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概要と使いどころ
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.
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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.
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The measurement surface for ALL Pulp DSP and audio-pipeline work — read it BEFORE writing or gating DSP, not only when something already sounds wrong. Covers the C++ harness (signal generators, metrics, assertions, RenderScenario, contracts), the offline Audio Doctor (magnitude/frequency response, THD/THD+N, phase/group delay), and their Python sibling the Audio Quality Lab (tools/audio/quality-lab — null residual + alignment, LTAS log-spectral distance, spectral flux/centroid, HNR, Theil-Sen drift slope, Kaiser-sinc resampling, license-guarded corpus, regression-net ratchet). TRIGGER on AUTHORING work — "build/design an oscillator/filter/synth/effect", "add a DSP module", "what should the acceptance gate be", "how do I measure aliasing / anti-aliasing / alias floor", "null against a reference", "is this DSP correct", "choose a tolerance", "golden/regression corpus for audio", "measure drift or jitter", "A/B two renders" — AND on DEBUGGING work — "is there sound / no audio / I hear nothing", "does this filter/compressor/synth/delay produce the right signal", "prove the DSP / prove the contract", "measure the frequency response", "what's the THD / is it distorting", "what's the group delay / phase response / measured latency", "magnitude response curve", "render a test tone and assert", "audio regression", "64-frame works but 128 is silent", "sample-rate change pitch-shifted it", "describe what's in this buffer", "audio doctor", "compare before/after a DSP refactor". Reach for this BEFORE hand-rolling any FFT, null test, alias measurement, pitch tracker, or golden-render script — most of it already exists in one of the two lanes. Test/tool layer over HeadlessHost — deterministic, no audio device, no speakers. Off the realtime thread entirely.
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Judge whether a change made things better with demonstrable proxy measures tied to the mechanism it targets, not wall-clock time. Load before choosing acceptance measures for any before/after or A/B claim — CI and fleet speed-ups, build speed, DSP/audio quality, render fidelity. Each proxy names its data source, detection floor and sample size, and every zero is paired with a control on the same instrument.
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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.
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Gate resumption of any multi-day LLM batch-scoring campaign that calls an unpinned model alias (deepseek-chat, gpt-*-latest, gemini-*-preview, any provider alias without a pinned version). Use when: (1) resuming a paused or credit-exhausted scoring run days after its last chunk, (2) topping up credits to finish a campaign, (3) extending a cached scoring pipeline with new items. Prevents silently splicing two model versions or serving revisions into one measure. Verified 2026-07-16: for $0.30 caught a serving-revision drift WITHIN DeepSeek v4-flash (same alias, same family, litigation scores systematically shifted across a 2-day gap) before an $83 resume spend.
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Use when a task needs the judgment of an Aviation Inspector — determining whether an air carrier's fleet is in compliance with an Airworthiness Directive across a maintenance-records sample, deciding where the FAA's compliance-and-enforcement ladder places a finding (compliance action vs. Letter of Correction vs. civil penalty vs. certificate action), auditing a Safety Management System for a functioning safety-assurance loop rather than paperwork completeness, scoping a records-sampling strategy across a fleet under surveillance, or deciding whether to ground an aircraft pending an AD compliance finding.
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。
Detect keyword cannibalization by identifying queries where multiple pages are competing for the same ranking. Uses Google Search Console data to find queries appearing across 2 or more pages. Use when user says "keyword cannibalization", "same keyword multiple pages", "cannibalizing keywords", or "duplicate rankings".
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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.
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Monitor SAM.gov federal contract opportunities (and follow-on awards) by NAICS, PSC, agency, set-aside, and keyword — produces a daily pipeline report with pursue/no-pursue scoring, capture-management cadence.
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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。
Use when working on the SAM3 plus Sapiens2 pose estimation pipeline, batch GPU processing, or VRAM and throughput issues.
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。
Troubleshooting Connected App OAuth flows — IP relaxation vs IP restriction, refresh token policy traps (default kills the connection on first refresh), session-revocation semantics, the OAuth error-code catalog (`invalid_grant`, `invalid_client_id`, `unsupported_grant_type`), per-user vs admin-pre-approved flows, and the user-policy check (Connected App must be assigned to the user via profile / permset). Covers the Login History debug trail. NOT for designing the OAuth flow itself — use integration/oauth-flows-and-connected-apps. NOT for a SAML / SSO login failure — use security/sso-saml-troubleshooting. Trigger keywords: 'invalid_grant', 'OAUTH_APP_BLOCKED', 'redirect_uri_mismatch', 'LoginHistory Status', 'LoginSubType', 'OauthRefreshToken', 'ForwardedForIp', 'SetupAuditTrail connected app', 'refreshTokenPolicy zero', 'isSecretRequiredForRefreshToken', 'works once then fails', 'diagnosis record'.
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。
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。
same-session two-turn planning fixture로 run-history 신호 검증
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.
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Calculate statistical power and sample sizes for RCTs. Use when user mentions: power analysis, sample size calculation, minimum detectable effect, MDE, statistical power, effect size, clustering effects, design effect, ICC, intra-cluster correlation.
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Create new skills, modify and improve existing skills, and measure skill quality. Use when users want to create a skill from scratch, edit or refine an existing skill, test a skill with sample prompts, or optimize a skill's description for better triggering accuracy.
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Implementing distributed tracing with W3C Trace Context — the byte-precise `traceparent` and `tracestate` header formats, OpenTelemetry's W3CTraceContextPropagator as the modern default (replacing X-B3-* and X-Datadog-*), head vs tail sampling, and HTTP→SQL trace propagation via sqlcommenter. Grounded in the W3C REC and OpenTelemetry specs.
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。
Normalizes and transforms RNA-seq count matrices for DE, visualization, clustering, and ML. Covers between-sample (TMM, TMMwsp, RLE/median-of-ratios, upper quartile), within-sample (TPM, FPKM/RPKM), variance-stabilizing (VST, rlog, log-CPM), GC-content correction (cqn, EDASeq), and single-cell (scran deconvolution, scanpy normalize_total). Encodes the composition-bias rationale, the "most genes not DE" assumption and its catastrophic failure modes (MYC amplification, apoptosis, viral host shutoff, prokaryotic stress), the "lengthScaledTPM is not TPM" naming trap, the "TPM is not for DE" rule, the blind=TRUE vs FALSE decision, ERCC spike-in normalization (SBN), and the single-cell zero-inflation breakdown of TMM/RLE. Use when choosing or applying normalization, debugging shifted-MA-plot diagnostics, handling zero-heavy single-cell data, or correcting GC bias.
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。
Call germline SNPs and indels with GATK HaplotypeCaller and the GVCF joint-genotyping workflow. Covers the local-reassembly + PairHMM mechanism (why HC beats pileup callers on indels), the -ERC GVCF reference-confidence model and <NON_REF> allele, BQSR-vs-DRAGSTR and --dragen-mode error modeling, allele-specific (AS_) annotations, and edge cases (ploidy, Mutect2 mitochondria mode, sex chromosomes/PAR, contamination gating). Use when deciding whether to use HaplotypeCaller vs a pileup or DRAGEN caller, whether BQSR still earns its place, whether to call per-sample GVCFs for a cohort, or how to handle non-diploid, mitochondrial, sex-chromosome, or contaminated samples. Not for post-calling filtering depth (see variant-calling/filtering-best-practices) or cohort joint-genotyping scaling (see variant-calling/joint-calling).
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。