「sample」の検索結果

Audits a methods section against a 45-item reporting checklist synthesized from the APSA Experimental Section rubric, JARS-Quant, CONSORT, and DA-RT, covering pre-registration, recruitment, randomization and treatments, sample flow and attrition, sample-size justification, three-tier results labeling, conjoint reporting, validity, and open-science infrastructure. Use when checking whether a methods section reports enough, preparing a submission or replication archive, asking what CONSORT, JARS, or DA-RT require, or documenting a deviation from a pre-analysis plan. Writing that plan before data collection goes to pre-registration-writing.

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。

Mock skill shipped by the sample bundle for installer 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。

Design and build runnable iOS UI mockups in SwiftUI, including UI direction, multiple visual or interaction variants, realistic sample data, state switching, Xcode Previews, and Simulator validation. Use when the user wants to explore what an iOS screen should look like, compare SwiftUI UI ideas, create a clickable native prototype, validate an app flow before production implementation, or asks for a SwiftUI mock, iOS mockup, native prototype, 画面モック, UI案, SwiftUIで動くモック, or 実際に触れるiOSモック.

Use when the user asks for a YARA rule, "write YARA for this sample/family", or `/hash-investigation` / `/malware-analysis` surfaces a sample worth a static-content rule. Pattern-matching rules for identifying malicious files.

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 characterising a malware sample, the user asks "what does this binary do?" / "analyse this hash deeply", or `/hash-investigation` flags a novel sample warranting deeper review. Static + dynamic methodology, behavioural indicators, sandbox interpretation.

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。

Support SOX 404 compliance with control testing methodology, sample selection, and documentation standards. Use when generating testing workpapers, selecting audit samples, classifying control deficiencies, or preparing for internal or external audits.

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。

"Calculate Wilson Score confidence intervals for ranking items by positive proportion with sample size correction. Use this skill when the user needs to rank products by ratings, sort content by approval rate, or build a 'best rated' list that accounts for sample size — even if they say 'rank by star rating', 'best rated with few reviews', or 'confidence-adjusted rating'.".

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。

Automates batch reverse engineering by generating Ghidra analyzeHeadless commands and post-analysis script skeletons to import, auto-analyze, and export results for many samples without the GUI. Activates for requests to script Ghidra headless analysis, batch-analyze samples with Ghidra, or generate an analyzeHeadless command and post-script.

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。

Summarizes runtime behavior of a sample from Procmon-style operation logs — process and thread creation, file and registry writes, and network-related operations — to build a behavioral profile. Activates for requests to analyze Procmon output, summarize sample behavior, or profile process/file/registry activity from a sandbox run.

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。

Runs a sample in an instrumented sandbox to observe behavior: process tree, file and registry changes, network activity, and persistence, then summarizes the behavioral report into capabilities and IOCs. Activates for requests to detonate a sample, do dynamic or behavioral analysis, or interpret sandbox output.

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。

Extracts an embedded malware configuration (C2 hosts, ports, campaign IDs, keys) from a process memory dump by locating decrypted config structures and decoding common obfuscation layers. Activates for requests to extract malware config from a memory dump, recover C2 settings from a running sample, or decode an in-memory configuration block.

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。

Computes cryptographic and fuzzy fingerprints for malware samples: MD5/SHA-1/SHA-256 for exact identity, plus import hashing (imphash), section hashing, and fuzzy hashes (ssdeep, TLSH) to cluster related variants. Activates for requests to hash a sample, compute imphash, or fingerprint files for similarity clustering.

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。

Bypasses anti-VM and sandbox checks during analysis by locating the specific detection routines (artifact strings, timing, CPUID/hypervisor bit) and planning patches or environment hardening so the sample detonates. Activates for requests to bypass anti-VM checks, defeat sandbox detection, or make an evasive sample run for analysis.

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。

Detects sandbox and analysis evasion techniques in a sample by scanning static strings, imports, and API traces for VM artifact checks, timing/sleep stalls, anti-debug calls, and environment fingerprinting. Activates for requests to detect sandbox evasion, find anti-VM or anti-debug checks, or explain why a sample did not detonate.

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。

Analyzes the PE Rich header and related compiler artifacts to fingerprint the build toolchain, cluster related samples, and detect inconsistencies that suggest tampering. Activates for requests to analyze a Rich header, fingerprint the compiler/linker, or cluster samples by build toolchain.

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。

Validate, install, update, list, rescan, remove, and reveal data-only Pulp content packs for installed plugins. Use for presets, themes, samples, sample banks, wavetables, and other end-user data that must be reviewed before install or update.

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。

Research, author, validate, render, and archive data-only Pulp Sample Heritage profiles. Use when creating or revising a sampler character profile, importing or exporting Heritage JSON, proving profile behavior with analytic and listening fixtures, recording evidence and trademark-safe provenance, or preparing a caller-owned profile artifact bundle.

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。

Plan and execute integrative analysis combining multiple ENCODE experiments for cross-dataset or multi-omic workflows. Use when the user wants to combine experiments, perform cross-dataset comparison, multi-omic integration, peak overlap analysis, differential binding, signal correlation, chromatin state segmentation, enhancer-gene linkage, or any analysis that requires merging or comparing data from two or more ENCODE experiments. Covers same-assay cross-sample, multi-omic same-sample, cross-organism, and perturbation integration designs. Guides compatibility checks, batch effect detection, normalization, integration strategy selection, and provenance documentation.

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。

Set up and operate a Shopify store with Shopify's official MCP server (the installed `shopify` command, NOT the npm Shopify CLI). Use whenever someone wants to sell online, start an online store or business, try Shopify, or manage a Shopify store — even if they do not say "Shopify": "I want to sell candles online", "help me start an online business", "open my first store", "add products", "check my orders", "set inventory", "make a discount", "show my sales", "how did my Shopify Email campaigns do". Before connection it can show relevant mock.shop sample catalogs, suggest business names, check domains, search shopify.dev, and validate GraphQL. The connect flow can create an account and store for a new user. Do the work here rather than only listing setup steps. Keywords: Shopify MCP, ecommerce, sample products, demo store, starter catalog, ShopifyQL, first store, create product.

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。

Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.

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 and evaluate paid-ad experiments with hypotheses, randomization units, sample-size and duration assumptions, guardrails, platform experiment tools, analysis, and decision rules. Use for A/B test, split test, experiment design, hypothesis, statistical significance, sample size, test duration, or experiment readout.

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 fully customized Amazon Connect AI agent PoC bundle (Lambda handlers, OpenAPI spec, AI agent prompt, Contact Flows, CloudFormation/CDK infrastructure, FAQ knowledge base) from a ~15-minute structured interview — or from a requirements doc, a flow sketch, or an existing flow/prompt you want to improve. Use when the user wants to build, scaffold, prototype, or refine an Amazon Connect contact center, an AI voice/chat agent on Amazon Connect, or any contact-center use case that mentions Connect, Lex, Wisdom, Q in Connect, "AI agent domain", or Agentic CX Designer (ACXD, Connect Customer, the Agentic CX block — the ACXD runtime target). Also triggers on "I have a requirements document", "turn this flow sketch into a Contact Flow", or "improve my existing Contact Flow / AI prompt". Produces 6 asset packages on local disk.

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 fully customized Amazon Connect AI agent PoC bundle (Lambda handlers, OpenAPI spec, AI agent prompt, Contact Flows, CloudFormation/CDK infrastructure, FAQ knowledge base) from a ~15-minute structured interview — or from a requirements doc, a flow sketch, or an existing flow/prompt you want to improve. Triggers on "amazon connect", "aicc", "contact center AI", "connect ai agent", "connect ai agents domain", "agentic cx designer", "acxd", "connect customer", "agentic cx block", "requirements document", "improve my contact flow", "flow sketch", "\uc544\ub9c8\uc874 \ucee4\ub125\ud2b8", "\ucee8\ud0dd\uc13c\ud130 AI \uc0c1\ub2f4\uc6d0".

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 sample/demo screens that make library options explorable. Add controls, previews, presets, sample data, and QA surfaces in the app module.

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。