「benchmark」の検索結果

Top-level orchestrator for the Gaia Skill Tree evidence verification pipeline. Run this when you want to do a full evidence pass — collecting raw sources, verifying live GitHub star counts, running Phase 2B benchmark-source verification, adversarially auditing for quality/formatting issues, and checking that every URL is reachable — all in one coordinated sequence. Trigger phrases: "run the evidence pipeline", "full evidence pass", "verify evidence", "evidence verification pipeline", "run ev-pipeline", "check all evidence", "audit the data lake", "evidence quality sweep", "refresh evidence", "validate evidence sources". Also aliased as /evidence-verification-pipeline. Use the individual sub-skills (ev-collection, ev-star-verification, ev-adversarial-audit, ev-link-validation) only when you need to re-run one phase in isolation; for end-to-end work, always start here.

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。

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update 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.

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。

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.

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。

Kubernetes and container security skill for cluster hardening, RBAC review, admission control (OPA Gatekeeper, Kyverno), Pod Security Standards, network policies, secrets management, runtime defense (Falco, Tetragon), image supply chain (cosign, SLSA, in-toto), and CIS benchmark compliance. Use to assess and harden clusters you operate.

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 needs the judgment of an Elementary School Teacher — interpreting a running record or fluency check against grade-level benchmarks, deciding whether a struggling reader needs a Tier 2 intervention, running a self-contained classroom's daily schedule across multiple subjects, implementing (not writing) an IEP/504 accommodation with fidelity, or writing a parent-conference summary grounded in assessment data.

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 needs the judgment of an Environmental Science Professor — deciding whether a field exercise crosses a weather/safety cancel threshold, building an NSF/EPA proposal budget, normalizing an interdisciplinary tenure case against the right benchmark, checking whether a mixed-methods field project needs IRB review, or handling a politically charged classroom topic without losing academic-freedom footing.

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 needs the judgment of a Clinical Nurse Specialist — diagnosing why a unit-level outcome metric (CLABSI, falls, pressure injury) is off benchmark, designing and scaling a practice-change pilot, appraising whether a new clinical guideline actually fits the local population, writing a quarterly outcomes report to nursing leadership, or coaching staff nurses through a new protocol without formal authority over them. US hospital/health-system default; state nurse practice acts vary. A reasoning aid, not clinical or legal advice.

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 needs the judgment of a Compensation and Benefits Manager — designing or evaluating a pay structure/leveling system, benchmarking compensation against market, deciding on a benefits package tradeoff, or auditing for pay equity issues. Narrower and more specialized than the broader hr-people-manager role's compensation-adjacent heuristics.

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 needs the judgment of an Adult ESL/Basic Education Instructor — placing a new adult learner using standardized assessment scores, designing life-context-relevant lesson content for working adults, tracking NRS-reportable level gains, addressing attendance/retention barriers, or preparing a learner for GED/HSE or workplace English benchmarks.

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。

Implements concurrent Go patterns using goroutines and channels, designs and builds microservices with gRPC or REST, optimizes Go application performance with pprof, and enforces idiomatic Go with generics, interfaces, and robust error handling. Use when building Go applications requiring concurrent programming, microservices architecture, or high-performance systems. Invoke for goroutines, channels, Go generics, gRPC integration, CLI tools, benchmarks, or table-driven testing.

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。

Dose-response modeling — EC50 estimation, curve fitting, synergy analysis, and benchmark-dose methods.

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 AI agents rigorously — task benchmarks, success criteria, failure taxonomy, cost/latency tracking, and regression testing. Use when you need to know whether an agent actually works, not whether it demos well.

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。

Read and manage the user's QuickBooks business through Intuit's official MCP server, including reports, invoices, customers, products, payment links, sales settings, and industry benchmarks.

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。

Generate a production-grade Product Detail Page (PDP) optimized for the 2026 conversion benchmarks — 4-8% target vs 1.5-3% average. Triggers: "product page", "PDP", "product detail page", "Shopify product page", "ecommerce conversion".

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。

Profile and audit mobile app performance -- cold/warm/hot startup time, memory leaks, battery drain, network efficiency, frame rate jank, and binary size. Triggers: diagnosing slow launches, investigating ANRs, reducing app size, or benchmarking rendering performance.

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。

Analyze an HR operations system for headcount planning effectiveness, attrition pattern detection, compensation benchmarking accuracy, workforce analytics maturity, and onboarding process optimization..

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。

Analyze donor retention and CRM systems — lapse risk scoring and prediction models, stewardship workflow automation, donor lifetime value (LTV) modeling, communication personalization engines, giving pattern analysis, and retention metric benchmarking.

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。

Rate any product, feature, or experience on the 11-star scale (Brian Chesky's Airbnb thought experiment). Use when user says "rate this experience", "11-star", "star rating", "experience audit", "how good is this", "experience rating", "product audit", "quality assessment", or wants to evaluate product quality and identify improvement paths. Also trigger when user wants to benchmark a feature, assess where a product stands, or map out what "great" looks like - even if they don't explicitly say "11-star".

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Refresh Claude Ads platform, API, policy, regulation, benchmark, issue, pull-request, fork, and repository evidence. Use for ads research refresh, expired refresh_due dates, stale API or platform claims, reverify-or-demote decisions, release-current claim validation, ecosystem review, current platform changes, or updating paid-media knowledge. When tools or sources are unavailable, still demote the stale claim for the current run and block dependent release-current assertions before requesting access.

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。

Professional finance research toolkit — backtesting (11 engines + benchmark comparison panel), factor analysis, Alpha Zoo (475 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 90 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 29 market-data sources (tushare, yfinance, okx, binance, akshare, baostock, tencent, mootdx, ccxt, futu, mt5, tickerall, local, eastmoney, sina, stooq, yahoo, pykrx, india_broker, qveris, longbridge, nobitex, wallex, plus optional-key finnhub/alphavantage/tiingo/fmp/gildata).

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。

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.

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。

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.

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。

Canonical go-live checklist for Agentforce deployments with rehearsed rollback and stakeholder sign-off records. NOT for the technical pre-prod verification behind those sign-off rows — cost telemetry, rate limits, canary rollout, latency benchmarks — use agentforce/agentforce-production-readiness-checklist. NOT for general Salesforce release management — use devops/release-management.

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。

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.

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。