'resistance' 검색 결과

Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Li & Hart 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving 2-3x sensitivity over MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).

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API로 사용하는 언어 모델의 프롬프트와 입력 표현을 바꾸어 안전 필터 우회 시도에 대한 응답을 검증합니다. 여러 모델의 답변 비교와 Hermes 설정 저장도 다룹니다.

• 모델의 안전장치 우회 저항성 평가
• 입력 표현에 따른 응답 차이 비교
• 여러 모델의 거절·품질·속도 비교

Plan structured adversarial tests for AI applications to assess safety bypasses, data leaks, and unauthorized tool use. Organize test prompts, record findings, and prioritize fixes using reporting guidance and an adaptable testing template.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess an AI feature before release
• Compare security behavior of candidate models
• Retest after a prompt injection or jailbreak incident

Build repeatable evaluations for AI agent responses, tool choices, and multi-step tasks. Use reference datasets and scoring criteria to compare changes, detect quality regressions, and set automated checks that must pass before deployment.

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• Compare agent quality before and after prompt changes
• Test tool selection and multi-step task execution
• Evaluate resistance to prompt injection

Assess the security of LLM applications and AI agents by tracing inputs, retrieved content, tool calls, and outputs. Covers prompt injection, where content carries unwanted instructions, along with permission misuse, memory poisoning, and supply-chain risks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Testing chatbot resistance to prompt injection
• Auditing agent tool permissions and approval steps
• Checking retrieval access controls and memory poisoning

Assess iOS and Android app security using OWASP MASTG, a mobile security testing guide. Examine app files, runtime behavior, stored data, authentication, and API traffic, then document vulnerabilities, their impact, and suggested fixes.

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• Assess mobile app security before release
• Check sensitive data storage and network traffic
• Test authentication and resistance to runtime manipulation

Plan how to approach a key stakeholder by examining their role, authority, concerns, and situation. Get tailored messaging, communication methods, frequency, mutual expectations, and a concrete next action with an owner and deadline.

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• Preparing executive alignment before a roadmap review
• Planning a conversation with a resistant stakeholder
• Including frontline users in decisions that affect them

Submit security scans for AI services, tools, skills, and agents through AI-Infra-Guard, then retrieve progress and results. It also supports jailbreak evaluation, testing whether prompts can bypass a model’s safety restrictions.

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• Scanning AI services for known vulnerabilities
• Auditing MCP tools and skill source packages
• Checking agents from saved settings or YAML files

Tests whether user messages can override an AI agent’s instructions or impersonate a higher authority. It checks for exposed internal instructions and changed behavior, then reports confirmed findings with prompts and responses as evidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Testing resistance to direct instruction overrides
• Checking for exposed system or developer instructions
• Checking behavior changes caused by role impersonation

Test whether instructions hidden in simulated documents, retrieved passages, or web pages can redirect an agent. The skill checks for unwanted behavior or sensitive disclosures and reports confirmed failures with the test dialogue as evidence.

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• Testing resistance to instructions embedded in uploaded documents
• Checking retrieved passages used to answer questions
• Evaluating hidden instructions in web page content

Test whether an agent misuses user trust by requesting secrets, making false safety guarantees, recommending unsafe actions, or impersonating authority. Confirmed issues are recorded with dialogue evidence, potential impact, and suggested improvements.

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• Testing whether an agent requests passwords or private keys
• Checking for misleading safety guarantees
• Reviewing advice that could lead to unsafe operations

Build SHA hashing applications for BlueField or ConnectX accelerators with guidance on single-request versus chunked processing, supported algorithms, buffer sizes, memory permissions, and errors returned by the DOCA SHA API.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Offloading SHA-256 hashing to BlueField
• Choosing single-request or chunked hashing
• Checking algorithm support and buffer limits

Comprehensive US stock analysis including fundamental analysis (financial metrics, business quality, valuation), technical analysis (indicators, chart patterns, support/resistance), stock comparisons, and investment report generation. Use when user requests analysis of US stock tickers (e.g., "analyze AAPL", "compare TSLA vs NVDA", "give me a report on Microsoft"), evaluation of financial metrics, technical chart analysis, or investment recommendations for American stocks.

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This skill should be used when analyzing weekly price charts for stocks, stock indices, cryptocurrencies, or forex pairs. Use this skill when the user provides chart images and requests technical analysis, trend identification, support/resistance levels, scenario planning, or probability assessments based purely on chart data without consideration of news or fundamental factors.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Statistical methods for calling hits in CRISPR screens. Covers MAGeCK, BAGEL2, drugZ, and custom approaches for identifying essential and resistance genes. Use when identifying significant genes from screen count data after QC passes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

MAGeCK (Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout) for pooled CRISPR screen analysis. Covers count normalization, gene ranking, and pathway analysis. Use when identifying essential genes, drug targets, or resistance mechanisms from dropout or enrichment screens.

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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.

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Authentication bypass testing playbook. Use when assessing login flows, password reset logic, account recovery, MFA bypass, token predictability, brute-force resistance, and session boundary flaws.

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Process CRISPR screening data to identify essential genes and hit candidates. Performs quality control, statistical analysis (RRA), and hit calling for pooled CRISPR screens including viability screens and drug resistance/sensitivity studies.

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Create a structured change management plan for any organisational change. Use when asked to write a change management plan, manage a change initiative, plan a system rollout, or lead an organisational transformation. Produces a plan covering stakeholder analysis, impact assessment, communication strategy, and resistance management.

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Prepare the conversations with aging parents that everyone postpones — the driving talk, the money talk, the care-options talk, the moving talk — each with an opener that doesn't ambush, a dignity-first script, rehearsal against realistic resistance, and the fallback when it goes badly. Use when someone says 'I need to talk to my dad about driving', 'my mum won't discuss her finances', 'we need to talk about care', or is dreading a visit for exactly this reason. Produces the conversation plan, a rehearsal, and the small-steps fallback. A preparation tool, not family therapy — and it says so when the situation needs more.

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Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.

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Resolve the architecture of focal oncogene amplifications — extrachromosomal DNA (ecDNA), breakage-fusion-bridge (BFB) cycles, homogeneously staining regions (HSR), and linear amplification — from whole-genome sequencing with AmpliconArchitect, the AmpliconSuite pipeline, and AmpliconClassifier. Covers copy-number seed selection, breakpoint-graph reconstruction, balanced-flow optimization, ecDNA classification, and the limits of depth-only amplification calls. Use when a focal amplification needs structural characterization, when distinguishing ecDNA from chromosomal amplification, suspecting ecDNA-driven oncogene amplification or therapy resistance, or selecting copy-number seeds for amplicon reconstruction.

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Run structured AI red team exercises for jailbreak resistance, data exfiltration risk, harmful output controls, and agent tool abuse resilience.

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