「eval」の検索結果

Provides verified corrections for IAM behaviors that AI agents frequently get wrong — policy evaluation edge cases, trust policy gotchas, STS session limits, Organizations quirks, and SAML/MFA specifics. Also provides structured workflows for IAM role management and baseline policy generation from application source code or a Terraform plan JSON. Covers condition operator safety (ForAnyValue/ForAllValues with Null checks), bucket policy deny patterns (VPC endpoint restrictions, org paths), confused deputy protection, and service role creation for AWS services (Glue, CloudTrail, Lambda, ECS, etc.) with aws:SourceAccount/aws:SourceArn trust conditions. Applies when creating IAM roles, writing IAM or bucket policies, generating policies from application source code or a Terraform plan JSON, working with STS, Organizations, or condition operators, or any task needing a service or execution role. Does not cover non-IAM authorization like Cognito user-pool policies or app-level RBAC.

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 AWS costs, find savings, manage budgets, evaluate Savings Plans and Reserved Instances, right-size EC2/Lambda/RDS/EBS with Compute Optimizer, look up service pricing, query CUR with Athena, detect cost anomalies, scope costs to billing views, and monitor Free Tier usage. Triggers on: AWS bill, cost analysis, reduce spend, savings plan, reserved instance, right-size, budget alert, cost optimization, pricing, free tier, cost anomaly, CUR, cost audit, billing view, billing view ARN.

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。

Submit feedback on an Expo skill—or Expo itself—and control bundled anonymous usage telemetry (off by default / opt-in). Submit feedback with: npx --yes submit-expo-feedback@latest "ACTIONABLE_FEEDBACK". Optionally add either or both: --category "CATEGORY" and --subject "SUBJECT". Replace the uppercase placeholders before running. Use when a skill was useful, confusing, broken, missing context, or worth improving; when Expo, Expo CLI, EAS CLI, docs, or MCP worked well or fell short; when an AI agent repeatedly failed, got stuck, or needed the user to take over an Expo task (report it as an eval candidate); or when the user explicitly asks to enable or disable telemetry (tracking), check its status, or understand what it collects.

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 and process AssemblyAI pre-recorded and streaming webhooks with authentication, fast acknowledgment, deduplication, and safe retrieval. Use when implementing event-driven completion. Trigger with "AssemblyAI webhook" or "AssemblyAI callback".

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。

Run your first Apify Actor and retrieve results via apify-client. Use when starting a new Apify integration, testing connectivity, or learning the Actor call/dataset retrieval pattern. Trigger with "apify hello world", "apify example", "run an apify actor", "apify quick start", "first apify scrape".

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 and optimize system throughput including request handling, data processing, and resource utilization. Use when identifying capacity limits or evaluating scaling strategies. Trigger with phrases like "analyze throughput", "optimize capacity", or "identify bottlenecks".

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 HTTP security headers of web domains to identify vulnerabilities and misconfigurations. Use when you need to audit website security headers, assess header compliance, or get security recommendations for web applications. Trigger with phrases like "analyze security headers", "check HTTP headers", "audit website security headers", or "evaluate CSP and HSTS configuration".

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 or review an Algolia search architecture across source data, indexing, query, UI, events, and operations. Use when establishing system boundaries or evaluating an existing integration. Trigger with "Algolia architecture", "design search platform", or "review Algolia integration".

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。

Scaffold production-ready AI agents on Google's Agent Development Kit (ADK): ReAct-style single agents, multi-agent orchestration (Sequential/Parallel/Loop), tool wiring, evaluation, and optional Vertex AI Agent Engine deployment. Use when building, scaffolding, or deploying ADK agents on Google Cloud, or when wiring ADK tools and orchestration patterns. Trigger with "build an ADK agent", "scaffold an agent on ADK", or "deploy to Agent Engine".

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 Abridge adoption, workflow value, and avoidable operational friction without inventing prices or vendor billing meters. Use when reviewing an Abridge rollout or renewal. Trigger with "analyze Abridge value".

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。

Rigorous, defensible email-spoofability verdict and SPF supply-chain risk analysis computed from published DNS alone. Deepens the record-level SPF/DMARC/DKIM/BIMI/MTA-STS/DNSSEC fetch recipes in the offensive-osint arsenal (§16.14) with the reasoning that section doesn't do: a priority-ordered composite verdict for whether an attacker can actually land header-From-spoofed mail in an inbox, and by which vector (exact-domain vs subdomain) — grounded in the single most-misunderstood distinction in email security: the envelope MAIL FROM that SPF authenticates vs the visible header From: that only DMARC governs. Explains precisely why SPF -all/~all alone is NOT spoof-proof without DMARC enforcement, and why SPF +all bypasses DMARC even under p=reject pct=100. Covers RFC 7208 §4.6.4's 10-DNS-lookup / 2-void-lookup PermError fail-open condition with a runnable stdlib-only lookup-counter script, plus the SPF-include-takeover supply-chain vector (an attacker re-registering a dead include inherits SPF-pass authority over the victim domain) with strict transient-vs-NXDOMAIN discrimination discipline so a temporary SERVFAIL is never mistaken for a takeover lead. Fully passive: DNS TXT reads only, no mail sent, no RCPT TO probe, no API keys. Use when auditing a domain's real spoofing resistance (not just its published records), explaining to a client why 'we have SPF -all' does not mean they're covered, investigating an SPF PermError or an unusually long include chain, evaluating a dead SPF include as a takeover lead, or writing a defensible spoofability finding for a deliverable.

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 OpenEnv environments from a concrete use case (for example, "generate an env for the library textarena"). Use when asked to design or implement a new environment under envs/ by researching a target library/API, selecting matching OpenEnv examples, asking key implementation questions, and building models/client/server/openenv.yaml. Do not use for model training or evaluation tasks.

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 Agent Skill design quality against official specifications and best practices. Use when reviewing, auditing, or improving SKILL.md files and skill packages. Provides multi-dimensional scoring and actionable improvement suggestions.

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。

Apply before you trust, report, or act on a number you measured: a speedup, a regression, a throughput, a latency, or an eval result. Find what limits it, and rule out that it measured something other than the work you think.

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。

Apply when refactoring, evaluating diff size, or tempted to add abstractions, layers, or signal threading. Bias toward deletion and the smallest change that solves the problem.

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。

Prepare and validate research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Use for aggregate or synthetic research documentation and traceability—not patient care or live clinical operation.

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 scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-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。

Provide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential decisions.

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。

SSTI playbook. Use when template expressions, server-side rendering, preview features, or templating engines may evaluate attacker-controlled content.

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。

Expression Language injection playbook. Use when Java EL, SpEL, OGNL, or MVEL expressions may evaluate attacker-controlled input in Spring, Struts2, Confluence, or similar frameworks.

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。

Select, pair, and implement typefaces for web projects. Use when the user mentions "font pairing", "which typeface", "line height", "responsive typography", "web font loading", "type hierarchy", "variable fonts", "FOUT/FOIT", "typographic scale", or "the text is hard to read". Also trigger when choosing between system fonts and web fonts, optimizing font-loading performance, or designing readable long-form content. Covers readability evaluation, CSS implementation, and performance optimization. For overall UI design systems, see refactoring-ui. For dramatic typographic experiences, see top-design.

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。

Manage software complexity through deep modules, information hiding, and strategic programming. Use when the user mentions "module design", "API too complex", "shallow class", "complexity budget", "strategic vs tactical", "deep module", "information leakage", "pass-through method", "this code is over-engineered", or "simplify this design". Also trigger when reviewing an interface for simplicity, evaluating whether an abstraction is pulling its weight, deciding whether a comment is worth writing, or choosing between general-purpose and special-purpose approaches. Covers deep vs shallow modules, red flags for complexity, and comments as design documentation. For code quality, see clean-code. For architecture boundaries, see clean-architecture.

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。

Apply foundational design principles: affordances, signifiers, constraints, feedback, and conceptual models. Use when the user mentions "why is this confusing", "affordance", "error prevention", "discoverability", "human-centered design", "mental model", "mapping", "seven stages of action", "users keep making mistakes", "this is unintuitive", or "people cant figure out how to use it". Also trigger when reducing product complexity or feature creep. Covers the gulfs of execution and evaluation. For usability scoring, see ux-heuristics. For iOS-specific patterns, see ios-hig-design.

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 habit-forming product loops using the Hook Model (Trigger, Action, Variable Reward, Investment). Use when the user mentions "users arent coming back", "habit formation", "engagement loops", "habit zone", or "the manipulation matrix". Also trigger when designing notification or re-engagement strategies, building streaks or progress systems, or analyzing why users stop after signup. Covers ethics evaluation and onboarding for habits. For friction reduction and B=MAP, see improve-retention. For viral sharing, see contagious.

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。