「technical」の検索結果

Design author-friendly content structures that work for non-technical content creators. Use when designing content structures for blocks in AEM Edge Delivery Services.

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Expert code reviewer specializing in code quality, security vulnerabilities, and best practices across multiple languages. Masters static analysis, design patterns, and performance optimization with focus on maintainability and technical debt reduction.

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Protect your SaaS app from common vulnerabilities. Use when building auth, handling user data, or deploying features. Covers authentication, data protection, API security, and OWASP Top 10 for non-technical founders using AI tools.

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Bootstrap een klantproject voor 42-SEO-Skills. Vraagt naar profiel (content/ecom/technical/full), activeert alleen relevante skills via disabledSkills in .claude/settings.local.json, zet auth (.env) op, maakt de gedeelde Python venv in ~/.claude/venvs/42-seo/, en prikt 42-reports/<klant>/ neer. Use when user says "init SEO project", "42-init", "setup SEO", "nieuw klantproject", "initialiseer SEO".

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Refactor a whole documentation page in this repository without losing source-verified behavior facts. Use for page rewrites, splits, reorganizations, or substantial shortening of README sections, docs pages, or instruction files. Do not use for ordinary documentation authoring, targeted corrections, or documentation review; use technical-documentation instead.

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Perform a diff-scoped, behavior-neutral cleanup of AI-generated slop on the current branch before code review. Use for removing trivial comment, defensive-check, type-laundering, redundant-intermediate, compatibility-shim, or local style drift from the branch diff. Do not use for full correctness or safety review, documentation work handled by technical-documentation or docs-refactor, or repo-wide refactors.

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Philosophy-first writing partner for Medium with tactical depth. Combines human storytelling values with production-ready technical content tools. Six content modes based on staff pick analysis, mode-specific quality gates, evidence requirements, annotated examples, and comprehensive scripts integration. Designed for sustainable practice, not algorithm gaming.

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Calls significant loops from protein-directed and targeted 3C assays (HiChIP, PLAC-seq, Capture Hi-C/PCHi-C, ChIA-PET) where the contact background is peak-anchored and coverage-biased, so generic Hi-C loop callers (cooltools dots, Juicer HiCCUPS) use the wrong null. Covers FitHiChIP (config-driven coverage+distance-decay spline regression, peak-to-peak vs peak-to-all foreground, loose vs stringent background, coverage vs ICE bias), MAPS (positive Poisson regression on bias factors for PLAC-seq/HiChIP), hichipper (restriction-site-distance bias model + library QC), CHiCAGO (Delaporte two-component Brownian+technical background for asymmetric bait x other-end Capture Hi-C), the with/without separate-ChIP anchor decision, and differential loops via diffloop. Use when calling loops from HiChIP/PLAC-seq/Capture Hi-C, choosing FitHiChIP/MAPS/CHiCAGO, picking peak-to-all vs peak-to-peak, setting the loop FDR, supplying ChIP peaks as anchors, QCing a HiChIP library, or comparing loops between conditions.

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Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.

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Download raw sequencing reads from NCBI SRA using sra-tools (prefetch, fasterq-dump, vdb-validate) or the ENA mirror. Use when pulling FASTQ for SRR/ERR/DRR accessions, deciding between SRA-direct, ENA mirror, or AWS/GCP cloud mirror (STRIDES), handling --include-technical for 10x and other single-cell records, validating with MD5/vdb-validate, navigating SRR/SRX/SRS/SRP/PRJNA hierarchy, or finding accessions via pysradb. Encodes SRA cloud-egress economics, the fasterq-dump uncompressed-scratch trap, and the --max-size default that silently truncates large prefetches.

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Classify constitutional (germline) copy number variants for clinical reporting using the 2019 ACMG/ClinGen technical standards points-based framework, with ClassifyCNV and AnnotSV for semi-automated scoring. Covers the separate copy-number-loss and copy-number-gain rubrics, the five-tier classification, ClinGen haploinsufficiency/triplosensitivity and dosage-sensitive regions, de novo and segregation evidence, and population-frequency benign evidence. Use when assigning pathogenic/likely-pathogenic/VUS/likely-benign/benign to a constitutional CNV, scoring a CNV against ACMG/ClinGen criteria, or distinguishing the automatable evidence from the case-specific evidence requiring manual input.

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Identify differentially bound regions across CLIP-seq conditions (knockdown vs control, treatment vs vehicle, disease vs healthy) using DEWSeq (sliding-window DESeq2), Flipper (Skipper-downstream), ASpeak, edgeR, or limma-voom. Use when computing condition-level changes in RBP binding intensity, choosing peak-level vs window-level vs crosslink-level testing, designing replicate experiments, or distinguishing biological binding shifts from technical confounders.

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Provides a structured framework for conducting thorough code reviews covering correctness, security, architecture alignment, performance, and technical debt.

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Run the full feature workflow — task validation, technical design, implementation, testing, review, and documentation.

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Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.

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Pushes interfaces past conventional limits with technically ambitious implementations — shaders, spring physics, scroll-driven reveals, 60fps animations. Use when the user wants to wow, impress, go all-out, or make something that feels extraordinary.

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Writing style guide for technical content, social media, blog posts, READMEs, git commits, and developer documentation. Optimized to avoid AI detection patterns. Use when writing any content beyond code.

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Comprehensive patterns for creating, maintaining, and managing Architecture Decision Records (ADRs) that capture the context and rationale behind significant technical decisions.

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Access 20+ years of global financial data: equities, options, forex, crypto, commodities, economic indicators, and 50+ technical indicators.

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Interactive technical design quality review and validation

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Create comprehensive technical design for a specification

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Create or revise the SDD Composy technical specification (tasks/prd-SLUG/techspec.md) from the approved PRD, resolved stories, and mapped codebase, with DEC/RISK and TU/TI/E2E traceability and a human gate. Use when the product contracts are approved and architecture is needed — 'criar a spec técnica', 'techspec do prd-SLUG', 'design the solution for…'. Do NOT use to change product scope, before PRD and stories approval, or for design notes outside SDD.

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Create or revise the problem-first SDD Composy PRD (tasks/prd-SLUG/prd.md) with stable RF/CA identifiers, stopping at the human approval gate. Use when a user problem needs a product contract — 'criar o PRD', 'documento de requisitos', 'revisar o PRD do slug X', 'write the requirements for…'. Do NOT use for user stories (sdd-stories), technical design (sdd-techspec), or PRDs outside an initialized SDD workspace.

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Write and refine high-quality user stories — INVEST criteria, "As a X, I want Y, so that Z" format, Gherkin acceptance criteria when applicable, definition of ready, anti-patterns, and a review checklist. Use when creating or reviewing user stories, PRD section 6, backlog items, or acceptance criteria. Do NOT use for technical specs, architecture decisions, or task decomposition.

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。