2,155 件 ・ 関連度順
概要と使いどころ
Summary statistics, data exploration, and descriptive analysis. Use for: summary stats, describe data, data exploration, distributions, means, medians, correlations, cross-tabs.
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Monitor social platforms for security threats, vulnerability discussions, and breach intelligence using Xpoz. Use when asked to "find CVE discussions", "security threat monitoring", "OSINT social media", "vulnerability intelligence", "breach mentions", or "threat intel from Twitter/Reddit".
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Auto-activate for Google ADK, LlmAgent, Runner, SQLSpecSessionService, google-genai, AgentRuntime, SpecTree, DynamicWorkflow, FunctionTool, or SSE agent chats. Not for offline ML training.
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Perform comprehensive technical reviews of Ruby on Rails applications. Runs automated analysis tools (RubyCritic, Brakeman, bundler-audit, Gitleaks, Debride, linters, SimpleCov, Rails stats, Rails ERD), analyzes code for architecture, security, authorization (Pundit/CanCanCan), dead code, and design issues, and produces a structured markdown report with prioritized findings and a 0-10 score. Use when the user requests a tech review, code audit, project assessment, or quality analysis of a Rails application.
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。
Ground GEODE design and evaluation work in Stanford CS329A Parts 2 and 5. Use for test-time compute, planning, multi-step reasoning, deep research, LATS, independent subplans, best-of-N, repair depth, measurement replication, verifier authority, and GEODE/Eco²/SIL/Crucible comparisons.
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Runs the full implement-and-commit loop over a task list — selects the next incomplete sub-task, implements it, runs linters and tests, marks it done, commits, and repeats until the list is finished. Use when asked to work through a feature's task list at .ai/[feature]/tasks.md unattended, or to keep going until every sub-task is complete. For a single sub-task with no commit, use task-implementation; to stop for approval between each one, use iterative-development.
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Framework for coordinating meaning across autonomous agents that use different conceptual models, vocabularies, and knowledge representations. Treats ontologies as first-class negotiable resources rather than hardcoded implementation details.
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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.
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Conversation patterns and interaction protocols for multi-agent systems. Covers request/response, pub/sub, blackboard, delegation chains, debate, critique, consensus, fan-out/fan-in, supervisor-worker, and peer negotiation. Deep analysis of AutoGen conversation patterns, CrewAI delegation, LangGraph state passing, and FIPA-ACL performatives. Teaches how to design what agents say to each other and in what order. Activate on: "agent conversation", "agent protocol", "multi-agent debate", "agent delegation", "supervisor worker pattern", "agent voting", "consensus protocol", "fan-out fan-in", "agent negotiation", "blackboard pattern", "agent dialogue", "conversation topology", "agent handoff". NOT for: wire format or serialization (use agent-interchange-formats), orchestration infrastructure (use agentic-infrastructure-2026), single agent behavior (use agentic-patterns).
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Rapidly generate up to 25 distinct ad creative hypotheses for testing. Produces a matrix of angles (pain, benefit, social proof, urgency, curiosity, comparison) x formats (static, carousel hook, UGC-style, bold-text overlay) with headline, visual prompt, and rationale for each, then generates the visuals with any image tool. Use when the user wants "ad variations", "ad concepts", "creative testing batch", "give me 25 ads", or paid-social creative ideation. Part of the Creative Production set.
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。
Authenticate with the Openapi marketplace (oauth.openapi.com). Use when you need to create, inspect, renew or delete Bearer tokens, list available scopes, check wallet credit, subscriptions, or API usage stats. Required by every other openapi-* skill.
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Plan and run safe reverse engineering, decompilation, disassembly, instrumentation, de-obfuscation, and anti-analysis countermeasures for binaries, firmware, mobile apps, WebAssembly, JavaScript bundles, and .NET assemblies. Use when the user asks for reversing strategy, decompiler selection, binary analysis, malware-style triage, unpacking, deobfuscation, defeating anti-debug or anti-VM checks, Wakaru, Ghidra, IDA, RetDec, radare2, Rizin, Frida, angr, YARA, or command scripts that capture analysis stats.
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Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native pipelines.
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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).
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Differential abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell populations change in frequency or marker expression between conditions using diffcyt (edgeR/voom/GLMM for DA, limma/LMM for DS), with cydar, CITRUS, and compositional methods (sccomp, scCODA, DCATS) as alternatives. Covers the sample-is-the-experimental-unit principle, design/contrast and mixed-model formulas, compositionality of cluster proportions, and FDR across clusters. Use when comparing populations between groups, choosing a DA method, handling paired/batch designs, or deciding whether compositional correction is needed.
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Convert between sequence file formats (FASTA, FASTQ, GenBank, EMBL, Stockholm) and re-encode FASTQ quality offsets using Biopython Bio.SeqIO. Use when changing a file format for a downstream tool, fixing FASTQ quality encoding (Phred+33 vs Phred+64 vs Solexa), or when a conversion risks silently dropping annotations or quality scores.
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Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
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Processes eDNA metabarcoding from raw paired-end reads to species tables, navigating ASV (DADA2, UNOISE3) vs OTU (swarm v2) decision (Callahan 2017 vs Schloss multi-copy-16S critique), marker/primer choice (Leray COI, MiFish 12S, 515F/806R 16S, ITS2) with primer-specific bias, OBITools3 v3 command-name break (obi stats plural; .tar.gz taxonomy), tag-jumping with dual-indexing (Schnell 2015; NovaSeq 10x MiSeq), decontam as screening-not-classifier (Davis 2018), read-counts-not-abundance critique (Lamb 2019), site-occupancy modeling (Ficetola 2015), Naive-Bayes calibration limits (Bokulich 2018), and eDNA decay (Strickler 2015). Use when going from raw eDNA FASTQ to species tables, picking marker + denoising pipeline, deciding whether read counts represent abundance, applying occupancy modeling, configuring OBITools3 v3, or interpreting decontam output. Not for clinical 16S microbiome (see microbiome/amplicon-processing).
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Detect introgression and admixture between species or populations using Dsuite (Malinsky 2021 fast D-statistics), Patterson's D / ABBA-BABA test (Green 2010; Durand 2011), f4-ratio and f-branch statistic (Malinsky 2018), TreeMix (Pickrell & Pritchard 2012), HyDe (Blischak 2018), QuIBL (Edelman 2019), sprime (Browning 2018), Twisst (Martin 2017), PhyloNet (Than 2008) for explicit phylogenetic networks, and qpAdm / qpGraph (Patterson 2012). Distinguish introgression from incomplete lineage sorting (ILS), ancestral structure, ghost-lineage admixture, and rate variation. Use when testing inter-species gene flow, dating admixture events, identifying introgressed segments, building phylogenetic networks for reticulate evolution, or applying the ABBAclustering (Koppetsch-Malinsky-Matschiner 2024) framework for divergent-species gene flow.
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Prioritizes rare-disease variants from trio/quad WES/WGS with de novo (DeNovoGear, Triodenovo), compound-heterozygous phasing (WhatsHap), mosaic VAF tiering, phenotype-driven ranking (Exomiser, Phen2Gene, AMELIE), ClinGen gene-disease validity gating, and ACMG SF v3.2 secondary findings reporting. Use when running diagnostic exome / genome pipelines, identifying candidate Mendelian disease genes, screening for incidental findings, or auditing VUS reclassification cycles. The ACMG/AMP classification framework (PVS1 decision tree, Pejaver PP3/BP4 calibration, Tavtigian point system) is in clinical-databases/acmg-classification.
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Identify recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad events, running GISTIC2, or extracting copy-number mutational signatures.
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Performs gene-level association from GWAS summary statistics via genetically predicted tissue expression using FUSION, PrediXcan, S-PrediXcan, S-MultiXcan, UTMOST, MOSTWAS, kTWAS, EpiXcan, TIGAR-V2, and probabilistic fine-mapping with FOCUS and MA-FOCUS. Use when running TWAS from GWAS sumstats, prioritising candidate causal genes from a GWAS lead locus, picking single-tissue vs cross-tissue models, identifying LD-induced TWAS false positives, choosing ancestry-matched prediction weights, fine-mapping co-regulated TWAS hits, or triangulating TWAS with cis-eQTL Mendelian randomization and colocalization to nominate a causal gene.
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Estimates SNP heritability and partitions it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LDAK enrichment estimates, computing local heritability with HESS, estimating genetic correlation between traits, or producing publication-grade enrichment with calibrated sensitivity to model assumptions.
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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.
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。