'bun' 검색 결과

Segundo cérebro persistente em Markdown — LLM Wiki no padrão Karpathy mantida como bundle Open Knowledge Format v0.2, com fontes imutáveis em raw/ e conceitos citados em wiki/. Use quando o usuário disser "segundo cérebro", "second brain", "adiciona isso ao meu brain", "salva na minha wiki", "ingere esse artigo/PDF/link", "o que minha wiki diz sobre", "o que meu brain sabe sobre", "consulta meu conhecimento", "lint da wiki", ou pedir para acumular conhecimento de fontes de forma durável. NÃO use para notas do Obsidian (pwdev-obsidian), planejamento de features (pwdev-feat) nem PRDs (pwdev-prd).

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Administração de Ubuntu, Debian e AlmaLinux — systemd, cron, SSH, Fail2Ban, supervisor, disco, memória, rede e processos. Use quando o usuário disser "servidor", "linux", "systemd", "serviço não sobe", "disco cheio", "load alto", "SSH", "cron não roda", "processo travado".

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React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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Performance regression detection using the browse daemon. Establishes baselines for page load times, Core Web Vitals, and resource sizes. Compares before/after on every PR. Tracks performance trends over time. Use when: "performance", "benchmark", "page speed", "lighthouse", "web vitals", "bundle size", "load time". (gstack) Voice triggers (speech-to-text aliases): "speed test", "check performance".

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React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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AEM as a Cloud Service Java/OSGi best practices, guardrails, and legacy-to-cloud pattern transformations. Use for Cloud Service–correct bundles, deprecated APIs, schedulers, ResourceChangeListener, replication, Replicator, JCR observation (javax.jcr.observation.EventListener), OSGi Event Admin (org.osgi.service.event.EventHandler), DAM AssetManager, BPA-style fixes, HTL (Sightly) Cloud SDK lint warnings (data-sly-test redundant constant value comparison), or any time you need the detailed pattern reference modules under this skill.

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[BETA] Expert assistance for the Adobe I/O CLI plugin `@adobe/aio-cli-plugin-aem-rde` — the `aio aem rde` / `aio aem:rde` command tree used to deploy, inspect, log-tail, snapshot, and troubleshoot AEM Rapid Development Environments (RDEs). Activate ONLY when the user explicitly references RDE concepts: 'AEM RDE', 'Rapid Development Environment', `aio aem rde`, `aio aem:rde`, `aem-rde`, RDE snapshots, RDE deploy/install, `rde install`, `rde inspect`, `rde status`, `rde history`, `rde reset`, the `@adobe/aio-cli-plugin-aem-rde` package, or Cloud Manager program/environment configuration that is specifically for an RDE environment. Do NOT activate on generic AEMaaCS phrases like 'deploy to AEM Cloud', 'push my bundle', 'tail the publish log', 'cloud sandbox', or unqualified 'dispatcher-config / frontend / env-config deployments' — those belong to Cloud Manager pipelines, not RDE. This skill is in beta. Verify all outputs before applying them to production projects.

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Use when finishing a feature, fixing a bug, before committing React code, or when the user types `/doctor`, asks to scan, triage, or clean up React diagnostics. Covers lint, accessibility, bundle size, architecture. Includes a regression check and a full local-triage workflow that fetches the canonical playbook.

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Use when finishing a feature, fixing a bug, before committing React code, or when the user asks to scan, triage, or clean up React diagnostics. Covers lint, accessibility, bundle size, architecture, regression checks, and local triage with React Doctor.

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React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

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Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, or build a new skill.

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Measure, profile, and fix performance bottlenecks — Core Web Vitals, backend latency, bundle size, and database queries. Load when performance requirements exist, users report slowness, profiling reveals bottlenecks, or the user asks to optimize load time, LCP, INP, CLS, or response time. Not for premature optimization without evidence. Pairs with browser-testing-with-devtools and ci-cd-and-automation for regression guards.

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Verify a code change end-to-end in the current worktree: identify what changed, run the relevant tests, get the app running, drive a browser over the affected areas, and report a verdict with screenshots. Generic across project types (Laravel/Herd, Node/bun, frontend, CLI, library). Use when the user wants to verify/QA/smoke-test a change, check that a PR or branch works, validate uncommitted work, or says "/flow", "verify this change", "does this PR work", "test and click through this". Optional target: PR number/URL, branch/ref, or worktree path; default is the current working changes.

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Angular performance optimization and best practices guide. Use when writing, reviewing, or refactoring Angular code for optimal performance, bundle size, and rendering efficiency.

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Predict peptide-MHC class I binding and natural presentation with MHCflurry, NetMHCpan-4.1, and MixMHCpred to nominate candidate CD8 T-cell epitopes. Covers the binding-affinity (BA) vs eluted-ligand (EL/presentation) distinction, why %Rank beats raw nM for cross-allele work, the MS abundance bias that misranks low-expression neoantigens, allele-coverage inequity, and length bias. Use when scanning a protein or peptide set for class I epitopes, scoring neoantigen candidates, or choosing a binding predictor. For CD4/HLA class II see mhc-class-ii-prediction.

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Analyze spatial cell-cell interactions, neighborhoods, and niches in IMC/MIBI data with squidpy and imcRtools, covering neighborhood-enrichment permutation nulls, the abundance-vs-density confound, inhomogeneous Ripley's K, cellular-neighborhood discovery, graph-construction (contact vs proximity), and edge effects. Use when testing whether cell types co-locate, choosing a spatial null, building a neighbor graph, discovering tissue niches, or deciding whether a spatial pattern is real or a density/segmentation artifact.

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Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimensionality optimization. Use when comparing diversity across sites with unequal sampling effort, picking the right richness estimator for singleton-heavy amplicon data, partitioning beta diversity into turnover vs nestedness, reporting Hill-number effective species counts rather than raw entropies, computing SES_MPD with explicit null-model justification, or deciding whether to apply standard metrics to compositional amplicon data. Not for clinical 16S microbiome diversity (see microbiome/diversity-analysis).

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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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Imports gene expression count matrices from featureCounts, HTSeq, STAR ReadsPerGene, Salmon/kallisto via tximport or tximeta, RSEM, 10X Genomics MTX/H5, AnnData H5AD, and RDS. Handles silent-miscounting traps (featureCounts -p v2.0.2 API break, STAR strandedness column choice, salmon NumReads-sum without tximport, RSEM non-integer expected_count, GENCODE _PAR_Y suffix, zero-length-transcript TPM divide-by-zero), and encodes the tximport countsFromAbundance decision tree with the "lengthScaledTPM is not TPM" warning. Use when assembling a gene-by-sample count matrix from aligner or quantifier output, importing salmon/kallisto for DESeq2 vs limma-voom, choosing strandedness column for STAR, debugging zero-count panics, or building tx2gene mapping.

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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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Visualize biological networks (PPI, gene-regulatory, co-expression, pathway) with layout algorithm choice (ForceAtlas2, Fruchterman-Reingold, Kamada-Kawai, hive plots), edge bundling, community-based coloring, and reproducible seeds using NetworkX, PyVis, igraph, and Cytoscape automation. Use when rendering biological networks for static publication, interactive HTML exploration, or Cytoscape-format export.

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Use this skill when the user asks about logging / log hauling operations — short-haul exemption specifics for logging, log truck securement, axle weights, state-by-state logging permits (especially OR, WA, ID, BC), bunk + stake configurations, and how logging fits into FMCSA + state rules. Reference 49 CFR 393 for cargo securement of logs.

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Use when a carrier asks whether to buy or lease their next tractor (or trailer), comparing full-service lease (Ryder, Penske, NationaLease), capital lease, finance lease, walk-away open-end lease, or cash purchase. Covers tax treatment, residual risk, maintenance bundling, balance-sheet impact, and cash-flow modeling.

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Use this skill for transporting logs. Covers § 393.116 specific rules, bunks, stakes, and tiedowns required.

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