills, and synthesizes a cited memo.

日本語の概要は準備中です。原文の説明を表示しています。

AI-powered bank compliance and regulatory review assistant - screen marketing materials, KYC documents, AML alerts, and NFRA/CBIRC compliance checks. Updated for 2025-2026 regulations including 商业银行资本管理办法 (Basel III China 2024), 消费者权益保护规定升级, FATF mutual evaluation 2025, and 反洗钱法修订. Keywords: bank compliance, KYC, AML, regulatory review, NFRA, Basel III, China banking, 合规审查, 合规审核, 监管合规, 反洗钱, KYC尽调, 客户身份识别, 受益人识别, 制裁筛查, 交易监控.

日本語の概要は準備中です。原文の説明を表示しています。

当AI智能体完成一段法律推理或论证后，需要对该推理结论进行自我评估，判断论证的整体强度与置信度，识别并标注推理链条中的薄弱环节。 触发条件包括但不限于： - 完成法律分析后需要给出结论可靠性评级 - 用户明确要求评估某一论证的说服力或可靠性 - 存在多个竞争性法律解释需要比较论证强度 - 推理过程中涉及不确定因素（事实不明、法律空白、争议观点）需要标注 - 为决策者提供风险提示，说明结论的局限性 - 对己方或对方的法律论证进行攻防强度评估 - 在法律意见书、备忘录等文书中需要附加置信度说明

日本語の概要は準備中です。原文の説明を表示しています。

Deep multi-source technical research with citations — tech evaluation, ecosystem/standards comparison. NOT for API docs (mk:docs-finder), codebase discovery (mk:scout), or project Q&A (mk:ask-me).

日本語の概要は準備中です。原文の説明を表示しています。

Loads, lists, composes, or validates graded evaluation rubrics: PASS/WARN/FAIL grading with weighted criteria and balanced anchor examples for evaluator-style review.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Evaluate Jira ticket complexity + inconsistencies via the jira-evaluator agent. Read-only. Triggers: 'evaluate KEY', 'analyze ticket complexity for KEY', 'check ticket quality for KEY'. NOT for estimation (mk:jira-estimator); NOT for full RCA (mk:jira-analyst).

日本語の概要は準備中です。原文の説明を表示しています。

Use when refining a draft user prompt before sending it to a coding agent. Decomposes goal/context/constraints/acceptance/output-format, detects ambiguity and model-coupled framing, then emits a model-agnostic rewrite. Supports --analyze, --score, and --deep. NOT for prompts from scratch (mk:brainstorming), plans/reviews (mk:elicit), implementation plans (mk:plan-creator), or general codebase scouting (mk:scout).

日本語の概要は準備中です。原文の説明を表示しています。

CEO/founder-mode plan review. Rethink the problem, find the 10-star product, challenge premises, expand scope when it creates a better product. Four modes: SCOPE EXPANSION (dream big), SELECTIVE EXPANSION (hold scope + cherry-pick expansions), HOLD SCOPE (maximum rigor), SCOPE REDUCTION (strip to essentials). Use when asked to "think bigger", "expand scope", "strategy review", "rethink this", or "is this ambitious enough". Proactively suggest when the user is questioning scope or ambition of a plan, or when the plan feels like it could be thinking bigger. Use AFTER a plan exists. For validating the idea itself before planning, use /mk:office-hours first.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Structured second-pass reasoning after code review or plan evaluation. Offers named reasoning methods (pre-mortem, inversion, red team, Socratic, etc.) to re-examine findings through a specific lens. Use after /mk:review verdict or any time deeper analysis is needed on an existing output. NOT for initial review (see mk:review); NOT for creating implementation plans (see mk:plan-creator).

日本語の概要は準備中です。原文の説明を表示しています。

Adversarial code review using attacker mindset — trust boundary, attack surface, business logic, and defense evaluation

日本語の概要は準備中です。原文の説明を表示しています。

Structured SE task evaluation using 15 benchmark definitions from claude-code-harness research

日本語の概要は準備中です。原文の説明を表示しています。