4.9K skills · sorted by relevance
Trace how access to cloud metadata leads to issued credentials and accepted privileges in a CTF sandbox. Organize endpoints, identity details, and API evidence into a minimal, reproducible path showing where trust is granted.
Examine Windows and Active Directory logs for attempts to extract passwords and other credentials. Identify suspicious memory access and database exports, correlate events with user and host context, and produce findings with severity ratings and detection queries.
Investigate DCSync attacks, which abuse Active Directory replication to steal password hashes. Use audit events and network traffic to identify suspicious replication requests, examine their sources, and check for subsequent credential misuse.
Deploy decoy credentials, documents, and URLs with Canarytokens to detect suspicious access. Guides token generation, placement, alert testing, and inventory tracking, with email or webhook notifications when someone interacts with a planted token.
Use shared organization credentials to call search services, paid APIs, or Git remotes through a broker, an intermediary that adds authentication without revealing the secret. Requests stay within the configured hosts, paths, and permitted operations.
Work with geographic points, lines, and areas in Python. Read spatial files, combine datasets by location, calculate areas and distances, transform coordinate systems, and export results as maps or converted files.
Build and run the GPUNetIO benchmark to measure sustained RDMA WRITE bandwidth, a remote-memory transfer rate driven by a CUDA kernel. Interpret results, investigate run-to-run variation, and identify GPU, network adapter, or link bottlenecks.
Azure Identity library for .NET. Authentication library for Azure SDK clients using Microsoft Entra ID. Use for DefaultAzureCredential, managed identity, service principals, and developer credentials. Triggers: "Azure Identity", "DefaultAzureCredential", "ManagedIdentityCredential", "ClientSecretCredential", "authentication .NET", "Azure auth", "credential chain".
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Compares Hi-C contact maps between conditions across the right scale -- differential bin-pair contacts (multiHiCcompare, diffHic), differential A/B compartments (dcHiC), differential TAD boundaries (delta insulation), and differential loops (diffloop, DiffHiChIP) -- with distance-stratified between-sample normalization, replicate-aware NB-GLM FDR, HiCRep SCC reproducibility gating, and CNV correction for cancer/aneuploid samples. Use when comparing Hi-C between treatment and control, finding differential contacts/compartments/boundaries/loops, normalizing two maps of unequal depth, choosing a replicate-aware test, gating replicates with SCC, or correcting copy-number artifacts before a tumor-vs-normal comparison.
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Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.
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This package implements functions to find influential TF and target based on different input type. It have five module: Multi-peak multi-gene annotaion(mmPeakAnno module), Calculate regulation potential(calcRP module), Find influential Target based on ChIP-Seq and RNA-Seq data(Find influential Target module), Find influential TF based on different input(Find influential TF module), Calculate peak-gene or peak-peak correlation(peakGeneCor module). And there are also some other useful function lik
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Intuitive framework for identifying spatially variable genes (SVGs) via edgeR, a popular method for performing differential expression analyses. Based on pre-annotated spatial clusters as summarized spatial information, DESpace models gene expression using a negative binomial (NB), via edgeR, with spatial clusters as covariates. SVGs are then identified by testing the significance of spatial clusters. The method is flexible and robust, and is faster than the most SV methods. Furthermore, to the
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Fix slow Flutter app startup caused by blocking network operations during initialization. Use when: (1) App takes several seconds to show first frame, (2) Startup logs show sequential network operations (WebSocket connections, API calls), (3) Services initialize during startup that aren't needed until user authenticates. Covers: converting sequential network ops to parallel with Future.wait(), deferring service initialization until actually needed (lazy init), and identifying blocking operations in Riverpod/provider initialization chains.
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Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.
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Expert-level differential equations knowledge. Use when working with ordinary differential equations, partial differential equations, systems of ODEs, Laplace transforms, Fourier methods, boundary value problems, stability analysis, or numerical methods for ODEs. Also use when the user mentions 'ODE', 'PDE', 'initial value problem', 'boundary value problem', 'Laplace transform', 'separation of variables', 'eigenvalue problem', 'heat equation', 'wave equation', 'Laplace equation', 'stability', or 'phase plane'.
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Audit, detect, and remediate Shadow Credentials misconfigurations on msDS-KeyCredentialLink in Active Directory. Focuses on access-controls, monitoring Event ID 5136, and credential-management hygiene.
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Compares Hi-C contact maps between conditions across the right scale -- differential bin-pair contacts (multiHiCcompare, diffHic), differential A/B compartments (dcHiC), differential TAD boundaries (delta insulation), and differential loops (diffloop, DiffHiChIP) -- with distance-stratified between-sample normalization, replicate-aware NB-GLM FDR, HiCRep SCC reproducibility gating, and CNV correction for cancer/aneuploid samples. Use when comparing Hi-C between treatment and control, finding differential contacts/compartments/boundaries/loops, normalizing two maps of unequal depth, choosing a replicate-aware test, gating replicates with SCC, or correcting copy-number artifacts before a tumor-vs-normal comparison.
The English summary is being prepared. Showing the original description.
Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.
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Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 with `bam_ip` + `bam_input` (control arm) and `bam_treated_ip` + `bam_treated_input` (treatment arm) for integrated GC-bias-aware differential calling (Liu 2022 *NAR Genom Bioinform* 4:lqac046), QNB beta-binomial test (Liu 2017 *BMC Bioinformatics* 18:387), MeTDiff HMM-based differential bundled with MeTPeak, RADAR (Zhang 2019 *Genome Biol* 20:294) with its `filterBins -> diffIP -> reportResult` workflow, and the defensible paired-symmetric case of edgeR / DESeq2 on featureCounts-on-peaks matrices when batch / lot covariates need explicit fixed-effect handling (exomePeak2's top-level API does NOT accept arbitrary covariates). Covers paired vs unpaired vs interaction designs, batch confounding (antibody lot, RNA prep, sequencing run) and the per-lot meta-analysis strategy when exomePeak2 is the primary caller, the stoichiometry-vs-expression-vs-IP-efficiency confound that all MeRIP differential methods inherit, normalisation choice (size factor on IP, on input, on per-sample IP/input ratio), the McIntyre 2020 reproducibility caveat, effect-size filtering as a guardrail against under-powered N=2 designs, and orthogonal-validation routes for absolute stoichiometry (GLORI / SAC-seq / m6Anet `mod_ratio`). Use when comparing m6A levels across two or more conditions, choosing between exomePeak2 / QNB / RADAR / MeTDiff for a given design, handling batch confounding when exomePeak2's API is too rigid, normalising against input properly, distinguishing real hyper- / hypo-methylation from expression-level shifts, applying effect-size thresholds, interpreting volcano plots of differential peaks, or planning a follow-up orthogonal stoichiometry validation.
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Compares Hi-C contact maps between conditions across the right scale -- differential bin-pair contacts (multiHiCcompare, diffHic), differential A/B compartments (dcHiC), differential TAD boundaries (delta insulation), and differential loops (diffloop, DiffHiChIP) -- with distance-stratified between-sample normalization, replicate-aware NB-GLM FDR, HiCRep SCC reproducibility gating, and CNV correction for cancer/aneuploid samples. Use when comparing Hi-C between treatment and control, finding differential contacts/compartments/boundaries/loops, normalizing two maps of unequal depth, choosing a replicate-aware test, gating replicates with SCC, or correcting copy-number artifacts before a tumor-vs-normal comparison.
The English summary is being prepared. Showing the original description.
Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 with `bam_ip` + `bam_input` (control arm) and `bam_treated_ip` + `bam_treated_input` (treatment arm) for integrated GC-bias-aware differential calling (Liu 2022 *NAR Genom Bioinform* 4:lqac046), QNB beta-binomial test (Liu 2017 *BMC Bioinformatics* 18:387), MeTDiff HMM-based differential bundled with MeTPeak, RADAR (Zhang 2019 *Genome Biol* 20:294) with its `filterBins -> diffIP -> reportResult` workflow, and the defensible paired-symmetric case of edgeR / DESeq2 on featureCounts-on-peaks matrices when batch / lot covariates need explicit fixed-effect handling (exomePeak2's top-level API does NOT accept arbitrary covariates). Covers paired vs unpaired vs interaction designs, batch confounding (antibody lot, RNA prep, sequencing run) and the per-lot meta-analysis strategy when exomePeak2 is the primary caller, the stoichiometry-vs-expression-vs-IP-efficiency confound that all MeRIP differential methods inherit, normalisation choice (size factor on IP, on input, on per-sample IP/input ratio), the McIntyre 2020 reproducibility caveat, effect-size filtering as a guardrail against under-powered N=2 designs, and orthogonal-validation routes for absolute stoichiometry (GLORI / SAC-seq / m6Anet `mod_ratio`). Use when comparing m6A levels across two or more conditions, choosing between exomePeak2 / QNB / RADAR / MeTDiff for a given design, handling batch confounding when exomePeak2's API is too rigid, normalising against input properly, distinguishing real hyper- / hypo-methylation from expression-level shifts, applying effect-size thresholds, interpreting volcano plots of differential peaks, or planning a follow-up orthogonal stoichiometry validation.
The English summary is being prepared. Showing the original description.
Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.
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Locate CBaseIssue::CountPotentialVoters in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the local gamedata entry "CBaseIssue::CountPotentialVoters" (symbol CBaseIssue_CountPotentialVoters). This is a non-virtual function - emit a byte signature (func_sig), not an offset. Locate it via cross-references, distinctive constants/strings in its body, or callers of related symbols; verify by decompilation before committing to a candidate. Trigger: CBaseIssue_CountPotentialVoters, CBaseIssue::CountPotentialVoters
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Tactical negotiation framework based on Chris Voss's "Never Split the Difference." Use when preparing for negotiations, during live negotiation scenarios, analyzing counterpart behavior, crafting responses to difficult conversations, handling objections, salary/contract negotiations, or when asked about negotiation techniques like mirroring, labeling, calibrated questions, or the Ackerman method.
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