「deno」の検索結果

Supabase (オープンソース Firebase 代替・BaaS) リファレンス。 PostgreSQL データベース、Auth (Email / OAuth / Magic Link / Phone / SSO)、Storage、 Edge Functions (Deno)、Realtime (subscriptions / presence / broadcast)、Vector embeddings、 supabase-js、supabase CLI、RLS (Row Level Security)、migrations。 PostgreSQL ベース。SQL Server / node-mssql は別スキル mssql。

Hono (軽量・マルチランタイム対応 Web フレームワーク) リファレンス。 Hono App, Context (c.req / c.json / c.text / c.html), HonoRequest, Routing, HTTPException、 Built-in Middleware (JWT, CORS, CSRF, Cache, Logger, Compress, Basic / Bearer Auth)、 Helpers (Cookie, Streaming, WebSocket, Factory, RPC)、 Cloudflare Workers / Deno / Bun / Node.js / AWS Lambda 対応。

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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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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Schema design, normalization decisions, indexing strategies, and migration safety for relational databases. Use when designing tables, choosing between normalize vs denormalize, selecting index types, planning zero-downtime migrations, or setting up connection pooling. Activate on "schema design", "normalization", "denormalization", "foreign key", "index strategy", "migration", "connection pooling", "composite key", "surrogate key", "soft delete", "polymorphic association". NOT for PostgreSQL-specific query tuning or EXPLAIN analysis (use postgresql-optimization), NoSQL or document database design, or cloud database provisioning.

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Restore and fix image quality — deblur, denoise, fix faces, and restore old documents using fal.ai's hosted restoration models.

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Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.

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Use when benchmarking denoise latency or profiling a diffusion bottleneck in SGLang.

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Run state-of-the-art machine learning models directly in JavaScript. `@huggingface/transformers` supports text, vision, audio, and multimodal tasks in browsers and Node.js / Bun / Deno, with WebGPU or WASM execution.

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Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.

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Nitro is the framework-agnostic server toolkit (powering Nuxt) for building and deploying web servers anywhere. Use when working with nitro.config, server routes/event handlers, route rules, caching, storage, tasks, websockets, or deploying to Node/Bun/Deno/Cloudflare/Vercel.

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Make a custom AI agent — Python or TypeScript/JavaScript, on a framework or hand-built — report what it did to Failproof AI, and run your own evaluator worker (the "eval pod") that scores those runs. Reach for it on vague phrasing too: "add observability to my agent", "why isn't my agent showing up?", "run an LLM judge on our own infra". Trigger when the user wants to: • plan an integration — which points in the agent loop to record; • instrument — add `failproofai-sdk` (Python) or `@failproofai/sdk` (Node, Bun, Deno, Next.js): turn on an adapter (LangChain/LangGraph, CrewAI, LlamaIndex, Pydantic AI, Vercel AI SDK, Mastra) or wire a hand-built loop; • verify — confirm events are written, or debug an integration that produces nothing; • evaluate — write, deploy or debug an Evaluator worker in Python or TypeScript. NOT for reading telemetry or scores that already landed (that's `fp-cloud-cli`), or deciding what is worth evaluating (that's `failproofai-eval-brainstorm`).

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Amplicon sequence variant (ASV) inference from 16S rRNA or ITS amplicon sequencing using DADA2. Covers quality filtering, error learning, denoising, and chimera removal. Use when processing demultiplexed amplicon FASTQ files to generate an ASV table for downstream analysis.

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Use when the user asks to "audit our social presence" or "is this batch safe to publish"; runs either the typed ECHO asset gate or a separate program-maturity profile, with channel-truth, claim, disclosure, manipulation, UGC-rights, and denominator checks. Not for creator deliverables — use creator-content-auditor; not for launch readiness — use launch-readiness-auditor. 社媒资产门/运营成熟度/发布前放行

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FAIR-aligned exposure quantification: turns a pile of recon findings into a defensible 0-100 + A-F org risk score (Likelihood x Impact, three ownership-aware factors: exposure/threat/impact), an ownership + proof demotion cap so unproven or weakly-owned findings can't inflate the number, a $-denominated FAIR loss-magnitude estimate (IBM/Ponemon per-record cost bands, cross-source record dedup, threat-factor annualization), attack-path amplification (curated red-team chain catalog + generic graph-walk engine, with a kill-chain vs shared-fate honesty gate), and a board-ready one-pager deliverable (hero $ + letter grade + top-3 findings + top attack path + the ask). Extends osint-methodology's severity rubric and client deliverable templates with quantification. Passive analysis only -- operates on findings already collected, no target traffic, no API keys. Use when asked to score risk, quantify exposure, estimate breach cost, build a board report, translate technical findings to dollars, or explain why a grade or dollar figure came out the way it did.

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An overview of the core model API of SeisBench, a Python framework for training and applying machine learning algorithms to seismic data. It is useful for annotating waveforms using pretrained SOTA ML models, for tasks like phase picking, earthquake detection, waveform denoising and depth estimation. For any waveform, you can manipulate it into an obspy stream object and it will work seamlessly with seisbench models.

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Rank and prioritize neoantigen/epitope candidates by likely T-cell response using NeoFox feature annotation, PRIME2.0, BigMHC-IM, the Łuksza/Balachandran fitness model (agretopicity + foreignness), and pVACtools tiering. Encodes the field's hard truths that immunogenicity is the least-solved layer (dedicated scores ~AUROC 0.6-0.7, modest PPV), that scores are valid only for RANKING within one patient (never absolute go/no-go or cross-patient), that DAI has anchor-inflation and WT-denominator traps, and that stacking weak correlated scores into one number is a red flag. Use when ordering a candidate list for a vaccine. Binding lives in mhc-binding-prediction; calling in neoantigen-prediction.

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Assembles genomes de novo from noisy long reads (Oxford Nanopore R9/R10/Dorado, PacBio CLR) with Flye (repeat graph), Canu (correct-trim-assemble OLC), NextDenovo, Shasta, Raven, wtdbg2, or miniasm, and reconciles bacterial assemblies into a consensus with Trycycler/Autocycler. Covers matching the input flag to the basecaller era (--nano-hq vs --nano-raw), why a raw long-read assembly is contiguous but low-QV and not finished until polished, haplotig false-duplication and purge_dups, coverage and read-N50 as non-substitutable inputs, and mid-read adapter de-chimerization. Use when assembling a bacterial or eukaryotic genome from ONT or PacBio noisy reads, choosing a long-read assembler, or diagnosing an over-collapsed or duplicated assembly. For PacBio HiFi use hifi-assembly instead.

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Load and preprocess imaging mass cytometry (IMC) and MIBI data from raw MCD/TXT through hot-pixel removal, spillover compensation, and variance-stabilizing transformation, covering readimc/steinbock ingestion, NNLS spillover compensation (CATALYST), IMC-Denoise, and the IMC arcsinh-cofactor question. Use when starting analysis from raw MCD files, building per-channel TIFF stacks, compensating channel spillover, choosing an arcsinh cofactor, or preparing single-cell intensities for phenotyping.

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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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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.

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Call copy number variants with the GATK best-practices workflows — the somatic CNV pipeline (CollectReadCounts, DenoiseReadCounts with tangent normalization, ModelSegments, CallCopyRatioSegments) and the germline GATK-gCNV pipeline (DetermineGermlineContigPloidy, GermlineCNVCaller cohort/case mode, PostprocessGermlineCNVCalls). Covers panel-of-normals construction, AnnotateIntervals/FilterIntervals, allelic-count integration, and QS-based filtering. Use when integrating CNV calling into a GATK variant pipeline, calling rare germline CNVs from an exome cohort, deciding between the somatic and germline GATK workflows, or diagnosing why tangent normalization removed a real event or why gCNV output has low precision.

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Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.

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Map N6-methyladenosine (m6A) RNA modifications at single-nucleotide resolution using miCLIP (Linder 2015), miCLIP2 + m6Aboost machine learning (Kortel 2021), GLORI (Liu 2023, antibody-free chemical conversion), DART-seq (Meyer 2019, APOBEC1-YTH fusion), m6Anet (nanopore direct RNA), or MeRIP-seq with calibration. Use when distinguishing antibody-based from antibody-free m6A detection methods, applying the DRACH motif constraint, reconciling cross-method disagreements (DART 44% in DRACH vs GLORI), or detecting m6Am at the cap.

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