'c2' 검색 결과

Map Azure architectures and operations to security and compliance controls. Use this skill when users ask for governance, policy enforcement, audit readiness, or regulatory alignment. Covers ISO, SOC2, HIPAA, PCI-DSS, and custom compliance frameworks.

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Cryptographic security for agentic systems — zero-trust agent networking, signed message envelopes (JWS/JWE), capability-based security (ocaps), Merkle tree audit trails, WASM sandboxing, and formal verification. Covers CLI dev tool security, mTLS between agents, permission boundaries (least privilege for AI agents), and supply chain security for skills/plugins. Activate on: "agent security", "zero trust agents", "secure agent communication", "capability-based security", "ocap", "signed messages between agents", "agent audit trail", "sandbox agent execution", "agent permissions", "mTLS agents", "cryptographic verification", "agent supply chain", "OWASP agentic", "prove agent did X", "tamper-proof agent logs". NOT for: application-level SAST scanning (use security-auditor), network firewall rules (use infrastructure), SOC2/HIPAA compliance (organizational), or prompt injection defense (use prompt-engineer).

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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 subclonal copy number, whole-genome doubling, and copy-number tumor evolution from bulk sequencing with Battenberg, TITAN, and MEDICC2. Covers clonal versus subclonal copy-number states, haplotype phasing for subclonal resolution, cancer cell fraction, whole-genome-doubling detection and timing relative to mutations, mirrored subclonal allelic imbalance, and copy-number phylogenies. Use when a tumor is heterogeneous and bulk data shows non-integer copy number, when calling subclonal CNAs, detecting or timing whole-genome doubling, reconstructing copy-number evolution, or deciding between Battenberg and TITAN.

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Assesses ChIP-seq quality across antibody specificity, fragmentation, enrichment, replicate concordance, and library complexity. Computes FRiP, NSC/RSC (phantompeakqualtools), library complexity (NRF/PBC1/PBC2), deepTools plotFingerprint (JS distance, AUC, synthetic JS), ChIPQC, IDR with ENCODE Nself/Nt rules, and detects hyper-ChIPable artifacts. Use when validating an antibody, diagnosing failed peak calls, deciding whether to proceed with downstream analysis, grading against ENCODE thresholds, or auditing replicate concordance.

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Treats a ctDNA assay as a molecule-counting experiment at the Poisson edge and builds its analytical-validation case the measurement-science way. Covers the genome-equivalent currency (~330 haploid copies/ng), the lambda = input_GE x VAF sampling ceiling (lambda>=3 for ~95% detection), the error-suppression ladder (raw NGS ~1e-3 -> single-strand UMI ~1e-4/1e-5 -> duplex <1e-7), the CLSI EP17 LoB/LoD/LoD95/LoQ framework, the per-locus-vs-panel-integrated LoD distinction that lets bespoke MRD reach ppm, contrived/SEQC2 reference standards, and honest LoD reporting conditioned on input mass + consensus depth + replicate detection rate. Use when stating or trusting a sensitivity claim, designing a dilution-series validation, deciding how many genome equivalents are needed at a target VAF, choosing a single-locus vs panel-integrated LoD, or auditing a "detects 0.1% VAF" claim.

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ATAC-seq library quality control -- TSS enrichment, FRiP, fragment-size periodicity, library complexity (NRF/PBC1/PBC2), mitochondrial fraction, and ENCODE 4 thresholds. Use when assessing whether an ATAC-seq library passes ENCODE acceptance criteria, diagnosing transposition artefacts, comparing Omni-ATAC vs standard prep quality, or selecting which replicates to drop before peak calling.

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Audits and designs iOS/macOS/watchOS/visionOS interfaces against the Apple Human Interface Guidelines, including the Liquid Glass design language (announced WWDC25, shipped with iOS 26/macOS Tahoe, Sept 2025). Use when reviewing an Apple-platform mockup or app for HIG compliance, checking contrast or tap-target sizes, or designing native-feeling Apple UI (e.g., 'audit my iOS app against the HIG', 'is this text readable on Liquid Glass?').

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Expert-level special relativity knowledge. Use when working with Lorentz transformations, time dilation, length contraction, relativistic energy, mass-energy equivalence, spacetime, four-vectors, or relativistic mechanics. Also use when the user mentions 'Lorentz transformation', 'time dilation', 'length contraction', 'E=mc2', 'spacetime', 'four-vector', 'relativistic momentum', 'light cone', 'proper time', 'Minkowski', or 'relativistic velocity addition'.

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Validate Commodore MPS-802 and 1526 serial dot matrix printer behavior in EMULATORS, including SerialPrinterMPS802 classes and their MatrixPrinterEmulation and SerialPrinterPeripheralSimulation hierarchy, IEC/KERNAL-trap printer routing, device numbers, secondary-address functions, PETSCII/character sets, 8x8 glyphs, enhanced width, reverse and paging modes, formatter strings, programmable characters, line/page geometry, PostScript output, and fidelity boundaries. Use when Codex reviews or edits COMMODORE MPS802 printer code, generated printer output, PostScript printer templates, C64/VIC-20/C264 serial-printer behavior, or compares implementation against .codex/docs/MPS802.pdf, the MPS-802 user guide, and 1526/MPS-802/4023 service information.

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Validate Commodore 1540/1541/1541C/1541-II compatible disk-drive behavior in EMULATORS, including IEC serial bus protocol, device numbers, KERNAL traps, CBM DOS commands/status, D64 image parsing, BAM/directory/file chains, PETSCII filenames, 6502 drive CPU/ROM/RAM expectations, 6522 VIA wiring, GCR/track/sector geometry, motor/head timing, and compatibility boundaries for 1541-like devices. Use when Codex reviews or edits C64, VIC-20, C264, COMMODORE disk, serial I/O, D64, CBM DOS, IEC, VIA, GCR, or 1541 drive code.

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Validate Commodore MPS-801 serial dot matrix printer behavior in EMULATORS, including SerialPrinterMPS801 classes and their MatrixPrinterEmulation and SerialPrinterPeripheralSimulation hierarchy, IEC/KERNAL-trap printer routing, device numbers, secondary addresses, PETSCII/character sets, 6x7 glyphs, double width, graphics mode, reverse field, tab and dot positioning, line/page geometry, PostScript output, and fidelity boundaries. Use when Codex reviews or edits COMMODORE MPS801 printer code, generated printer output, PostScript printer templates, C64/VIC-20/C264 serial-printer behavior, or compares implementation against .codex/docs/MPS801.pdf and historical MPS-801 sources.

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The crouton-conformance gate — the SECOND acceptance axis of graduation (the first is the experience side-by-side, /graduate stage C1). Checks a graduated app is a REAL crouton app — CLI-scaffolded, real collections, consumes the correct packages without reinventing them — by composing the existing probes (frontend-review, a11y, e2e-smoke, typecheck) plus crouton-structural checks, then renders a required, signed-off checklist on the graduation PR. Use at /graduate stage C2, or when asked "is this app crouton-native", "run the conformance gate", "check graduation conformance".

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This skill teaches security teams how to detect and respond to unauthorized cryptocurrency mining operations in cloud environments. It covers identifying cryptomining indicators through compute usage anomalies, network traffic patterns to mining pools, GuardDuty CryptoCurrency findings, and runtime process monitoring on EC2, ECS, EKS, and Azure Automation workloads.

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Systematically deobfuscate multi-layer PowerShell malware using AST analysis, dynamic tracing, and tools like PSDecode and PowerDecode to reveal hidden payloads and C2 infrastructure.

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This skill teaches security teams how to deploy and operationalize Amazon GuardDuty for continuous threat detection across AWS accounts and workloads. It covers enabling protection plans for S3, EKS, EC2 runtime monitoring, and Lambda, interpreting finding severity levels, and building automated response workflows using EventBridge and Lambda.

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Build an automated system to track adversary infrastructure using passive DNS, certificate transparency, WHOIS data, and IP enrichment to map and monitor threat actor command-and-control networks.

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Parse NetFlow v9 and IPFIX records to detect volumetric anomalies, port scanning, data exfiltration, and C2 beaconing patterns. Uses the Python netflow library to decode flow records, builds traffic baselines, and applies statistical analysis to identify flows with abnormal byte counts, connection durations, and periodic timing patterns.

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Detect PowerShell Empire framework artifacts in Windows event logs by identifying Base64 encoded launcher patterns, default user agents, staging URL structures, stager IOCs, and known Empire module signatures in Script Block Logging events.

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Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts. Uses Wireshark, Zeek, and NetFlow analysis techniques. Activates for requests involving network traffic analysis, packet capture investigation, PCAP analysis, network forensics, C2 traffic detection, or exfiltration detection.

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- Verify whether an image or video is authentic, original and correctly captioned — provenance checks, error level analysis, noise and JPEG compression analysis, clone and copy-move detection, lighting and shadow consistency, C2PA… Use when fact-checking a photo or video, checking for a deepfake or AI-generated image, spotting manipulation, or testing whether footage is recycled or miscaptioned

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ВОРОНКА «ПОЛЕЗНЫЙ ЧЕЛОВЕК → ДИАЛОГ → ЗВОНОК»: каждый, кто дал нам пользу / написал качественный коммент / покритиковал по-доброму / поделился знанием — получает проактивный диалог и зов познакомиться на звонке. Триггеры: “/comment-to-call“, “/c2c“, «выведи комментаторов на звонок», «кого звать на звонок», «прогони воронку по комментам», «comment to call»

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Use when the user asks to "codify how our founder sounds", "build a founder voice dossier", or "set our content pillars"; runs an 80%-extraction interview over the user's OWN posts, emails, and decks (never competitor scraping, never an invented persona) and produces the versioned voice record — a per-platform register map (incl. 小红书/微信公众号), banned phrases, per-context disclosure lines (the ECHO C2 upstream), a few-shot bank built exclusively from own posts, and 3-5 content pillars with Estimated %-allocations — submitted via memory/events/channels.ndjson for channel-registry to store as voice-dossier.md, the record every Craft-phase skill reads first. Not for the brand-level voice canon — use brand-language-codifier; not for audience/persona research — use audience-mapper. 声音档案/品牌语气/创始人语气/内容支柱/披露声明

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Use when working with Aws Inspector — aWS Inspector vulnerability management and finding analysis. Covers Inspector v2 coverage, vulnerability findings, finding summaries, scan configurations, suppression rules, and coverage statistics. Use when auditing vulnerability posture, reviewing Inspector findings, checking scan coverage, or analyzing security vulnerabilities across EC2, ECR, and Lambda resources.

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