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提案依頼への回答漏れを点検し、競合製品との機能比較や実証実験の計画・評価を支援します。顧客要件の整理からデモ準備、技術提案書の作成まで進めるスキルです。

• 提案回答の要件漏れを確認したいとき
• 競合製品との機能比較
• 顧客の優先事項に沿うデモ準備

Claude Managed Agentの成果を明文化した評価基準で採点し、回数を区切って改善します。合格後は改善に使わなかった事例でも品質を確認します。

• 成果の合格基準を明文化したいとき
• 回数を決めてエージェントを改善
• 未使用の事例で品質を確認したいとき

Claude Managed Agentの目的を整理し、構築・起動準備・評価改善・定期実行の各工程へ振り分け、実行計画と起動用コマンドを用意するスキル。

• エージェントに任せる仕事の整理
• Anthropicアカウントでの起動準備
• 回数制限付きの評価・改善計画

既存・新規の製品と市場を組み合わせて成長策を4つに整理し、売上の可能性、必要な投資、競争、リスクを比較して、優先候補の市場投入計画と段階的な実行計画をまとめます。

• 既存事業の成長策を比較したいとき
• 新地域・新顧客層への展開計画
• 既存顧客向けの新製品の検討

編集したOpenSEOスキルを独立したCodexセッションで実行し、保存されたレポートとツールの実行記録を比較・採点して、出力品質の改善を検証します。

• スキル修正前後のレポート比較
• 異なるサイトで指示の妥当性確認
• SEO監査レポートの基準別採点

既存のGoogle Cloud環境を読み取り専用で調べ、データの保存地域やサービス利用、暗号化設定を基にAssured Workloadsの適合候補と準備事項を示すスキル。

• 既存フォルダやプロジェクトの準備状況確認
• EU・米国のデータ境界の枠組み比較
• 保存地域・サービス・暗号化の準備状況確認

Google SecOpsの脅威検知ルールを作成・検証し、過去のログや模擬イベントで検知の抜けを調べ、承認後のルール配備まで支援するスキル。

• 既知の攻撃を検知ルールにしたいとき
• 既存ルールの誤検知を減らしたいとき
• 脅威情報やCVEに対する検知の抜けを調べたいとき

Google Cloudの利用料金とリソース構成を点検し、未使用資源の整理、適切なサイズへの調整、割引の比較、継続的な費用管理の方針をまとめるスキル。

• チーム・プロジェクト別の費用確認
• 未使用資源や過剰な構成を探したいとき
• CUDs・SUDs・Spot VMsの比較

Use this skill to manage Google Cloud Workload Manager evaluations, rules, scanned resources, and validation results by using public client libraries and the REST API. Use when you need to inspect workload best-practice rules, create and run evaluations for Google Cloud general best practices, SAP, SQL Server, or custom organizational rules, review violations, export results to BigQuery, or automate Workload Manager through client libraries because no service-specific public CLI or MCP server is available. Don't use for general Google Compute Engine instance management, VPC configuration, or standard IAM auditing.

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。

Provides workflows to optimize BigQuery environments (capacity planning, editions), storage assets (partitioning, clustering, storage lifecycles, billing models), and SQL queries. Use when optimizing cost, modeling Edition migrations, rightsizing reservations, evaluating logical vs. physical storage, designing table partitioning/clustering, generating table DDL, migrating unpartitioned tables, managing partition expiration, optimizing individual SQL queries, or evaluating acceleration structures (search indexes, materialized views, BI Engine). Do not use for raw usage reporting (use bigquery-observability), query execution plan analysis, error troubleshooting, or diagnosing why a specific job was slow (use bigquery-troubleshooting).

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。

Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when generating synthetic user scenarios, evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.

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。

DeepEval evaluation workflow for AI agents and LLM applications. TRIGGER when the user wants to evaluate or improve an AI agent, tool-using workflow, multi-turn chatbot, RAG pipeline, or LLM app; add evals; generate datasets or goldens; use deepeval generate; use deepeval test run; send results to Confident AI; monitor production; run online evals; inspect traces; or iterate on prompts, tools, retrieval, or agent behavior from eval failures. AI agents are the primary use case. Covers Python SDK, pytest eval suites, CLI generation, traced evals, Confident AI reporting, and agent-driven improvement loops. DO NOT TRIGGER for unrelated generic pytest, non-AI test setup, or non-DeepEval observability work unless the user asks to compare or migrate to DeepEval; for instrumenting an app with DeepEval tracing, @observe, or framework integrations (use the `deepeval-tracing` skill); or for raw OpenTelemetry / OTLP export without the deepeval package (use the `deepeval-otel` skill).

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。

This skill should be used when the harness, scaffold, workflow, or optimizer itself is the optimization target: recursive self-improvement (RSI) loops, meta-harnesses, self-improving harnesses that mine their own failures and propose bounded edits, evolutionary or population-based search over agent scaffolds, acceptance gates for self-modifying systems, and agentic context evolution where the mechanism that produces context is versioned and evolved. Route governance of a single autonomous loop (locked surfaces, durable logs, rollback, novelty gates, approval boundaries) to harness-engineering, measurement and quality-gate design to evaluation, judge design to advanced-evaluation, and remote sandbox infrastructure to hosted-agents.

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。

This skill should be used when building agent evaluation systems: deterministic checks, regression suites, multi-dimensional rubrics, quality gates, production monitoring, baseline comparison, and outcome measurement for agent pipelines.

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。

Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.

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。

Design custom metric packs for plugin-eval so teams can add local evaluation rubrics that emit schema-compatible checks and metrics. Use when the user wants their own evaluation criteria or visualizations.

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。

Help engineers evaluate a local skill or plugin, explain why it scored that way, show what to fix first, measure real token usage, benchmark starter scenarios, or decide what to run next. Use when the user says things like "evaluate this skill", "give me an analysis of the game dev skill", "why did this score that way", "what should I fix first", "measure the real token usage of this skill", or "what should I run next?".

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。

Route Public Equity Investing workflows for listed-company and public-security research, earnings analysis, valuation, model updates, long/short pitches, catalysts, thesis tracking, ETF/index and constituent diligence, position sizing, hedging, dashboards, and investment memos. Use when the user is evaluating a public company, ticker, sector, portfolio position, event, earnings setup, or investment thesis through an investor lens, including what is priced in, what is mispriced, what proves or kills the thesis, and what to watch next. Do not use for private-company diligence, credit-first analysis, personal financial advice, generic company summaries, or generic document drafting with no public-equity investment context.

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 a repo agent harness (AGENTS.md, rules, skills, skill refs) for broken paths/commands, redundant instructions, and usefulness using a stack-agnostic dual-judge protocol with planted traps. HIGH PRIORITY questionnaires at top: Q1 optional docs, Q2 B/C budget before Track A (certainty/tokens). A always runs after Q2; B/C opt-in. ADRs/RFCs excluded from T2. Mixed apply uses 11-mixed-apply.md (KEEP/CUT). Use when the user says harness eval, harness-eval, harness debug, audit AGENTS.md, audit skills/rules, instruction audit, redundancy of agent instructions, usefulness of skills, Ship/Review/Hold/Slim/Keep-core for harness, or wants Track A/B/C harness evaluation. Do NOT use for harness setup or init, feature spec-driven work (tlc-spec-driven), or applying Ship/Slim trims unless the user explicitly asks after the report.

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。

Darwin Skill 2.0 (达尔文.skill 2.0): autonomous skill optimizer, v2.0 integrates Microsoft Research SkillLens (arXiv 2605.23899) 9-dim rubric + SkillOpt (arXiv 2605.23904) validation-gated design + human-in-the-loop checkpoints. Evaluates SKILL.md files using a 9-dimension rubric (structure + effectiveness + meta-skill blacklists), runs hill-climbing with git version control, spawns independent judge agents for blind evaluation, validates improvements through test prompts with auto-break on diminishing returns, and generates visual result cards. Use when user mentions "优化skill", "skill评分", "自动优化", "auto optimize", "skill质量检查", "达尔文", "darwin", "帮我改改skill", "skill怎么样", "提升skill质量", "skill review", "skill打分".

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 the user wants proof that finished work meets its acceptance criteria, not just that tests pass: 'verify it is actually done', 'check it against the spec', 'evaluate the run'. Runs mechanical checks, a semantic review per criterion, and multi-model consensus when uncertain. With execution.auto_evolve on (the default), a rejected result with a Seed continues into a Ralph fix loop; pass auto_evolve: false to verify only.

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 design from a UX perspective, assessing visual hierarchy, information architecture, emotional resonance, cognitive load, and overall quality with quantitative scoring, persona-based testing, and actionable feedback. Use when the user asks to review, critique, evaluate, or give feedback on a design or component.

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。

This skill should be used when the user asks to "analyze skill quality", "evaluate this skill", "review skill quality", "check my skill", or "generate quality report". Evaluates local skills across description quality, content organization, writing style, and structural integrity.

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。

The way to answer "how are my production AI agents doing?" and to run the team's agent-observability deployment — reach for it even on casual phrasing that names no tool. Trigger when the user wants to: • inspect agent telemetry — did agents error/fail/go flaky; sessions, events, latency, token usage, slowest models; eval/quality scores and whether quality dropped; • operate the deployment — ack/assign/resolve/mute/dismiss issues (alerts, reports, and audit findings) with notes; run and triage audits; see who has access and change roles (e.g. read-only); create or scope API keys (e.g. a push-only CI key); change settings; run saved or ad-hoc ClickHouse queries. Served by the `fp` CLI against FailproofAI Cloud. NOT for writing or designing an evaluator service / scoring logic (that's `agenteye-evaluator`), adding SDK/instrumentation to your app (that's `failproofai-sdk`, imported as `failproofai_sdk`), debugging the collector/daemon, or unrelated dev work (why a build/CI run failed, rotating non-FailproofAI Cloud secrets).

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。