386 件 ・ 関連度順
概要と使いどころ
Reverse-engineer a company's sales and marketing tech stack from public signals. Detects CRMs, cold email tools, people databases, ad pixels, email delivery services, and outbound sending domains via DNS records, website source inspection, Apify technology profiling, blacklist checks, and public spam complaint searches. Works on single companies or batches. Outputs a structured markdown report per company.
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。
Generates an x-ray.md pre-audit report covering overview, enhanced threat model (protocol-type profiling, git-weighted attack surfaces, temporal risk analysis, composability dependency mapping), invariants, integrations, docs quality, test analysis, and developer/git history. Triggers on 'x-ray', 'audit readiness', 'readiness report', 'pre-audit report', 'prep this protocol', 'protocol prep', 'summarize this protocol'.
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Assembles a genome de novo from Illumina short reads with SPAdes (isolate/careful/sc/meta/plasmid/rna modes), MEGAHIT (low-memory, huge datasets), Unicycler (bacterial finishing/hybrid), MaSuRCA (large hybrid), ABySS (Bloom-filter), and Platanus (heterozygous diploids), using multi-k de Bruijn graphs. Covers the repeat-resolution limit, why N50 plateaus at the genome not the depth, GenomeScope2 k-mer profiling first, the heterozygosity/haplotig trap, error-correction erasing rare alleles, GC dropout, and NG50/auN/BUSCO reporting. Use when assembling a bacterial isolate, fungal, small-eukaryotic, single-cell, or metagenome genome from Illumina reads, or when deciding whether short reads can even produce the assembly being asked for.
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Profiles a genome from raw reads BEFORE assembly with a k-mer spectrum (KMC or Jellyfish histogram), then models it with GenomeScope2 to estimate genome size, heterozygosity, repeat content, and ploidy, and Smudgeplot to infer ploidy from heterozygous k-mer pairs (diploid AB vs triploid AAB vs tetraploid AABB). Covers choosing k via Merqury best_k.sh, the k-mer-coverage vs sequencing-coverage confusion, reading het/repeat/contamination/organelle peaks, why noisy ONT must not be used for counting, and how the estimate becomes the NG50 denominator, the Flye -g value, the hifiasm --hom-cov/purge setting, and the 1.5-2x-too-big haplotig sanity check. Use when starting any de novo assembly, deciding whether short reads can work, estimating genome size for an unknown organism, diagnosing ploidy, or sanity-checking an assembly's size against expectation.
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。
Identify direct miRNA-target interactions from AGO HITS-CLIP, AGO-CLEAR-CLIP (chimeric reads), HEAP (Halo-Ago2 mouse), chimeric eCLIP / miR-eCLIP (deep miRNA-target profiling), or CLASH using chimeric-read processing pipelines, seed-pairing analysis, and 3' auxiliary pairing rules. Use when distinguishing direct miRNA targets from indirect, integrating CLIP-derived target maps with TargetScan / miRDB / DIANA predictions, applying canonical 7mer-8mer seed matching with 3' UTR context, or recovering miRNA-mRNA chimeras at scale.
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 CUT&RUN (Skene Henikoff 2017) and CUT&Tag (Kaya-Okur 2019) chromatin profiling data. Handles SEACR vs MACS2 peak calling (with the btaf375 2025 benchmark guidance), pA-MNase vs pA-Tn5 vs pAG-Tn5 chimera differences, E. coli spike-in carryover normalization, IgG-only control logic (no input), characteristic fragment-size signatures (25-75 bp for CUT&Tag), and lower depth requirements (5M reads typical vs 25M for ChIP). Use when calling peaks from CUT&RUN/CUT&Tag, scaling by E. coli spike-in carryover, choosing SEACR norm mode, or comparing CUT&RUN/Tag results to traditional ChIP.
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 the user wants automated performance profiling, headless Instruments analysis, or CLI-based trace collection.
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 an Apple-platform app feels slow, memory grows, battery drains, or ANY performance issue needs diagnosing. Covers memory leaks, profiling, Instruments workflows, retain cycles, optimization.
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。
Integrate Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodel, .mlpackage, .mlmodelc), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.
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。
Debug iOS apps and profile performance using LLDB, the interactive Memory Graph Debugger, and Instruments. Use for crashes, retain-cycle inspection, hangs, build failures, and generic CPU, memory, energy, or network profiling. Use ios-memgraph-analysis for .memgraph capture, leaks CLI ownership paths, or persistent heap growth; use ios-ettrace-performance for ETTrace capture and JSON.
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 capturing or analyzing ETTrace profiles for a focused iOS launch or runtime flow, including exact-build dSYM UUID matching, Simulator or device capture, processed per-thread flamegraph JSON, sampled inclusive/exclusive time, unresolved symbols, and comparable verification. Use debugging-instruments or swiftui-performance for generic profiling instead.
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。
Speed up local parfor, parfeval, or spmd by switching to a thread-based parallel pool. Trigger when a user describes slow or disappointing local parallel performance, even if they don't mention threads. Symptoms: parfor on a laptop/workstation is slower than expected or "only slightly faster than for"; parfor scales poorly with the number of workers; ticBytes/tocBytes, the Parallel Pool dashboard, mpiprofile, or system tools show large per-worker data transfer; large broadcast variables or sliced inputs make iterations slow; opening a process pool dominates a short workload; user mentions serialisation or data transfer overhead. Also trigger on any question about whether code or a function works on a thread pool. For non-pool MATLAB performance work (vectorisation, preallocation, profiling), defer to matlab-optimize-performance.
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 DuckDB from MATLAB via Database Toolbox (R2026a+) as a non-math operations engine on large tabular files (CSV/Parquet/JSON) and as a zero-config embedded database. Use when connecting to DuckDB, querying CSV, Parquet, and JSON files directly with SQL, reducing or profiling large data before MATLAB analysis, creating portable development databases, or installing DuckDB extensions. Triggers on: DuckDB, duckdb(), large CSV/Parquet/JSON, file too large for readtable, filter/aggregate at source, deduplicate, reduce before analysis, profile large file, persistent file import, analytical engine, SQL on CSV, SQL on Parquet, SQL on JSON, query CSV with SQL, query Parquet with SQL, run SQL on files, SQL queries on files, query files directly, SQL without database, in-process SQL.
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, verify, refine, and accelerate C/C++ or CUDA code from MATLAB with MATLAB Coder, Embedded Coder, GPU Coder, or MATLAB Test. Also covers writing codegen-ready MATLAB code: language constraints, coder.* directives, and optimization patterns. Triggers on: codegen, MEX, deploy MATLAB as C/C++, GPU Coder, coder.screener, coder.config, coder.gpuConfig, coder.typeof, coder.runTest, matlabtest.coder.TestCase, SIL, embedded config, no dynamic memory, EnableMexProfiling, coder.timeit, coder.perfCompare, %#codegen, writing codegen-ready MATLAB, code generation readiness, coder.varsize, coder.unroll, coder.noImplicitExpansionInFunction, coder.ceval, coder.inline, coder.extrinsic, coder.const, coder.classSignature, class codegen limitations, temporal types codegen, DMA-off, stack-only, host-target InstructionSetExtensions, SIMDAcceleration, OptimizeReductions, host SIMD tuning, host OpenMP, codegen performance.
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Build and risk-screen a compact role-based method shortlist for a mathematical-modeling subquestion. Use after problem framing and data profiling, before model code generation, to propose a main candidate, a usable baseline, and at most one conditional fallback without padding the pool.
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 a Godot game stutters, drops frames, hitches when entering new areas, overheats a phone, or must ship: profiling and Performance monitors, CPU or GPU bound, draw calls, MultiMesh, occlusion culling, LOD, shader compilation hitches, export to macOS, Windows, Linux, Steam, Android (APK, AAB, keystore), iOS (Xcode), web (itch.io, COOP/COEP headers, SharedArrayBuffer, JavaScript interop), build size budgets, demo and DLC builds, signing and notarization.
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 rigging a character, creature or prop in Maya: "rig this character", placing or orienting joints, Orient Joint doing nothing, IK/FK switch and matching, pole vector flips or elbow drift, space switching, foot roll, spline IK spine, offsetParentMatrix, blendMatrix or matrix rigging, controls and lockdown for animators, Quick Rig, HumanIK, AdvancedSkeleton or mGear, a game skeleton for Unreal or Unity, or a slow rig (parallel evaluation, profiling, GPU Override not kicking in).
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。
Iterative measurement-driven improvement loop. Measure, profile, mutate, re-measure, commit. Works for performance, bundle size, complexity, test coverage — anything quantifiable. Use when the user wants to systematically improve a metric through repeated cycles of profiling and targeted changes.
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。
DNA sequence profiling and genetic variant characterization
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。
Performance profiling and analysis
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。
Browser-based QA combining Chrome MCP control with Python analysis tools. Use when performing browser QA testing, visual regression tracking, WCAG accessibility auditing, performance profiling, or health-scoring web applications.
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。
Performance profiling for Node.js, Python, and Go: CPU flamegraphs, memory leak detection, bundle analysis, query optimization, and k6 load testing. Use when diagnosing slow endpoints, memory growth, large bundles, or traffic spikes.
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。
Signup and registration flow optimization covering SSO strategy, progressive profiling, field reduction, multi-step flow design, authentication UX, post-submit experience, and mobile registration patterns.
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。
When the user wants to research, profile, or analyze competitors from their URLs. Also use when the user mentions 'competitor profile,' 'competitor research,' 'competitor analysis,' 'profile this competitor,' 'analyze competitor,' 'competitive intelligence,' 'competitor deep dive,' 'who are my competitors,' 'competitor landscape,' 'competitor dossier,' 'competitive audit,' or 'research these competitors.' Input is a list of competitor URLs. Output is structured competitor profile markdown files. For creating comparison/alternative pages from profiles, see competitors. For sales-specific battle cards, see sales-enablement.
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。